TechWiki

DocWiki

While Also Discovering Hidden Publication and Collaboration Potentials

A few weeks back I completed a three-part introductory series to what Structured Dynamics calls a ‘total open solution‘. A total open solution as we defined it is comprised of software, structure, methods and documentation. When provided in toto, these components provide all of the necessary parts for an organization to adopt new open source solutions on its own (or with the choice of its own consultants and contractors). A total open solution fulfills SD’s mantra that, “We’re successful when we’re not needed.”

Two of the four legs to this total open solution are provided by documentation and methods. These two parts can be seen as a knowledge base that instructs users on how to select, install, maintain and manage the solution at hand.

Today, SD is releasing publicly for the first time two complementary knowledge bases for these purposes: TechWiki, which is the technical and software documentation complement, in this case based around SD’s Open Semantic Framework and its associated open source software projects; and DocWiki, the process methodology and project management complement that extends this basis, in this case based around the Citizen Dan local community open data appliance.

All of the software supporting these initiatives is open source. And, all of the content in the knowledge bases is freely available under a Creative Commons 3.0 license with attribution.

Mindset and Objectives

In setting out the design of these knowledge bases, our mindset was to enable single-point authoring of document content, while promoting easy collaboration and rollback of versions. Thus, the design objectives became:

• A full document management system
• Multiple author support
• Authors to document in a single, canonical form
• Collaboration support
• Mixing-and-matching of content from multiple pages and articles to re-purpose for different documents, and
• Excellent version/revision control.

Assuming these objectives could be met, we then had three other objectives on our wish list:

• Single source publishing: publish in multiple formats (HTML, PDF, doc, csv, RTF?)
• Separate theming of output products for different users, preferably using CSS, and
• Single-click export of the existing knowledge base, followed by easy user modification.

Our initial investigations looked at conventional content and document management systems, matched with version control systems or SVNs. Somewhat surprisingly, though, we found the Mediawiki platform to fulfill all of our objectives. Mediawiki, as detailed below, has evolved to become a very mature and capable documentation platform.

While most of us know Mediawiki as a kind of organic authoring and content platform — as it is used on Wikipedia and many other leading wikis — we also found it perfect for our specific knowledge base purposes. To our knowledge, no one has yet set up and deployed Mediawiki in the specific pre-packaged knowledge base manner as described herein.

TechWiki v DocWiki

TechWiki is a Mediawiki instance designed to support the collaborative creation of technical knowledge bases. The TechWiki design is specifically geared to produce high-quality, comprehensive technical documentation associated with the OpenStructs open source software. This knowledge base is meant to be the go-to source for any and all documentation for the codes, and includes information regarding:

• Coding and code development
• Systems configurations and architectures
• Installation
• Set-up and maintenance
• Best practices in these areas
• Technical background information, and
• Links to external resources.

As of today, TechWiki contains 187 articles under 56 categories, with a further 293 images. The knowledge base is growing daily.

DocWiki is a sibling Mediawiki instance that contains all TechWiki material, but has a broader purpose. Its role is to be a complete knowledge base for a given installation of an Open Semantic Framework (in the current case, Citizen Dan). As such, it needs to include much of the technical information in the TechWiki, but also extends that in the following areas:

• Relation and discussion of the approach viz. other information development initiatives
• Use of a common information management framework and vocabulary (MIKE2.0)
• A five-phased, incremental approach to deployment and use
• Specific tasks, activities and phases under which this deployment takes place, including staff roles, governance and outcome measurement
• Supporting background material useful for executive management and outside audiences.

The methodology portions of the DocWiki are drawn from the broader MIKE2.0 (Method for Integrated Knowledge Environments) approach. I have previously written about this open source methodology championed by Bearing Point and Deloitte.

As of today, DocWiki contains 357 articles and 394 structured tasks in 70 activity areas under 77 categories. Another 115 images support this content. This knowledge base, too, is growing daily.

Both of these knowledge bases are open source and may be exported and installed locally. Then, users may revise and modify and extend that pre-packaged information in any way they see fit.

Basic Wiki Overview

The basic design of these systems is geared to collaboration and embeds what we think are really responsive work flows. These extend from supporting initial idea noodling to full-blown public documentation. The inherent design of the system also supports single-source publishing and book or PDF creation from the material that is there. Here is the basic overview of the design:

(click for full size)

Mediawiki provides the standard authoring and collaboration environment. There are a choice of editing methods. As content is created, it is organized in a standard way and stored in the knowledge base. The Mediawiki API supports the export of information in either XHTML or XML, which in turn allows the information to be used in external apps (including other Mediawiki instances) or for various single-source publication purposes. The Collection extension is one means by which PDFs or even entire books (that is, multi-page documents with potentially chapters, etc.) may be created. Use of a well-designed CSS ensures that outputs can be readily styled and themed for different purposes or audiences.

As wikis designed from the get-go to be reusable, and then downloaded and installed locally, it is important that we maintain quality and consistency across content. (After download, users are free to do with it as they wish, but it is important the initial database be clean and coherent.) The overall interaction with the content thus occurs via one of three levels: 1) simple reading, which is publicly available without limitation to any visitor, including source inspection and export; 2) editing and authoring, which is limited to approved contributors; and 3) draft authoring and noodling, which is limited to the group in #2 but for which the in-progress content is not publicly viewable. Built-in access rights in the system enable these distinctions.

Features and Benefits

Besides meeting all of the objectives noted at the opening of this post, these wikis (knowledge bases) also have these specific features:

• Relatively complete (and growing) knowledge base content
• Book, PDF, or XHTML publishing
• Single-click exports and imports
• Easy branding and modification of the knowledge bases for local use (via the XML export files)
• Pre-designed, standard categorization systems for easy content migration
• Written guidance on use and best practices
• Ability to keep content in-development “hidden” from public viewing
• Controlled, assisted means for assigning categories to content
• Direct incorporation of external content
• Efficient multi-category search and filtering
• Choice of regular wikitext, WikED or rich-text editing
• Standard embeddable CSS objects
• Semantic and readily themed CSS for local use and for specialty publications
• Standard templates
• Sharable and editable images (SVG inclusion in process)
• Code highlighting capabilities (GeSHi, for TechWiki)
• Pre-designed systems for roles, tasks and activities (DocWiki)
• Semantic Mediawiki support and forms (DocWiki)
• Guided navigation and context (DocWiki).

Many of these features come from the standard extensions in the TechWiki/DocWiki packages.

The net benefits from this design are easily shared and modified knowledge bases that users and organizations may either contribute to for the broader benefit of the OpenStructs community, or download and install with simple modifications for local use and extension. There is actually no new software in this approach, just proper attention to packaging, design, standardization and workflow.

A Smooth Workflow

Via the sharing of extensions, categories and CSS, it is quite easy to have multiple instances or authoring environments in this design. For Structured Dynamics, that begins with our own internal wiki. Many notes are taken and collected there, some of a proprietary nature and the majority not intended or suitable for seeing public release.

Content that has developed to the point of release, however, can be simply tagged using conventions in the workflow. Then, with a single Export command, the relevant content is then sent to an XML file. (This document can itself be edited, such as for example changing all ‘TechWiki’ references to something like ‘My Content Site’; see further here.)

Depending on the nature of the content, this exported content may then be imported with a single Import command to either the TechWiki or DocWiki sites. (Note: Import does require admin rights.) A simple migration may also occur from the TechWiki to the DocWiki. Also, of course, initial authoring may begin at any of the sites, with collaborators an explicit feature of the TechWiki or DocWiki versions.

Any DocWiki can also be specifically configured for different domains and instance types. In terms of our current example, we are using Citizen Dan, but that could be any such Open Semantic Framework instance type:

(click for full size)

Under this design, then, the workflow suggests that technical content authoring and revision take place within the TechWiki, process and methodology revision in the DocWiki. Moreover, most DocWikis are likely to be installed locally, such that once installed, their own content would likely morph into local methods and steps.

So long as page titles are kept the same, newer information can be updated on any target wiki at any time. Prior versions are kept in the version history and can be reinstated. Alternatively, if local content is clearly diverging yet updates of initial source material is still desired, the local content need only be saved under a new title to preserve it from import overwrites.

Where Is It Going from Here?

We are really excited by this design and have already seen benefits in our own internal work and documentation. We see, for example, easier management of documentation and content, permanent (canonical) URLs for specific content items, and greater consistency and common language across all projects and documentation. Also, when all documentation is consolidated into one point with a coherent organizational and category structure, documentation gaps and inconsistencies also become apparent and can readily be fixed.

Now, with the release of these systems to the OpenStructs (Open Semantic Framework) and Citizen Dan communities, we hope to see broader contributions and expansion of the content. We encourage you to check on these two sites periodically to see how the content volume continues to grow! And, we welcome all project contributors to join in and help expand these knowledge bases!

We think this general design and approach — especially in relation to a total open solution mindset — has much to recommend it for other open source projects. We think these systems, now that we have designed and worked out the workflows, are amazingly simple to set up and maintain. We welcome other projects to adopt this approach for their own. Let us know if we can be of assistance, and we welcome ideas for improvement!

Cisco Video is a Good Starting Intro for Management

Like the seminal linked data publication by PricewaterhouseCoopers of about a year ago (see “PWC Dedicates Quarterly Technology Forecast to Linked Data“, May 29, 2009), a video released by Cisco yesterday is another signal of the emergence of the semantic enterprise.

The Cisco tech brief on The Semantic Enterprise is a quite accessible — but a bit eerie — seven-minute introduction.  The video was prepared by Cisco’s Internet Business Solutions Group (IBSG), with Shaun Kirby, its Director of Innovations Architectures, as the narrator:

Well, as for being eerie, when the video first came up, I thought I was looking at an advanced, next generation avatar, perhaps a reincarnation of Douglas Adams’ Hyperland. Maybe this semantic stuff was closer at hand than we thought!

But, as it turned out, that first blush was only a reaction to how the video was shot. As it gets rolling, the Cisco video is extremely well done and informative. It is a great intro for sharing with management when contemplating your own moves to becoming a semantic enterprise.

I suggest you first view — and then bookmark — this one.

Using Incremental, Low-risk Semantic and Open World Approaches

OK. So, you’re looking at your garage … or your bedroom closet … or your office and its files. They are a mess, and you can’t find anything and you can’t stuff anything more into the nooks, cubbies, crannies or cabinets. What do you do?

Well, when you finally get fed up and have a rainy day or some other excuse, you tackle the mess. Maybe you grab a big mug of coffee to prepare for the pending battle. Maybe you strip down to comfort clothes. Then, if you’re like me, you begin to organize stuff into piles. Labeled piles and throwaway piles and any other piles that can provide a means to start bringing order to the chaos.

In the semantic Web world, there is a phrase coined by Jim Hendler that captures this approach: A little semantics goes a long way [1]. A little semantics, just like your labeled piles, helps to bring order to information chaos.

Mind you, this is not fancy or expensive stuff. In the case of my office, it is colored sheets of paper labeled with Magic Markers as “Taxes” or “Internal” or “Blog Posts” or whatever. Then, I begin sifting and distributing. In the case of the semantic world, these are classifying things into like categories and simply relating them to other categories with simple relationships, such as “is Part Of” or “is Narrower Than”.

Of course, I could have approached my mess in a different way. I could have hired an efficiency expert to come in, interview me and all of my employees and colleagues, gotten a written analysis and report, and then committed to a multi-week project to completely store and place every single last piece of paper in my office or organize every rake and set of abandoned golf clubs in my garage. When done, I would have shelled out much money and I suspect still not have been able to find anything.

Sort of sounds like the traditional way IT does its business, doesn’t it? To clean up their information messes, enterprises need to find a better strategy.

I’m not too long from having returned from the SemTech conference, which overall was quite an excellent show. But despite its emphasis on semantic technologies and their usefulness to businesses and enterprises, I found one critical theme unspoken: the ability of semantic approaches to change how enterprise IT actually does business. New ways have got to be found to clean up the many and growing information piles emerging all around us.

The Changing Nature of IT

IT is — and has been — going through a fundamental set of changes for decades. In the last decade, these changes have led to lowered relative spending, a shift in spending priorities toward services, less innovation, and less productivity. Some data and observations by researchers and analysts document these trends.

The following chart, using US Bureau of Economic Analysis data [2], shows the clear 50-year trend in declining hardware costs for enterprises, mostly resulting from the observation known as Moore’s Law. These massive hardware cost reductions (logarithmic scale) have also resulted in lower prices for IT as a whole. In 2008, for example, total relative IT prices were about two-thirds what they were a mere decade earlier:

Source: M.K. Bergman and Bureau of Economic Analysis [2] (click for full size)

In contrast, relative prices for software and services have remained remarkably flat over this entire period, including for the past decade. This is somewhat surprising given the emergence of packaged software and more recently open source. However, relative percentage expenditures for custom software and software developed in-house have also remained strong over the past decade [3].

The mid- to late-1990s represented the high-water mark on many bases for enterprise IT, expenditures and vendors. Roughly in 1997 or so, the number of public enterprise software vendors peaked as did venture funding [4] and relative expenditures for IT in relation to GDP. There was a major uptick in relation to preparing for Y2K and a major downtick due to the dot-com bubble, and then of course the past two years or so have seen a global economic downturn. But, as the figure below shows (red), the long-term trend tends to suggest a relative plateau for IT expenditures in relation to GDP somewhat around 2000:

Source: M.K. Bergman and Bureau of Economic Analysis [2] (click for full size)

Yet, like the first chart, software seems to be bucking this trend (blue lines above). Though perhaps the rate of growth in expenditures for software is slowing a bit, it is still on a growth upslope, especially in relation to overall IT expenditures. The next chart, in fact, specifically compares software expenditures to total IT expenditures. Software expenditures are some 40% higher in relation to total IT than they were a mere decade ago:

Source: M.K. Bergman and Bureau of Economic Analysis [2] (click for full size)

The mix of these software expenditures is also changing in major ways while stagnating in others.

The changing aspect is coming about from the shift of expenditures from license and maintenance fees to services. A number of software vendors began to see revenues from services overcome that from licensing in the 1990s. By the early 2000s, this was true for the enterprise software sector as a whole [4]. Today, service revenues account for 70% or so of aggregate sector revenues. Combined with the emergence of open source and other alternatives such as software as a service (SaaS), I think it fair to say that the era of proprietary software with exceedingly high margins from monopoly rents is over [5].

The stagnating aspect occurs in how the software expenditures are applied. According to Gartner, in the US, more than 70% of IT expenditures are devoted to simply running existing systems, with only about 11% of budgets devoted to innovation; other parts of the world spend nearly double on innovation and much lower for operations [6]. This relative lack of support for innovation and high percentages for running existing systems has held true for about a decade. Meanwhile, IT’s contribution to US productivity has been declining since 2001 [7].

What is the Cause for IT’s Ills?

Last year, PricewaterhouseCoopers published a major report with the provocative title, “Why Isn’t IT Spending Creating More Value?” [7]. The 42-page report covered many of the aspects above. Among other factors, the PWC authors speculated that:

I suppose one could add to this litany other factors such as the growth and emergence of the Internet, sector consolidations through mergers and acquisitions, the rise of open source and alternatives such as SaaS, etc.

But which of these are causes? Which are symptoms? And which might only be consequences or coincident?

To be sure, all recognize the explosion of digital data and information, with sources and formats springing up faster than Whack-a-Mole. It is such an evident and ubiquitous phenomenon that pointing to it as a cause appears on the face of it quite obvious. Also obvious is that these new sources carry with them a diversity of systems and tools. While not categorically stated as such, it appears that PWC fingers the difficulties of “cobbling” these systems together as the root cause for low productivity and thus the IT cost crisis.

I agree totally that these are symptoms of what we see in IT’s current circumstance. I would even say these factors are a proximate cause to these ills. But I disagree they are the root cause. To discover that root, I believe, we must look deeper to mindset and assumptions.

Closed World Mindset as the Root Cause

There are some phenomena that are so obvious that they are easily missed. Not seeing your fingertip six inches between your eyes is one of these. We aren’t used to focusing on things so near at hand.

So, let’s look for a moment at the closed world assumption (CWA), a key underpinning to most standard relational data systems and enterprise schema and logics. CWA is the logic assumption that what is not currently known to be true, is false. If CWA is not directly familiar to you that is understandable; it is an implied assumption of these systems and logics. As such, it is not often inspected directly and therefore not often questioned [8].

With regard to standard IT systems, the closed world assumption has two important aspects:

1. The assumption is that the information domain at hand is complete [9], and
2. The related negation as failure, which assumes every predicate to be false that cannot be proved to be true.

On the face of them, these assumptions seem tame enough. And, indeed, there are some enterprise data systems that absolutely rely on them for efficient processing and completion times, such as most transaction systems. CWA is absolutely the appropriate design for such applications.

However, for knowledge management or representation applications — that is, applications which involve combining or using heterogeneous data or information from multiple data sources, which are exactly the same sources requiring information “cobbling” noted above by PWC — there are two very critical implications of the closed-world assumption (CWA):

1. Efforts or projects can not be undertaken incrementally; if done in pieces, each piece must be complete and consistent, which is expensive to scope and do
2. To be consistent and explicit, the predicates (properties or relationships) must also be complex to model the “reality” of the system, which is also expensive to scope and do [10].

The net effect, which I have argued before, most notably in a major piece about the open world assumption [11], is that typical projects with a knowledge management aspect have become costly, take very long to complete, often fail, and require much planning and coordination. These facts have been true for three decades as enterprises have attempted to extract knowledge from their electronic information using closed world approaches based on relational systems. And, as recognized by PWC, these problems are only getting worse with growth in diversity and scope of systems.

The implications of closed world v. open world approaches are absolutely at the root of the causes leading to declining productivity, low innovation, significant failures and increasing costs — all exacerbated with more data and more systems — now characterizing traditional enterprise IT. Moreover, it is not a problem for open world systems to link to and incorporate closed world approaches. With open world, there is no need for Hobson’s choices. Unfortunately, such is not true when one begins with a closed world premise.

Incremental is Good: Pay as You Go

As best as I can tell, Alon Halevy was the first to use the phrase “pay as you go” in 2006 to describe the incremental aspect of the open world approach in relation to the semantic Web [12]. The “pay as you go” phrase had been applied earlier to data management and storage and had also been used to describe phone calling plans.

Incremental concepts and “agility” have been popular topics for the past five to ten years in IT, most often related to software development. And, while “incremental” sounds good in relation to enterprise projects, especially of a knowledge management or information integration/federation nature, the actual methodologies put forward were anything but incremental in their conceptual underpinnings.

Unfortunately, the “pay as you go” phrase has (and still is) largely confined to incremental, open world approaches involving the semantic Web. How this approach might apply and benefit enterprises has yet to be articulated. Nonetheless, I like the phrase, and I think it evokes the right mindset. In fact, I think with linked data and many other aspects of the current semantic Web we are seeing such approaches come to fruition. Inch-by-inch, brick-by-brick, data on the Web is getting exposed and interlinked. “Pay as you go” is incremental, and that is good.

Purposeful is Better: Pay as You Benefit

Yet the idea of “pay as you benefit” is more purposeful, able to be planned and implemented, and founded on standard enterprise cost-benefit principles. I think it is a better (and more nuanced) expression of the “pay as you go” mindset in an enterprise setting. What it means is you can start small and be incomplete. You can target any domain or department or scope that is most useful and illustrative for your organization. You can deploy your first stand-ups as proofs-of-concept or sandboxes. And, you can build on each prior step with each subsequent one.

One of the reasons we (Structured Dynamics) embraced the MIKE2.0 methodology [13] was its inherent incremental character. (Government deployments often call them “spirals”.) In general, the five phases of MIKE2.0 can be represented as follows:

(click for full size)

It is specifically during the fifth phase, testing and improvement, that quantitative and qualitative benefits from the current increment are calculated and documented. This evolving methodology is where the enterprise can assess the results of its prior investment and scope and budget for the next one. These can be quick, rapid increments, or more involved ones, depending on the schedule, prior results and risk profile of the enterprise (or department) at that time.

Much is made of “incremental” or “agile” deployments within enterprises, but the nature of the traditional data system (and its closed world assumption) can act to undermine these laudable steps. The inherent nature of an open world approach, matched with methodologies and best practices, can work wonderfully with KM-related projects.

Quite Simply a Different Way to Do Business

We see in our current IT circumstances a number of embedded practices and assumptions. We have been assuming control and completeness — the closed world opposite to the open world approach. We have thus embraced and promoted “global” or enterprise-wide solutions: be they desktop operating systems or browsers or expensive enterprise-level proprietary software solutions. This scope leads to immense hurdle rates and risks: we better get our choices right up front, because if we don’t, the department or enterprise are at risk. We have an inward focus about our own resources, our own networks, our own systems. Meanwhile, when we look outward, we wonder how all of these new Web companies can grow and expand so rapidly in comparison to us.

Clearly, we are seeing shifts to more services than products, more open source, more outsourcing, and more software as a service. Yet, because of the legacy of decades-long commitments from prior IT investment and the failures of many hyped “solutions” such as ERP or BI or data warehousing or a dozen others, we also see a decline and a reluctance for IT to embrace new and transforming approaches. Our prior choices were practically tantamount to “betting the enterprise.” What if our new approaches fail as so many of their predecessors did? In a demanding, competitive environment can we afford to make such wrong choices again with such immense implications?

Yet, now that information technology is a given, it only seems natural that its role becomes an integral part of the enterprise, and not a special function. Like procurement, IT has matured to become a support function. Businesses should not succeed or fail based on the types of pencils and paper stock they use; so should they not depend on the software support choices that IT makes. Enterprises are now past the need to get “computerized”; they are thoroughly so. But our understanding of IT’s role and position has not evolved with its own success.

The first whiffs of these challenges to IT’s initial hegemony came from the departmental introduction of PCs and local networks in the early 1980s. It has continued with desktop software, spreadsheets and Web portals and sites. Large, mature companies awoke in horror in the last decade to discover they had hundreds — sometimes thousands — of Web sites and content dissemination points over which IT had little or no control. Such is the nature of entropy, and it is a fact for any organization of any size.

So, now, with strategies such as “pay as you benefit,” there is no longer an excuse not to innovate. There is not a justification to put off testing and discovering benefits that the open world and semantic approaches can bring to your organization. There is now a basis to make the case and set the affordable budgets within desirable timelines for becoming a semantic enterprise.

Mindsets and expectations do require some adjustment. For example, not everything will be known or modeled in early phases. But, is that also not true in any “real” real world? We’re not talking high-throughput transaction systems here, but beginning to pull together and link the information that is important to your organization strategically.

Remember the intro statement that “a little semantics goes a long way”? Well, that truth — and it is true — when combined with incremental deployment firmly tied to demonstrable results, promises quite simply a different way to do business. Never before have enterprises had working and winnable approaches such as this to test and innovate and learn and discover. Jump on in; the water is clear and warm.

And, oh, as to that mess in your closet or garage? Well, if you adhere to CWA, you will need to define a place for everything to go before you can start cleaning things up. I say: forget those false hurdles. If you’d really want to make a dent in the mess, grab a broom and start cleaning.

Release of Semantic Components Adds Final Layer, Leads to Streamlined Sites

Yesterday Fred Giasson announced the release of code associated with Structured Dynamics‘ open source semantics components (also called sComponents).  A semantic component is an ontology-driven component, or widget, based on Flex. Such a component takes record descriptions, ontologies and target attributes/types as inputs and then outputs some (possibly interactive) visualizations of the records.

Though not all layers are by any means complete, from an architectural standpoint the release of these semantic components provides the last and missing layer to complete our open semantic framework. Completing this layer now also enables Structured Dynamics to rationalize its open source Web sites and various groups and mailing lists associated with them.

The OSF “Semantic Muffin”

We first announced the open semantic framework — or OSF — a couple of weeks back. Refer to that original post for more description of the general design [1]. However, we can show this framework with the semantic components layer as illustrated by what some have called the “semantic muffin”:

(click for full size)

The OSF stack consists of these layers, moving from existing assets upward through increasing semantics and usability:

• Existing assets — any and all existing information and data assets, ranging from unstructured to structured. Preserving and leveraging those assets is a key premise
• scones / irON — this layer is for general conversion of non-RDF data and data schema to RDF (via irON or RDFizers) or for information extraction of subject concepts or named entities (scones)
• structWSF — is the pivotal Web services framework layer, and provides the standard, common interface by which existing information assets get represented and presented to the outside world and to other layers in the OSF stack
• Semantic components — the highlighted layer in the “semantic muffin”; in essence, this is the visualization and data interaction layer in the OSF stack; see more below
• Ontologies — are the layer containing the structured assets “driving” the system; this includes the concepts and relationships of the domain at hand, and administrative ontologies that guide how the user interfaces or widgets in the system should behave, and
• conStruct — is the content management system (CMS) layer based on Drupal and the thinnest layer with respect to OSF; this optional layer provides the theming, user rights and permissions, or other functionality drawn from Drupal’s 6500 third-party modules.

Not all of these layers are required in a given deployment and their adoption need not be sequential or absolutely depend on prior layers. Nonetheless, they do layer and interact with one another in the general manner shown.

The Semantics Components Layer

Current semantic components, or widgets, include: filter; tabular templates (similar to infoboxes); maps; bar, pie or linear charts; relationship (concept) browser; story and text annotator and viewer; workbench for creating structured views; and dashboard for presenting pre-defined views and component arrangements. These are generic tools that respond to the structures and data fed to them, adaptable to any domain without modification.

Though Fred’s post goes into more detail — with subsequent posts to get into the technical nuances of the semantic components — the main idea of these components is shown by the diagram below.

These various semantic components get embedded in a layout canvas for the Web page. By interacting with the various components, new queries are generated (most often as SPARQL queries) to the various structWSF Web services endpoints. The result of these requests is to generate a structured results set, which includes various types and attributes.

An internal ontology that embodies the desired behavior and display options (SCO, the Semantic Component Ontology) is matched with these types and attributes to generate the formal instructions to the semantic components. These instructions are presented via the sControl component, that determines which widgets (individual components, with multiples possible depending on the inputs) need to be invoked and displayed on the layout canvas. Here is a picture of the general workflow:

(click for full size)

New interactions with the resulting displays and components cause the iteration path to be generated anew, again starting a new cycle of queries and results sets. As these pathways and associated display components get created, they can be named and made persistent for later re-use or within dashboard invocations.

Consolidating and Rationalizing Web Sites and Mailing Lists

As the release of the semantic components drew near, it was apparent that releases of previous layers had led to some fragmentation of Web sites and mailing lists. The umbrella nature of the open semantic framework enabled us to consolidate and rationalize these resources.

Our first change was to consolidate all OSF-related material under the existing OpenStructs.org Web site. It already contained the links and background material to structWSF and irON. To that, we added the conStruct and OSF material as well. This consolidation also allowed us to retire the previous conStruct Web site as well, which now re-directs to OpenStructs.

We also had fragmentation in user groups and mailing lists. Besides shared materials, these had many shared members. The Google groups for irON, structWSF and conStruct were thus archived and re-directed to the new Open Semantic Framework Google group and mailing list. Personal notices of the change and invites have been issued to all members of the earlier groups. For those interested in development work and interchange with other developers on any of these OSF layers, please now direct your membership and attention to the OSF group.

There has also been a revigoration of the developers’ community Web site at http://community.openstructs.org/. It remains the location for all central developer resources, including bug and issue tracking and links to SVNs.

Actual code SVN repositories are unchanged. These code repositories may be found at:

• structWSF
• conStruct
• Semantic Components
• irON Parsers.

We hope you find these consolidations helpful. And, of course, we welcome new participants and contributors!

As of July 1, Daily Readership Passed 3000

As of yesterday, the readership on this AI3 blog passed 3000 daily for the first time. It has been steadily inching upward, and finally passed that minor milestone. Thank you!

I’ve been writing this blog for five years now, with some 400 total posts, or about 1.5 blog posts per week. I know my style is toward longer articles and less frequent posting, most often of a fairly detailed or technical nature. And, while I have a Twitter account, I do not bleat. My style is for more meaty discussions. Perhaps it belies my age.

The real growth in this blog, however, has come about with my conscious attempt to write for the enterprise audience. RDF, the semantic enterprise, linked data and ontologies need a bridge from the technical community to the one of practitioners. Much progress and uptake has been occurring with these business and government audiences.

At the recent SemTech meeting, I was taken aside by many individuals noting my blog posts and thanking me for the thought and effort behind them. Thank you for noticing, and reading, and you are welcome. We need more translation of semantic topics and technologies to pragmatic terms.

If you have been following the standard W3C and SemWeb mailing lists recently, you will have noticed an anxiety and a continuation of the fractious nature of this “community”. In part this comes about because there are efforts afoot to revisit the RDF specs. But, mostly, I think, it is the ongoing nature of many in this group to snatch defeat from the jaws of victory. The search by some for perfection and insistence on parochial needs and preferences can give a pettiness to this “community” that is unbecoming.

Many of us have abandoned those forums for those reasons. As for myself, I will continue to evangelize to the buying market and keep the gaze pointing outward. There is a wealth of need for tools, techniques, methods, documentation, structures, and narratives. Thanks to all of you, the readership of this blog, for continuing to affirm this value.

So, in the great scheme of things, the readership of this blog is quite small in comparison with the big boys. On the other hand, very few individuals have higher numbers, and all of this for a fairly esoteric area. I think this proves there is a market and a need out there for semantic solutions.

Thanks again! And, for those in the United States, have a most enjoyable 4th of July holiday!

Structured Dynamics Completes Design Phase; Citizen Dan First Exemplar

Structured Dynamics has been in a fervent — and, we believe, fruitful — design phase for the past 18 months. All of the working parts related to how to embrace becoming a semantic enterprise have now been defined and designed. Actual tools and components accompany many of these parts and have been deployed.

Recently, I have been speaking and blogging much about rationale, process, mindset and approach for how to bring semantics into the organization. But, prior to now, we have not spoken much about the overall design behind our approach. Today, as we complete our design phase and introduce our first exemplar instance of it — Citizen Dan [1] — we are finally in a position to describe this overall approach.

We term our approach the open semantic framework, also OSF. The open semantic framework is a combination of a layered architecture and modular software. The open semantic framework represents the software component of the four-component total open solution, recently described in a three part series. I return to this topic in the conclusion of this post.

Revisiting Design Objectives

Over the past nine months, I have been focusing my writing largely on the semantic enterprise, with more specificity regarding our Open SEAS (Semantic Enterprise Adoption and Solutions) initiative. In bits and pieces, these writings have tended to reflect a number of objectives:

• Leverage existing information assets (data + structure) as much as possible
• Develop incrementally, and validate and justify as you go
• Emphasize, where possible, open standards and open software
• Employ Web-oriented architectures
• Adopt an open-world approach that acknowledges that information is most often incomplete; the approach is a key enabler for incremental deployments
• Use URIs as object identifiers, and use linked data where practical
• Embrace any data format found in the wild, but use RDF as the ultimate integration data model
• Design architectures and APIs that avoid “lock-in” and support multiple tools options across the stack
• Provide systems and capabilities that put all information sources — text, media, semi-structured and conventional databases — on an equal footing
• Promote designs that bring the ability to create useful results into the hands of users and decisionmakers; relegate IT to a support role.

To date, the result of these design objectives is perhaps best captured in my Seven Pillars of the Open Semantic Enterprise posting, as well as our general discussions regarding adaptive ontologies. Yet, still, these writings have been somewhat piecemeal. What this document attempts to do is to place all of these perspectives into a single, coherent whole.

The Incremental Layers of the Open Semantic Framework

Structured Dynamics has been a strong advocate for layered architectures, with clear APIs between layers as appropriate. But these layers are not “laminates” that completely cover the layer below, nor are they all needed or necessary. Depending on the circumstance, some layers are unneeded or superfluous. Layers may be added or not incrementally.

In this manner, then, the open semantic framework is perhaps more akin to a pearl, than to a laminate or cocoon. Each subsequent layer does not “embed” the layer prior to it, and some layers actually may inter-operate with multiple layers below or above it (this is notably true for the “ontologies” layer, which has interactions up and down the stack).

Nonetheless, we can envision this pearl of the open semantic framework and its layers as follows:

(click for full size)

Others have termed this the “semantic muffin” or even “semantic muppet” or “semantic blob”. Whatever (hehe). The real idea is that layers may accrete (as in the growth of a pearl) and occur over time and be uneven. Each layer, though, does have a role to play (though it may not be needed in a given deployment), and does act to augment existing information assets in the transition to a semantic framework. Beginning at the core, each of these layers — with external references as appropriate for more details — is described below.

Existing Assets Layer

The open semantic framework is premised on leveraging existing information assets. Sure, once the framework is in place, new information can be brought into it in a more direct, semantic manner. But, the real thrust and benefit of this framework is to provide an incremental pathway for finally inter-operating and federating prior decades of data, structure and information assets.

These information assets may reside inside or outside the enterprise. They may (and DO!) exist in many formats and are described by many schema. They may come from internal transaction systems or warehouses, or may exist external on the Web or at supplier or partner sites. These information assets may span from conventional databases and relational data systems to XML interchange standards, Web pages and standard internal text or documents. In short, there is NO information asset that is not amenable to be included in this framework.

The Information Transformation (scones/irON) Layer

The information transformation layer provides either: 1) extraction of concepts and entities as structured metadata from source text or documents; or 2) conversion of existing data assets to interoperable form. As implemented by Structured Dynamics, the extractions are conducted by either scones (Subject Concept or Named EntitieS) or third-party utilities, and the conversions occur via irON (instance record Object Notation) or third-party “RDFizers“.

Depending on the source, the net result of the transformation is to produce interoperable data and information that can be ingested and used by other layers in the framework.

Though not strictly analogous, this layer bears some resemblance to the ETL (extract, transfer, load) utilities used in many enterprise information integration applications. Unlike those conventional systems, this information transformation layer also may capture and represent some of the source schema.

In all cases, however, these transformations are relatively simple and get parsed against the available structure (the ontologies, schema and entity reference lists) in the system to generate the semantic metadata (tags).

At this point, the extracted structure is generally at the level of instance records, or the ABox, with simple assertions of attribute-value pairs for specific records [2]. Little schema transformation or mapping occurs at this layer (if such is needed, that occurs at the structWSF layer; see next). Actual federation or interoperation occurs at later layers based on the TBox structures [2].

This modular portion of the framework is explicitly designed with APIs to allow third-party tools to be plugged in and substituted.

The structWSF Layer

The major workhorse of the open semantic framework is the structWSF (Web services framework) layer. structWSF is the most complicated of the OSF layers and has many supporting software packages and capabilities. The structWSF layer provides the standard, common interface (”canonical”) layer by which existing information assets get represented and presented to the outside world and to other layers in the OSF stack.

structWSF is a platform-independent Web services framework for accessing and exposing structured RDF data. Its central organizing perspective is that of the dataset. These datasets contain instance records, with the structural relationships amongst the data and their attributes and concepts defined via ontologies (schema with accompanying vocabularies; see below).

The structWSF middleware framework is generally RESTful in design and is based on HTTP and Web protocols and open standards. The current structWSF framework comes packaged with a baseline set of about twenty Web services in CRUD, browse, search and export and import. All Web services are exposed via APIs and SPARQL endpoints. Each request to an individual Web service returns an HTTP status and optionally a document of resultsets. Each results document can be serialized in many ways, and may be expressed as either RDF or pure XML. An internal representation, structXML [3], is used for internal communications across all structWSF Web services and with other layers.

structWSF has a central service that governs access rights and permissions. These rights occur at the level of the dataset, which gives immense flexibility to how data may be accessed, read, modified, created or deleted (or not). Datasets within a given structWSF instance may be accessed directly via API or via SPARQL queries to the instance’s endpoint. Depending on rights and query, results sets may be returned from a given structWSF instance in an infinite variety of ways.

This latter capability is the essential interface for subsequent layers in the open semantic framework stack. Depending on those subsequent components, pre-staged data and results sets may be returned for an essentially limitless variety of purposes.

Each structWSF instance also has a unique Web address that enables one or a multitude of instances to communicate and share with one another. This simple, but elegant, method enables structWSF instances to participate or not in potentially global or restricted local networks and collaboration environments. This is currently the largest untapped potential of structWSF with respect to its existing deployments.

The Semantic Components Layer

The newest layer in the stack is the semantic components layer. This layer takes results sets — most often generated by a specific query or data slice request — from one or more structWSF instances and then presents that information via a variety of data visualization or data presentation widgets (what we specifically call ‘semantic components‘ due to their design [4]). The operation and sensitivity of these display components are themselves driven by a presentation and data analysis (including statistics) ontology.

Current display widgets include: filter; tabular templates (similar to infoboxes); maps; bar, pie or linear charts; relationship (concept) browser; story and text annotator and viewer; workbench for creating structured views; and dashboard for presenting pre-defined views and component arrangements. These are generic tools that respond to the structures and data fed to them, adaptable without modification to any domain.

As presently implemented by Structured Dynamics, this layer consists either of Flex data visualization components or structured data display templates based on Smarty. The inherent design allows for updates to other bases (such as HTML5). The layer may also be swapped out or substituted with third-party capabilities.

The strength and power of this system is governed by its own ontology, the Semantic Component Ontology (SCO) (see next).

This is an extremely flexible layer in the open semantic framework stack. Expect an ongoing series of explanatory blog posts and online resources in the upcoming weeks to explain this innovative capability.

The Ontologies Layer

The ontologies layer actually refers to all structured assets driving the system. As such, this layer might be considered the “brain” (though rather simply specified!) of the open semantic framework.

At a true schema or TBox level [2], the ontologies layer represents the concept and relationships of the domain at hand. This layer also hosts the specific local entities and prominent things (people, places, events, etc.) useful for extracting local and domain-specific relevance. However, those views are also supplemented with some administrative ontologies (two examples are SCO and irON) that guide how the user interfaces or widgets in the system should behave.

The concept level represents the “world view” of the specific instantiation of the open semantic framework at hand. This conceptual (TBox) view provides the structural organization of information, inferencing capabilities, and navigation, faceting and explorer structure. The entity (ABox) view provides tagging for prominent individuals and instances important to the domain at hand, and guides the structure behind data visualizations of attribute or indicator data.

The administrative level uses simple roles and relationships for attributes and indicators to inform the framework as to how and with what widget to display information. For example, a “type” of information that is geographically related can be instructed to use the map component as an option for display. Whether some information is used for totals, comparison purposes, or other specifications useful to data visualization and graphing may also be specified.

The language and relationships (predicates or properties) of these administrative ontologies are simple and straightforward. It is, for example, relatively easy to define data display functions at the broad dataset and attributes level. Simple determinations drive how results sets and their associated results types may be displayed, no matter what datasets or slices may be generated as a result of the queries or requests fed to the system.

The structure in these layers can be replaced by other structures for other instantiations and circumstances. Indeed, all other layers in the open semantic framework can remain relatively fixed while tailoring the instance to new domains solely via this layer. The ontologies layer is what gives any given instantiation of OSF — such as Citizen Dan — its unique focus and scope.

The Content Management System (conStruct) Layer

The thinnest layer (that is, least substantial with respect to this framework) is the content management system (CMS) layer. In its current form, the open semantic framework uses the Drupal CMS via our conStruct plug-in modules. The design of the framework, however, has explicitly accommodated the possibility that other CMSs may substitute for this role.

The CMS layer is optional if structWSF endpoints are sufficient or if simple Web pages hosting semantic components are deemed as adequate. Very small organizations or deployments may reasonably choose to have no CMS layer at all.

However, for most sites or portals with more than a few active users, it is desirable to have broad flexibility in theming (”skinning”), user rights and permissions, or other functionality. These are the roles of the CMS layer. Drupal, for example, is presently supported by more than 4500 third-party modules in every conceivable function, from polling to blogs and rating systems and bulletin boards.

For such generalized portals or collaboration environments, it makes sense to adopt and install a flexible CMS system, such as Drupal. Much of the user experience and functional environment can be provided through such means.

The open semantic framework is thus designed to reside easily in a CMS while also providing the hooks to take advantage of the generalized user rights and functionality of the CMS. In this manner, the open semantic framework is able to stay focused on its structured data and interoperability purposes, while still gaining the advantages of rich-featured content management systems.

The OSF is a Web-oriented Architecture

With its inherent open-world orientation [5] and distributed and collaborative potential, the open semantic framework was designed from the outset to be Web-capable and Web-oriented:

(click for full size)

A Web-oriented architecture (WOA) has a number of understood requirements, to which the open semantic framework adheres. Specifically, these design considerations support the framework as being part of WOA:

• Data and objects are all identified with Web addresses (URIs)
• Data is generally exposed (and universally available) as linked data
• SPARQL endpoints and APIs are generally RESTful in design
• The overall architecture is modular, with inherent decentralized and distributed aspects
• All display and visualization aspects are cross-browser ready and capable.

OSF is the Basis for Domain-specific Instantiations

Citizen Dan is our first exemplar instance of this open semantic framework. The details page for the project goes into some of Citizen Dan’s functionality and capabilities.

Citizen Dan is specifically geared to local governments and localities, with an emphasis on community indicator systems (CIS). CIS have become a popular way of measuring and tracking measures of local economic and social well-being; they are closely related to sustainability and how to measure it as used in many economic and environmental domains.

However, in the context of this post, what is really interesting about Citizen Dan is that its semantic framework is a completely open and generic one. The same set of tools and capabilities described on its details page can be applied to any domain that needs to manage and understand information in its own domain. This includes from unstructured text or documents to conventional structured databases.

What changes from domain to domain are the data structures (the ontologies, schema and entity reference lists; see above) that are fed to this open semantic framework. By swapping out new structures, what can be called Citizen Dan in one instance can morph to become Curriculum Carla in say, the education instance or Doctor Doolittle in the veterinary science instance [6].

We can illustrate these multiple instances as follows:

(click for full size)

What this figure illustrates is that even a branded expression of the framework — such as Citizen Dan — is merely an instance of that framework. And, actually, when expressed in such a packaged manner, we can more accurately call the standard and bundled suite of generic functions and accompanying structure of Citizen Dan as an instantiation of the open semantic framework:

By replacing the structure bases, and by tailoring the function suite appropriate to a given market and use, we can create many instantiations of the open semantic framework for different domains and markets. In this manner, Citizen Dan can be seen as an early exemplar of the framework, but not as a definer and limiter to it.

OSF is the Software Leg to a ‘Total Open Solution‘

So far, this discussion has focused solely on considerations of software and architecture. While we see the power of the open semantic framework, highly useful in itself, this is inadequate alone to achieve acceptance and success in the enterprise (as we noted in our most recent posts). The very forces that are compelling enterprises to look at new options, are also the same ones that pose difficult hurdle rates for acceptance of open source.

To address this issue, we have developed a four-legged foundation to what we termed the total open solution. The solution involves software, structure, documentation and methods (or best practices). Each of these connect and relate to the other foundations.

The open semantic framework is clearly the software (and architecture) leg to this foundation. Again, however, what is interesting is that the mere swapping out of the structure can also make the system relatively ready for other domains.

We see these relationships in the following diagram, that also shows that the DocWiki portions of the solution embody the documentation (aside from code-level comments) and methods legs of the foundation:

(click for full size)

Differences between domains may also lead to differences as to which components are included or not in that domain’s desired instantiation.

The hugely important implied point, however, from the diagram above, is to show how nearly universal the content and methods in the DocWiki may be to other domains. Because the deltas between domains largely result from structure and what specific functional components are included or not, it becomes clear that most documentation and practices shared with the DocWiki will be applicable across domains. Sure, the use cases and some of the specific terminology may change, but we can also now see a high degree of re-usability of documentation and knowledge base across markets. This realization makes the usefulness and leverage of the DocWiki even higher.

A Common Language and Framework for Moving Forward

Developing “common language” by which to describe and convey things — especially new things like semantics that also have strong technical aspects — is tough, very tough. We are only now beginning on this process; we look to many in the community and elsewhere to help define informative and evocative terminology.

Per the original design objectives above, Structured Dynamics has approached the challenge of the semantic enterprise in what we think is both a pragmatic and a new way. The insistence on preserving and respecting existing information assets, matched with the opportunities and different mindsets arising from an open-world approach [5], have necessitated thinking through new designs and developing new concepts. Any time such new thinking and concepts occurs, new language and new metaphors must accompany it.

While certainly there are components and various software packages that populate and comprise an open semantic framework, the framework is also just as importantly a world view or way to think about information, information development, and its architecture. For example, a pivotal concept is that an open semantic framework is built around generic tools responsive to the information structures fed to them. This realization shifts the locus of emphasis from software development per se to creating, managing and adapting data and information structures. While this democratizes the information development process and is more inclusive of all knowledge workers, it also imposes needs for new toolsets and business processes. We are only at the nascent stages of understanding and learning about these differences.

Similarly, a development approach that is inherently incremental and leverages (rather than replaces or displaces) existing information assets means IT projects need to be considered in a new light. Small projects with more emphasis on tangible and demonstrable benefits will alter budgets, lower risks, and place a need for quicker turnaround. Like the architecture of the open semantic framework itself, projects based on OSF are also more distributed, decentralized and modular.

With such decentralization also comes the need for mechanisms and systems to overcome vendor “lock-in” and proprietary systems. A key thrust in support of what we have called the total open solution and its mixture of documentation and methods to accompany software and structure is specifically targeted at this issue. Tools and means for collaboration and concurrent contributions are another possible answer. Prior software practices in agile development and version control will see extensions to all manner of information development across the enterprise.

We are proud of our design work and proof-testing with clients over the past 18 months. We believe the open semantic framework and its implications to be a fundamental shift in how organizations need to think about their information development, existing information assets, and IT budgets and processes. We know widescale adoption is not yet at hand — enterprises are justifiably conservative when it comes to new thinking. But, given global competition and tight pocketbooks, the open semantic framework is a formulation to which enterprises and governments should pay very close attention.

How Shall We Measure Progress Over the Past Three Years?

For a dozen years, my career has been centered on Internet search, dynamic content and the deep Web. For the past few years, I have been somewhat obsessed by two topics.

The first topic, a conviction really, is that implicit structure needs to be extracted from Web content to enable it to be disambiguated, organized, shared and re-purposed. The second topic, more an open question as a former academic married to a professor, is what might replace editorial selections and peer review to establish the authoritativeness of content. These topics naturally steer one to the semantic Web.

A Millennial Perspective

The semantic Web, by whatever name it comes to be called, is an inevitability. History tells us that as information content grows, so do the mechanisms for organizing and managing it. Over human history, innovations such as writing systems, alphabetization, pagination, tables of contents, indexes, concordances, reference look-ups, classification systems, tables, figures, and statistics have emerged in parallel with content growth [19].

When the Lycos search engine, one of the first profitable Internet ventures, was publicly released in 1994, it indexed a mere 54,000 pages [1]. When Google wowed us with its page-ranking algorithm in 1998, it soon replaced my then favorite search engine, AltaVista. Now, tens of billions of indexed documents later, I often find Google’s results to be overwhelming dross — unfortunately true again for all of the major search engines. Faceted browsing, vertical search, and Web 2.0’s tagging and folksonomies demonstrate humanity’s natural penchant to fight this entropy, efforts that will next continue with the semantic Web and then mechanisms unforeseen to manage the chaos of burgeoning content.

An awful lot of hot air has been expelled over the false dichotomy of whether the semantic Web will fail or is on the verge of nirvana. Arguments extend from the epistemological versus ontological (classically defined) to Web 3.0 versus SemWeb or Web services (WS*) versus REST (Representational State Transfer). My RSS feed reader points to at least one such dust up every week.

Some set the difficulties of resolving semantic heterogeneities as absolutes, leading to an illogical and false rejection of semantic Web objectives. In contrast, some advocates set equally divisive arguments for semantic Web purity by insisting on formal ontologies and descriptive logics. Meanwhile, studied leaks about “stealth” semantic Web ventures mean you should grab your wallet while simultaneously shaking your head.

A Decades-Long Perspective

My mental image of the semantic Web is a road from here to some achievable destination — say, Detroit. Parts of the road are well paved; indeed, portions are already superhighways with controlled on-ramps and off-ramps. Other portions are two lanes, some with way too many traffic lights and some with dangerous intersections. A few small portions remain unpaved gravel and rough going.

Wreck in Nebraska during the 1919 Transcontinental Motor Convoy

A lack of perspective makes things appear either too close or too far away. The automobile isn’t yet a century old as a mass-produced item. It wasn’t until 1919 that the US Army Transcontinental Motor Convoy made the first automobile trip across the United States.

The 3,200 mile route roughly followed today’s Lincoln Highway, US 30, from Washington, D.C. to San Francisco. The convoy took 62 days and 250 recorded accidents to complete the trip (see figure), half on dirt roads at an average speed of 6 miles per hour. A tank officer on that trip later observed Germany’s autobahns during World War II. When he subsequently became President Dwight D. Eisenhower, he proposed and then signed the Interstate Highway Act.

That was 50 years ago. Today, the US is crisscrossed with 50,000 miles of interstates, which have completely remade the nation’s economy and culture [2].

Today’s Perspective

Like the interstate system in its early years, today’s semantic Web lets you link together a complete trip, but the going isn’t as smooth or as fast as it could be. Nevertheless, making the trip is doable and keeps improving day by day, month by month.

My view of what’s required to smooth the road begins with extracting structure and meaningful information according to understandable schema from mostly uncharacterized content. Then we store the now-structured content as RDF triples that can be further managed and manipulated at scale. By necessity, the journey embraces tools and requirements that, individually, might not constitute semantic Web technology as some strictly define it. These tools and requirements are nonetheless integral to reaching the destination. We are well into that journey’s first leg, what I and others are calling the structured Web.

For the past six months or so I have been researching and assembling as many semantic Web and related tools as I can find [3]. That Sweet Tools listing now exceeds 500 tools [4] (with its presentation using the nifty lightweight Exhibit publication system from MIT’s Simile program [5]). I’ve come to understand the importance of many ancillary tool sets to the entire semantic Web highway, such as natural language processing and information extraction. I’ve also found new categories of pragmatic tools that embody semantic Web and data mediation processes but don’t label themselves as such.

In its entirety, the Sweet Tools listing provides a pretty good picture of the semantic Web’s state. It’s a surprisingly robust picture — though with some notable potholes — and includes impressive open source options in all categories. Content publishing, indexing, and retrieval at massive scales are largely solved problems. We also have the infrastructure, languages, and (yes!) standards for tying this content together meaningfully at the data and object levels.

I also think a degree of consensus has emerged on RDF as the canonical data model for semantic information. RDF triple stores are rapidly improving toward industrial strength, and RESTful designs enable massive scalability, as terabyte- and petabyte-scale full-text indexes prove.

Powerful and flexible middleware options, such as those from OpenLink [6], can transform and integrate diverse file formats with a variety of back ends. The World Wide Web Consortium’s GRDDL standard [7] and related tools, plus various “RDF-izers” from Massachusetts Institute of Technology and elsewhere [8], largely provide the conversion infrastructure for getting Web data into that canonical RDF form. Sure, some of these converters are still research-grade, but getting them to operational capabilities at scale now appears trivial.

Things start getting shakier when trying to structure information into a semantic formalism. Controlled vocabularies and ontologies range broadly and remain a contentious area. Publishers and authors perhaps have too many choices: from straight Atom or RSS feeds and feeds with tags to informal folksonomies and then Outline Processor Markup Language [9] or microformats [10]. From there, the formalism increases further to include the standard RDF ontologies such as SIOC (Semantically-Interlinked Online Communities), SKOS (Simple Knowledge Organizing System), DOAP (Description of a Project), and FOAF (Friend of a Friend) [11] and the still greater formalism of OWL’s various dialects [12].

Arguing which of these is the theoretical best method is doomed to failure, except possibly in a bounded enterprise environment. We live in the real world, where multiple options will always have their advocates and their applications.

All of us should welcome whatever structure we can add to our information base, no matter where it comes from or how it’s done. The sooner we can embrace content in any of these formats and convert it into canonical RDF form, we can then move on to needed developments in semantic mediation, some of the roughest road on the journey.

Potholes on the Semantic Highway

Semantic mediation requires appropriate structured content. Many potholes on the road to the semantic Web exist because the content lacks structured markup; others arise because existing structure requires transformation. We need improved ways to address both problems. We also need more intuitive means for applying schema to structure. Some have referred to these issues as “who pays the tax.”

Recent experience with social software and collaboration proves that a portion of the Internet user community is willing to tag and characterize content. Furthermore, we can readily leverage that resulting structure, and free riders are welcomed. The real pothole is the lack of easy — even fun — data extractors and “structurizers.” But we’re tantalizingly close.

Tools such as Solvent and Sifter from MIT’s Simile program [13] and Marmite from Carnegie Mellon University [14] are showing the way to match DOM (document object model) inspectors with automated structure extractors. DBpedia, the alpha version of Freebase, and System One now provide large-scale, open Web data sets in RDF [15], including all of Wikipedia. Browser extensions such as Zotero [16] are showing how to integrate structure management into acceptable user interfaces, as are services such as Zoominfo [17]. Yet we still lack easy means to design the differing structures suitable for a plenitude of destinations.

Amazingly, a compelling road map for how all these pieces could truly fit together is also incomplete. How do we actually get from here to Detroit? Within specific components, architectural understandings are sometimes OK (although documentation is usually awful for open source projects, as most of the current tools are). Until our community better documents that vision, attracting new contributors will be needlessly slower, thus delaying the benefits of network effects.

So, let’s create a road map and get on with paving the gaps and filling the potholes. It’s not a matter of standards or technology — we have those in abundance. Let’s stop the silly squabbles and commit to the journey in earnest. The structured Web’s ability to reach Hyperland [18], Douglas Adam’s prescient 1990 forecast of the semantic Web, now looks to be no further away than Detroit.

This Friday brown bag leftover was first placed into the AI3 refrigerator about three years ago on May 3, 2007.  The piece was my answer to a request by Jim Hendler to pen some thoughts on the semantic Web, based on I believe what he thought might be a pragmatic perspective combining Internet business with Web science. The formal piece appeared as a guest editorial in the May/June 2007 issue of IEEE Intelligent Systems. What appears above is unaltered from my original posting (aside from some minor formatting clean-up and — sorry to say — some of the projects are now defunct).

Introducing the Open Source ‘DocWiki’ System

In the first part to this series, we began with the argument that open source software alone was not sufficient to meet the required acceptance factors in the enterprise. As a guiding way to create the right mindset around these issues we shared the saying that we have adopted at Structured Dynamics that, “We’re successful when we are not needed.”

In the second part of this series we described the four legs of a stable, open source solution. These four legs are software, structure, methods and documentation. When all four are provided, we termed this a total open solution.

Now, in this third and concluding part to our series, we introduce the open source documentation and methodology system called ‘DocWiki’. It complements the base open source software, in the process completing the conditions for a total open solution.

Though we call this system ‘DocWiki’, it is not meant to be a brand or particular product description for what Structured Dynamics is offering. Rather, ‘DocWiki’ is merely a placeholder name for a generic, open source system and knowledge base that can be downloaded, installed, branded, modified and extended in whatever way the user sees fit. ‘DocWiki’ is a baseline documentation and methodology “starter kit” that can be dressed up in new clothes or packaged and named in whatever manner best suited to a given deployment.

In describing the major components of this ‘DocWiki’ system we will again use our Citizen Dan initiative [1] as we did in Part 2. This gives us a real use case, though the same approach is applicable to any open source information management initiative by enterprises.

We call the specific version of the ‘DocWiki’ used in the case of Citizen Dan the ‘CIS DocWiki‘ (for community indicator systems), specific to the domain and local government focus of Citizen Dan. Similarly, the structured vocabulary and ontology that guides the system is the MUNI ontology. For other information development initiatives, the specific content of these components would be swapped out for ones appropriate to that initiative.

Overview of the ‘DocWiki’ System

A number of desires and objectives intersected to guide the design of the ‘DocWiki’ system. We wanted:

• A consolidated knowledge base with complete, turnkey implementation content
• A collaborative document authoring system with authoring tools comfortable to most knowledge workers
• A version control system to enable rollbacks and restoration of prior official versions
• A system that would enable and facilitate the collection and import of relevant content; in our own case, that included widely distributed internal content in many forms and locations plus relevant external content (such as defined items in Wikipedia)
• A document management framework that would allow existing content to be mixed, combined and re-purposed for different uses, from training to marketing collateral
• A single source publishing system that would allow content to be published as paper documents, PDFs, Web pages and the like
• A system that could be easily themed, skinned and branded, tailored for any given deployment or circumstance, and
• A system built entirely from open source components and with content that had no restrictions on use or re-use.

In first formulating this design, our assumption was the major building blocks would be an open source document management system linked with some form of version control. Though we think such a formulation could work OK, our exposure to the MIKE2.0 methodology actually caused us to re-look at and re-think a wiki-based approach. Ultimately the trump card that decided the design for us was familiarity and ease-of-use.

The resulting architecture of the full ‘DocWiki’ system is shown below:

(click for full size)

What is cool about this design is that a single software download install with a few extensions (Mediawiki, the Wikipedia software, plus some standard extensions and judicious use of Semantic Mediawiki) and a single loadable database are all that is required to transfer and install the ‘DocWiki’ system.

To better describe this system, we will focus on three major interconnecting pieces in this architectural diagram: the knowledge base; the vocabulary and structure (ontology); and the authoring and publishing system (wiki).

The ‘DocWiki’ Knowledge Base

The pre-loaded content for the ‘DocWiki’ system comes from its knowledge base. This is provided as a text-exported MySQL database that can be modified en masse before loading (such as substituting ‘YourName’ for ‘DocWiki’). The exemplar upon which this knowledge base is modeled is the MIKE2.0 framework.

MIKE2.0 (Method for an Integrated Knowledge Environment ) provides a comprehensive methodology that can be applied across a number of different projects within the information management space. MIKE2.0 provides an organized way to describe the why, when, how and who of information management projects. Via standard templates and structures, MIKE2.0 provides a consistent basis to describe and manage these projects, and in a way that helps promote their interoperability and consistency across the enterprise.

MIKE2.0 has a generalized methodology and set of templates applicable to initiatives, the phases, activities and tasks to undertake them, and supporting assets. Supporting assets can range from glossaries and definition of terms and concepts to very specific technical documents or background material. The entire system is logical and applies a consistent design and organizational structure and categories.

For our purposes, we wanted a complete, turnkey content knowledge base. This meant that we needed to accommodate all forms of project management and guidance, ranging from specific “how-to” and technical discussions to the entire suite of background and supporting material. The scope of this knowledge content is defined as what a new person assigned a lead or implementation responsibility would need to read or master.

As a destination site MIKE2.0 is quite broad: it embraces the ability to model virtually any information management initiative. This makes MIKE2.0 an invaluable source of structure and methodology guidance, but also results in it being quite limited in the specific how-tos associated with any given initiative. I have earlier spoken about the structure of MIKE2.0 and in particular its applicability to the semantic enterprise.

The strength of MIKE2.0, however, is that its structure can be grabbed and quickly applied to form an organizational and structural basis for filling out the knowledge base for any specific information development initiative. And, that is exactly what we did with the ‘CIS DocWiki.’

MIKE2.0 hosts and maintains its project-related structure in Mediawiki (with some extensions). Combined with its templates, this provides a rapid-start baseline for beginning to tailor and flesh out the specific details for a given information management initiative. Thus, after copying broad aspects of the MIKE2.0 system into the incipient ‘DocWiki’, it was relatively straightforward to let the existing structure and templates of MIKE2.0 guide next steps.

As of today’s date, the ‘CIS DocWiki’ contains about 300 substantive articles, a complete activity and tasking structure, and various re-usable templates based on Semantic Mediawiki for structured and consistent access and retrieval. New tasks and structure can be readily added to the system. Existing structure or content can be deleted or marked as archive for non-display. We are still gathering all requisite content pieces, and anticipate by first public release that the baseline knowledge base will include 2x to 3x the scale of its current content.

For new ‘CIS DocWiki’ (or Citizen Dan-based) deployments, this means the knowledge base can be completely modified and extended for local circumstances. The set-up of the Mediawiki instance is separate from the loading or modification of the knowledge base, which means the look-and-feel of the entire system, not to mention user rights and permissions, can also be readily tailored for local requirements.

The core content of the ‘CIS DocWiki’ and its basis in a set structure and methodology (derived from MIKE2.0) means that the knowledge base is also adaptable for other broader information development areas, especially in the semantic enterprise or semantic government arenas. Thus, while Structured Dynamics is first releasing the ‘CIS DocWiki’ in the context of Citizen Dan and semantic government, we also are developing a parallel instance for the Open SEAS approach to the semantic enterprise.

The approach taken here is somewhat different than the standard wiki use. As experts, we are basically sole authoring (with contributions from selected collaborators and our clients) the starting basis of the knowledge base. Unlike many wikis, this enables us to be quite consistent in content, style, and organization. Such an approach allows us to present a coherent and complete starting content and methodology foundation. However, once delivered and installed for a given deployment, its users are then free to extend and change this knowledge foundation in the standard wiki manner. Whether those subsequent extensions are free-form or more tightly controlled and managed is the choice of the new deployment’s administrators.

The Supporting MUNI Structure

Strictly speaking, the vocabularies and structures (including, of course, ontologies) that drive our semantic government or semantic enterprise offerings are also part of the knowledge base.  And, in fact, many of these aspects, especially related to the actual operating of the instances, are included as part of the standard knowledge base.

However, the applicable domain ontology itself is separately maintained. Descriptions of how to use and modify such ontologies are part of the general ‘DocWiki’ knowledge base, but the ontology is not. This arm’s length-separation is done to acknowledge that the ontology has independent use and value apart from the knowledge base or the software (Citizen Dan, in this case) that is the focus of it.

In the Citizen Dan instance, this structure is the MUNI ontology. MUNI is a general local government domain ontology that can find use in a broad array of circumstances, using or not Citizen Dan. Thus, like other ontologies developed and maintained by Structured Dynamics, such as BIBO (the Bibliographic Ontology), the ontology itself and its documentation, discussion forums and use cases are maintained separately.

The first release of MUNI is still under development and will be released this summer.

The Wiki/Publication Portion of ‘DocWiki’

The software framework that hosts and manages all of this content is the Mediawiki software, originally developed for Wikipedia. This framework is supported by a number of standard extensions packaged with the ‘DocWiki’ distribution. One of the more notable extensions is Semantic Mediawiki. Mediawiki also is the wiki framework underlying MIKE2.0, so content sharing between the systems is straightforward.

The Collaborative Wiki Portion

The first use of the ‘DocWiki’ is to add new content to the knowledge base and to modify or extend what is provided in the baseline. For straight authoring, ‘DocWiki’ offers the standard wikitext basis for content entry and editing, as well as the WikED enhanced editor and the FCKEditor WYSIWYG rich-text editor. Each of these may be turned on or off at will.

All of the baseline content is fully organized and categorized via a standard structure. Pre-existing templates aid in entering new content in specific areas consistently or in providing standard administrative ways of tagging content for completeness or need for editorial attention. Tasks and concepts, in particular, follow set ways of entry and description. These set templates, some forms-based and some derived from Semantic Mediawiki, are also tied into automatic internal scripts for listing and organizing various items. So long as new material is entered properly, it will be reflected in various stats and listings. Unlike sole reliance on Semantic Mediawiki, the ‘DocWiki’ approach is a mix of standard wiki categories and semantic types. Both are used for effective organization of the knowledge base.

Besides the knowledge base of domain content and “how-to”, the system also comes pre-packaged with many wiki “how-to” and best practices guidance for using the system effectively and consistently. Of course, a given deployment may or may not enforce all of these practices. A poorly administered instance, for example, could degenerate fairly quickly and lose the native structure and organization of the baseline system.

As with standard wikis, there is a history of prior page revisions that gives the system rollback and version control. Mediawiki has a pretty good user access and permissions framework ranging from access, reading, editing and to uploads.

Besides the standard and required extensions, ‘DocWiki’ also comes packaged with the necessary settings and configuration files to operate “out-of-the-box” in its designed baseline mode. Of course, these settings, too, can be changed and modified by site administrators, and ‘DocWiki’ also includes guidance on how to do that.

The Publication Portion

A little known but highly useful part of the Mediawiki API allows direct export of XHTML content [2]. Then, with minor XSLT conversion templates, it is possible to strip out wiki-specific conventions (such as the editing of individual sections) or to create straight XML versions. When this is combined with the use of internal ‘DocWiki’ CSS style sheets that impose some clean and semantic style identifiers, a common canonical output basis for content is possible.

From that point, a given deployment may use its own CSS styles to theme output content. Output Web pages (XHTML) or XML files then can be processed using existing and accurate utilities to produce PDF or *.doc documents. Then, with systems such as OpenOffice, an even wider variety of document formats can be produced. These facilities mean that the ‘DocWiki’ can also act as a single-source publishing environment.

In its initial release, re-purposing ‘DocWiki’ content into other presentations (for example, combining sections from multiple pages into a new document as opposed to re-using existing pages as is) will require creating new wiki pages and then cutting-and-pasting the desired content. However, it should also be noted that both DocBook and DITA have been applied to Mediawiki installations [3]. It should be possible to enable a more flexible re-purposing framework for ‘DocWiki’ moving into the future.

When Available

The ‘CIS DocWiki’ is meant to accompany the first release of Citizen Dan, likely by the end of summer. The MUNI ontology will also be released roughly at the same time. At release, the ‘CIS DocWiki’ is anticipated to have on the order of 500-800 baseline content and “how to” articles.

Depending on time availability and other commitments, Structured Dynamics will also be using this information to build a semantic government composite offering to MIKE2.0. We will be contributing this new offering for free, similar to what we have done earlier for a semantic enterprise offering.

Subsequent to those events, we will then be modifying the ‘CIS DocWiki’ for the semantic enterprise domain. Much of the necessary content will have already been assembled for the ‘CIS DocWiki’.

Conclusions and Applicability

Paradoxically, while developing such knowledge bases and systems such as ‘DocWiki’ appears to be extra work, from our standpoint as developers it is useful and efficient. Structured Dynamics already researches and assembles much material and tries to “document as it goes.” Having the ‘DocWiki’ framework not only provides a consistent and coherent way to organize that information, but it also helps to point out essential gaps in our offerings.

The ‘DocWiki’ delivers the methods, documentation and portions of the structure to a total open solution. The ‘DocWiki’ is the primary means — along with software development and accompanying code-level and API documentation, of course — for us to fulfill our mantra that “We’re successful when we are not needed.” As we pointed out in Part 1 of this series, we really think such an attitude is ultimately a self-interested one. The better we can address the acceptance factors in the enterprise for our offerings, the more opportunities we will gain.

We would like to think that other enlightened open source software developers, especially those in the semantic space but certainly not limited to them, will see the wisdom of this four-legged foundation to total open solutions. Up until now, pragmatic guidance for what it takes to create a complete open source offering to businesses and enterprises has been lacking.

The tools, methods, and workflows all exist for making total open solutions real today. All of the pieces are themselves open source. There are many useful guides for best practices across the pipeline. It is just that — prior to this — no one apparently took the time to assemble and articulate them. We think this three-part series and some of the “how to” guidance in the ‘DocWiki’ system can help fix this oversight.

Ultimately, with wider adoption by developers, goaded in part by demands of the marketplace for them, we would hope that additional innovations and ideas may be forthcoming to improve the industry’s ability to offer total open source solutions. Adding just a small bit of attentive effort to how we organize and package what we know is but a small price to pay for greater acceptance and success.