Over the last few months you’ve probably seen a number of announcements about how OpenCalais has been chosen by one organization or another to support its business.

In a number of recent meetings I’ve been asked the (very fair) question, Why OpenCalais and not one of the other entity extraction services out there?

Given that the question seems to be coming up more often as the number of extraction services increases, I thought I’d get my best understanding of why many major players we’ve announced (and an equal number we haven’t) have chosen to go with OpenCalais. And – at the end – I’ll mention a few reasons why others haven’t chosen OpenCalais.

So, in no particular order, why do organizations choose Calais?

Thomson Reuters

OpenCalais is provided by Thomson Reuters – the largest professional information organization in the world.

If you’re interested in kicking around some semantic technologies in your spare time this doesn’t really matter. If you’re incorporating those technologies deep within your business – or, as is true with many users – actually building a new business on top of them, this becomes pretty important. Basically – you need to know that the service is going to be there for you.

Facts & Events

With the increase in structured content assets like Wikipedia / DBpedia, it’s become pretty easy to knock out a basic entity extraction tool. And – while we like entity extraction as much as anyone else – it’s really just the tiniest starting point in what you can and will need to do.

OpenCalais extracts a wide range of facts and events from unstructured content and lets you know what’s happening in your content – not just tags for things.

• Facts are things like “John Doe is CEO of XYZ Corporation.”
• Events are things like “XYZ Corporation today announced that it would acquire ACME Corporation.”

OpenCalais is the only service that does this in a production-strength manner.

Reliability

OpenCalais stays up. It’s hosted in mirrored data centers thousands of miles apart from each another. It’s monitored 7*24. It basically doesn’t go down – even during system upgrades and maintenance. We stopped adding 9s after we got beyond 99.99% uptime.

Accuracy

We’ve been building the tools underneath OpenCalais for over a decade. They’ve been used by hundreds of organizations and many many thousands of end users. One of the things we’ve learned is that accuracy matters. While no NLP system is perfect, we’re convinced ours is the best and we have some ideas in the pipeline to increase accuracy even more.

Integration

We basically focus on providing great semantic plumbing. But we know that not everyone wants to be a plumber. We’ve worked to integrate (or motivate others to integrate) OpenCalais with a wide range of tools including Drupal, WordPress, WordPress Multiuser, Oracle, Lucene, Coldfusion, Flash, Firefox, Prolog, Lisp, Django, Java, PHP, Python, Alfresco, Perl, .NET, Ruby, TopBraid and a few others.

From content management systems to language-specific libraries – there are lots of ways to get started quickly.

Linked Data

We’re serious about Linked Data. We’re also worried about the proliferation of incorrect links and the effects of link rot. So, rather than just pointing to Linked Data assets out on the cloud and risking that they’ll go stale, we host our own Linked Data cloud, which is kept up to date with both Thomson Reuters contributed content as well as regularly validated links to other sources such as DBpedia, Freebase and others.

SocialTags

Pure semantic extraction is great – but sometimes you need more. If you’re writing about Porsches and Ferraris you’d probably like to have categorization concepts like “sports cars” and “automobiles” returned to you with your semantic metadata. OpenCalais does this via our ever-improving SocialTags concept tagging capability. It’s good now, and it’s going to get a lot better soon.

Focus

OpenCalais is here to provide great semantic plumbing. We’re not trying to sell ads. We’re not trying to provide the prettiest decorations for blogs. We build the plumbing – you architect the solutions.

Now, in a spirit of transparency, here’s why some people don’t choose OpenCalais:

Languages

We’re great in English and okay in French and Spanish (we extract entities but neither facts nor events in these two languages). We intend to implement more languages in the future – but for the time being we’re concentrating our efforts on improved functionality and accuracy in English.

Complexity

OpenCalais isn’t a simple tagging tool. What it returns to the calling application is a reasonably complex RDF construct. It takes a little time to get up to speed on RDF and how to use it in your applications. We think it’s worth it because it’s the most flexible and powerful format we know of.

Performance in Knowledge Domain ‘x’

Where ‘x’ is fashion or square dancing or rugby. OpenCalais is optimized for performance in the general world of business – that’s where we excel.

We have extended OpenCalais to take steps in other areas (such as sports, media, etc.) – but if you need deep semantic extraction capabilities related to protein binding – there are better places to look.

• Search Results Page | PlanetGapYear.com –
• AmeriCorps NCCC Student Blogs About Gap Year Volunteer Experience | PlanetGapYear.com –
• My AmeriCorps – Home Page –
• Who offers placements? | Volunteer | Lonely Planet –
• Volunteer abroad, TEFL Courses and more from i-to-i –
• Volunteer | Volunteer | Lonely Planet –
• EmpDesign | RSS 001 –
• Nodalities » Blog Archive » Linked Data In(ter)action –
• tague.otherinbox.com –
• Breaking News and Opinion on The Huffington Post –
• The New, Interactive Singles Map. Or, Looking for Love in All the Statistically Wrong Places. – Great visualization of gender ratios / age group / geography
• More Roster Cuts | Twins Nation –
• PublicProfiler » examples –
• Write to Reply –

• Calais Viewer –
• Topic Hubs | drupal.org –
• OpenPublish – Calais Powered Drupal Publishing Platform – Drupal distribution incorporating Calais modules, theming and integration – semantically enabled publishing right out of the box.
• Media Cloud: Foggy Payoff : Beyond Search –
• OpenCalaisLDA « context:forge –
• everyday icons – Patterns –
• Semantic Library » Linked Data and libraries – http://bit.ly/kX0yw
• Please ignore this post « context:forge –
• IPDealSource –
• Luxury narrow boats on UK canals from Valley Cruises –

The quick background. Drupal is a hot Open Source content management and web site deployment platform. It has probably tens of thousands of users and thousands of internal and external deployments. Suffice it to say it’s the hot thing in Open Source CMS platforms right now.

Drupal let’s you build a site fairly quickly. It won’t be pretty and it won’t have much functionality – but it can be up and running in a matter of minutes.  Then you can spend the next few days, weeks or months giving it a nice look and feel, finding the extensions for the functionality you need and perhaps building some glue to hook it all together. Weeks or months later you’ll have the basics in place and can start to think about the advanced features you’d like to implement – in the next few weeks or months.

(Elapsed time – maybe 1-3 months)

Or, we can do it the OpenPublish way. Download the installation setup (from here), run the setup, Get a key from Open Calais (here), enter it into OpenPublish.

Done. Start writing or grabbing feeds. You’re finished.

(Elapsed time – maybe 1 hour)

But – here’s where things start to get very interesting. OpenPublish isn’t just a quick way to install Drupal. OpenPublish uses Calais semantic technology (look at that – seven paragraphs in and the first time we’ve used the word semantic) to provide features even the big guys don’t have. Here are a few examples:

• Articles are automatically tagged with the people, places, companies, geographies and other elements inside them. You can do this automatically by setting relevance thresholds or do it interactively where Calais suggests and you approve.

• You can automatically tag your archives. Thousand of articles – no problem. Millions – give us a call and we’ll work something out to get it done in a day or two.

• You can automatically create topic hubs on any tag (e.g. Drupal vocabulary), set of tags, logical arguments about tags. Want a topic hub on “Natural Disasters” in California? About five clicks and it’s done – and it will maintain itself forever.

• “More like this” functionality is built right in. Your readers can see other related content on your site or – at your option – on other blogs or mainline news sources.

• Map integration, RDF generation and exposure, lots of other cool stuff.

What we like is that the semantics aren’t the goal here – they’re simply the enabler for a high performance publishing platform.

If you’re a publisher and you want help customizing the installation you should contact our friends at Phase2 and they’d be happy to help. If you’re a smaller non-profit, an advocacy organization or generally someone who doesn’t have a lot of money or time – OpenPublish can literally get you up and running in hours.

The Calais Initiative is proud to sponsor the development of the Drupal modules underlying OpenPublish and proud to work with the Phase2 team – they’re a great group of people.

P.S. It’s all free.

P.P.S Nancy Kho wrote a great overview here.

In the first post, Kas discusses the power of “Metadata as a Service” – in short what can you make happen if metadata generation is widely available to your content creation, management and consumption tools.

What’s great is that he doesn’t stop there. In his second post he goes on to construct an OpenOffice plugin that automatically meta-tags your content as you’re creating it. This has obvious benefits for content management and search across or outside the enterprise.

Now – take what Kas has done and extend it to the Linked Data cloud as we’ve done with Calais 4.0. Beyond metadata we now have super-metadata. By using the Linked Data capabilities built in to Calais you could not only tag an article as being about say “IBM” – but insert the fact that IBM is headquartered in New York, That New York is part of North America and that IBM has an SIC code of 8742 and others.

Here’s the Calais URI for IBM: Start exploring the DBPedia links at the bottom and I’m sure you’ll think of some interesting use cases.

• Slow Travel England – Canal Boats in England, how to plan your trip –
• Argilos Archaeological Site > Application Form > –
• Kalat –
• Past Horizons Volunteer Projects –
• Minnan nettikolo. » In English: XP and Ubuntu Dual Boot – S10 ubuntu
• shopaddonics – Network Attached Storage adapter –
• Vital Hotel Tel Aviv –
• Life in the Linked Data Cloud – Calais Release 4 Coming Jan 09 – An introduction to the Linked Data capabilities being introduced as part of Calais Release 4
• InternetNews Realtime IT News – Web 3.0: Semantic Web Turns to the Mainstream –
• SacredFacts: Journalists and the Semantic Web – Good introductory overview of the semantic web for journalists

The Gist: Release 4 of Calais will be a big deal. In that release we’ll go beyond the ability to extract semantic data from your content. We will link that extracted semantic data to datasets from dozens of other information sources, from Wikipedia to Freebase to the CIA World Fact Book. In short – instead of being limited to the contents of the document you’re processing, you’ll be able to develop solutions that leverage a large and rapidly growing information asset: the Linked Data Cloud.

The goal of this post is just to give our community a heads-up to start thinking and planning.

During the course of 2008 we’ve had three significant releases of Calais, with additional point releases nearly each month along the way. We’ve added new knowledge domains, improved performance, delivered integration with a range of tools and developed new user-facing applications. It’s been a year of amazing growth in our developer community and the capabilities of the Calais service.

While every previous release has accomplished something significant, Release 4 is going to introduce something that we think is game changing – and that’s life in the Linked Data cloud. It’s important enough that we want to give all the members of our community time to think about it, prepare for it and get your brains in gear on how you might use it.

Every release of Calais up to this point has focused on meeting the need to extract semantic information from text. Release 4 builds on this by creating the ability to harvest the Linked Data cloud using that semantic data.

For this all to make sense we need to introduce a few things. If you already know about de-referenceable URIs and the Linked Data cloud – skim ahead. If not – please take a moment to ingest the background you need.

When you send text to Calais it returns several things: entities, facts, events and categories. For purposes of today’s discussion we’re going to focus in on entities. Entities are just what they sound like – they are things. Some specific examples are people, companies, organizations, geographies, sports teams and music albums.

When Calais extracts an entity from your text it returns (at least) a few things. It tells you the name of the entity and it tells you what type of entity it is. Unlike other extraction services we don’t just return a list of things – Calais tells you it found a thing of type=Company and a value=IBM or type=Person and value=Jane Doe. But – there’s something else Calais returns that hasn’t meant very much up until now: it returns a Uniform Resource Identifier (URI) for that entity. There’s nothing magic about URIs – they are simply a unique identifier for every entity that Calais discovers. Here’s an example (it’s not pretty) of what the URI for the Company IBM looks like:

d.opencalais.com/comphash-1/7c375e93-de13-3f56-a42d-add43142d9d1

Well, that doesn’t look very useful does it? If you were to pull up that URI (when Release 4 is out) all you’d see is RDF with links to places called DBpedia and Freebase and Reuters. But keep those links in mind: they’re the key to a whole new world.

Linked Data is the name of a movement underway (not too surprisingly, initiated by Sir Tim Berners-Lee) that sets a standard and expected behavior for publishing and connecting data on the web. This isn’t about publishing web pages – this is about turning those web pages into data that’s accessible to programs to work with. We’ll give you a quick example to make it real: Wikipedia is one of the single largest sets of information across a broad range of topics in the world. It’s really great if I’m a person who’s casually looking for information on a particular topic – but it’s not so great if I’m a computer program that wants to use that data. Why? Because it’s formatted and organized for people – not computers – to read.

But Wikipedia has a twin – in fact a Linked Data twin – called DBpedia. DBpedia has the same structured information as Wikipedia – but translated into a machine-readable format called RDF and accessible via the Linked Data standards. And, Wikipedia is not alone. A growing cloud of information sets from DBpedia to the CIA World Fact Book to U.S. Census data to Musicbrainz – and many others – is becoming available. What’s important is that this cloud is 1) growing, and 2) interoperable. There are “pointers” from entries in DBpedia to entries in Musicbrainz and back to entries in Geonames – it’s another big Web – but this time it’s a Web of Data.

So – lots of words and arcane concepts. Let’s try to bring it all together into something that makes sense. We’ll put one sentence out there – and then we’ll give a few examples.

Beginning with Calais Release 4 you and the programs you develop will be able to go from many of the entities Calais extracts directly to the Linked Data Cloud.

A simple example:

I want to process today’s business news. For each article I want to extract all of the companies mentioned – but only if the article also mentions a merger or acquisition. I am only interested in companies whose headquarters (or those of their subsidiaries) are located in New York State. Do all of that and give me a widget for my news site titled “Merger Activity for NY Consulting Companies”. And oh, by the way, this isn’t a research project – I want you to do it real time for the 10,000 pieces of news I process every day.

How would you do that? Option 1 is to hire a bunch of researchers, give them a fast internet connection and teach them to type very very fast.  Option 2 is to write some code that looks like this:

For each Article

Submit to Calais, get response

If MergerAcquisition exists then

For each Company

Retrieve Calais Company URI, extract DBpedia link

Send Linked Data inquiry to DBpedia, get response

If CompanyIndustry contains “Consulting”

If CompanyHeadquarters = “New York”

Put them on the list

For each subsidiary

Send Linked Data query to Dbpedia, get result

If CompanyHeadquarters = “New York”

Put them on the list

(lots of endif’s)

Print the list

That really is a pretty straightforward example. How about companies in the news with at least one subsidiary doing business in an area that the CIA Factbook considers dangerous? Or books released by authors who attended Harvard who live in Ohio? Or … . We think you get the idea.

So. The summary. The combination of semantic data extraction (generic extraction, tags, keywords won’t do the trick) + de-referenceable URIs (entity identifiers you and your programs can retrieve) + the Linked Data Cloud = amazing stuff.

We’d like you to start thinking about it.

iPlayerist by Geography

iPlayerlist is an interesting application that takes shows available via the BBC iPlayer and allows you to find them by topics, times and other attributes. Andy @ mibly.com has just rolled out an enhancement that uses the new Calais geo-location capabilities to find shows based on the locations mentioned in their descriptions. Available here I think it’s a great example of a simple, clean way to improve the user experience using semantic metadata extraction. Unfortunately viewing many of the resulting videos won’t work unless you’re in the U.K. This isn’t iPlayer’s fault – it’s a limitation the BBC has put in place.

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Calais Geo Location Tutorial and Demo App

Guilhem Vellut has put together a nice demonstration app that shows the Calais geo-location features in action. While I really like the application (you can see it here) it’s the blog post he wrote giving the details of exactly how he built the applications – including code samples – that’s really great. By investing the time to document what he did and how he got everything working together he’s provided a great jumpstart for anyone else wanting to experiment with Calais geo-location. Thanks!

Web 2.0 created a problem – overwhelming content overload. Web 3.0’s job is solve that problem. That’s it.

Maybe later on I’ll write a few thousand more words around the details. But that’s what they are: details. Figure out how to decrease content overload in publishing, in user generated content, in social networks and in search. Stop worrying about the killer app. Just make things better.

Greg Boutin

Greg Boutin wrote a fairly in-depth piece on SemanticProxy. In this article Greg reviews SemanticProxy’s performance and asks a number of questions about whether it’s truly “Semantic”. So – second in a series of cheating by republishing responses I’ve written… here we go.

Greg’s original article is located here

Greg:

I thought I had responded to this post – but it appears it was one of those many responses I’ve composed in my head while driving or whatever and never actually gotten down in writing.

First, a couple of things that may need clarification.

SemanticProxy is Calais. What SemanticProxy does is to take the burden of fetching a web page, cleaning HTML, calling Calais and all that off the developer. It does all of that for you and returns the results as RDF – or as HTML for demonstration purposes. So – any functionality in Calais is automatically reflected in SemanticProxy. The main technical challenge with SemanticProxy other than engineering for scalability is simply HTML cleaning. One thing we’re thinking of is the creation of a simple tag publishers can embed to indicate the start/stop of the “core” content on a page.

The second area is around the engine underlying Calais. In your post you mention that you assume it’s a statistical engine – it isn’t. The Calais engine is built on core Natural Language processing (NLP) technology augmented by lexicons and statistical methods. It works by parsing out the parts of speech into core elements and then applying a three-tiered set of pattern recognition and rule-based approaches wrapping up with a voting and scoring system that selects from the candidate entities, facts and events. The rules and pattern recognition techniques are tuned to identify specific types of entities (people, places, organizations, etc), facts (Person:JobPosition, Person:PoliticalAffiliation, etc) and events (NaturalDisaster, SportingGame, EarningsAnnouncement, etc). The specific elements that Calais understands are documented on our site and expand by 5-15 each month.

Calais also supports “Semi-Exhaustive Extraction” (SEE) for those that want to dive into the deep end of the semantics pool. In SEE we extract all relationships between Thing1 and Thing2 if we can type at least one of the things.

Entity recognition will always be a “IS A” type predicate. “John Doe” “IS A” “Entity Type Person” – so all of our entity recognition will automatically fall into this category.

Facts and events are a little more complicated. For example let’s take something simple like Calais extracting that a person has a particular job title at a particular company. I’m not going to even attempt to write out the RDF – but the basics of that type of relationship would look like:

“John Doe” “IS A” “Person”
“John Doe” “Has the Title” “Chief Wrangler” “AFFILIATED WITH” “ACME”
“ACME” “IS A” “Company”

That’s not even close to RDF – but you get the idea.

So – are we using “Smart” predicates – I think so. Everything we identify (other than simple entity recognition – which is the easy part) is represented in RDF as a series of relationships and attributes. Every fact we identify is, in essence, it’s own smart predicate. Every event is built of of facts and entities.

What we don’t do is deliver any level of analysis beyond what’s presented to us. We don’t dip into the global linked data brain or Dbpedia or other assets to find and deliver more information about what we’ve extracted. If we tell you someone is a “Person” – we don’t tell you that people are mammals. As far as I’m concerned – that’s where linked data and large scale “describe the world” ontologies come in.

So – in summary. Entity recognition (the relatively easy part of what we do) is always about “IS A” type relationships. The harder (and cooler in the long run) stuff is much more sophisticated.

Also – one (well two) exceptions to the “we don’t augment with external data” statement above. In our current technology preview release we’ve rolled out disambiguation around companies and geographies. What this means is that if an article says IBM, IBM Research, IBM Limited or IBL Labs – we’ll tell you it’s really “IBM” and give you the appropriate identifying information (Ticker, web site, etc). We do this using a BIG table – but we also go beyond that and look for contextual clues like industries and geographies that will help us narrow things down.

Geographies are similar – “Longhorns” are more likely the be associated with Paris, TX than with Paris, France.

Long response – but I felt a few of these things were worth clarifying. We’re really enjoying the widespread adoption of Calais (almost 1.5M transactions per day and climbing) – but at this point most of the use cases are barely scratching the surface of what Calais provides. Once people have gotten over the current focus on entity recognition (tag clouds anyone?) we hope they’ll step back and explore some of the more powerful semantic capabilities Calais has to offer.

Regards,

Tom

Mark Gould

Mark Gould wrote a nice overview of Calais and SemanticProxy.com here http://bit.ly/gL1Aq. Because this was an introduction to Calais for a new audience oriented toward brand and marketing – I though it was worthwhile to respond with a basic overview of what Calais is about and why we’re doing it. Given that the response ended up being fairly lengthy – I though I’d share it here as well. Some general thoughts on the Semantic Web vs. The Semantic Stack, barriers to adoption, getting to critical mass and reality vs. philosophy.

–

First, thanks for taking note of Calais. We’re still deep in the learning curve and the more that different people with different needs think about it, try it out and give us feedback the better.

If you’re just starting to look into this area – a word of warning. It’s very important to distinguish between the vision of the Semantic Web and the stack – the defined set of standards – that will enable the Semantic Web. In my view the Semantic Web is an aspiration comprised of 1) use of the semantic stack and 2) a critical mass of adoption across the web. While we’re seeing many instances of adoption of the technologies – we have a long ways to go before we reach critical mass.

So – how do we move toward critical mass? What Calais is trying to do is address what we see as the central rate-limiting factor for adoption: the generation of high quality semantic metadata for unstructured content such as news, reports, novels – whatever. While the standards are well defined for how to represent this metadata we’re still left with one simple issue: it takes time and it costs money. Given that the “semantic consumer” end of the story is still relatively undeveloped, few writers and publishers can afford to invest that time and money.

Calais doesn’t solve this problem – but it does throw some fuel on the fire. By automating the generation of semantic metadata with a very high degree of accuracy we hope to jumpstart the adoption curve. If there’s lots of semantic content out there people will build great semantically enabled applications. If there are great applications people will invest in semantically enabled content.

The best way to take it for an initial spin is with the Calais viewer application at http://bit.ly/4DXfKw . Copy a news article or such, paste it in and see how we do. In general you’ll see better results with the viewer than with SemanticProxy.com because the proxy has additional work to do such as cleaning HTML pages. This work can create noise that reduces accuracy.

One last point. You don’t have to believe in or even agree with all of the philosophy around the Semantic Web to take advantage of it. There are a well-defined set of standards from RDF to SPARQL and capabilities such as Calais that can add value to what you’re doing today. Grab a piece of that stack and make something cool happen.

Regards,

We’ve all been to the conferences, we’ve all had the meetings, whether we’re builders or consumers – it’s clear that something is in the air around this topic.

We’re also impatient. The Semantic Web (stack, apps, whatever) has been right around the corner for a little while now. That impatience is causing us to spend an inordinate amount of time casting around for the application that’s going to prove the naysayers wrong, change the game, change the world.

And because we’re humans, tool users and pattern matchers – we end up landing at an answer that feels safe, that we know works, that people understand, that’s generated a bunch of billions of dollars: Search. And then we tie a bow on it so it feels new and ….. we have Semantic Search.

Let’s put aside the whole issue of whether semantic search is the killer app for the moment.  I personally think it may be one of the functions that see dramatic improvement through semantic technologies – but it doesn’t feel, today, like the application that’s going to knock our socks off.

I’d also like to take off the table the applicability of semantic search to tightly constrained, well defined, rigidly controlled knowledge domains. We all know it can do some great stuff when applied to questions about gene expression in the nasal epithelial cells of the South African Tree Frog under ultraviolet stimulation – but I think it might be a little more interesting to concentrate on searches that the other 99% of the bell curve care about.

Part of the problem may be that we’re using the term Semantic Search. I have no idea what it means. When I’m talking with someone about it we have no shared understanding. I absolutely cannot explain it to non semantageeks. So, let’s deconstruct semantic search into it’s constituent components and talk a bit about how and whether semantic technologies might actually make it better.  The results of the dissection are here on the table….

1. What kinds of questions can we ask? Can we embed logic in our questions? Do we expect inference in our results?
2. How can we ask them – keywords, natural language and all that jazz.
3. Generating the “right” result set for the query.
4. Displaying the result set in the most effective manner
5. Making money from doing all that

So – my challenge to myself is to write a brief (well, maybe not too brief) post about each of these subtopics and talk about how semantics can – or cannot – make it better. Until we get down to this level of granularity “semantic search” is just a catchphrase without, well … semantics.

• SFBags – WaterField Designs – Case for the Amazon Kindle –
• Timbuk2 Bags – Commute Daypack – Ballistic Fabric –
• Wandora 2008-08-28 (Default branch) | Open Source Pixels –
• Bionics – Wikipedia, the free encyclopedia –
• Biomimetics – National Geographic Magazine –
• Api-Madness.com » Blog Archive » Setting Up API Service Interactions –

• Powerset – I'm still confused
• Calais Release 2.1 – Relevancy, Pipes and Gnosis | OpenCalais –
• The Semantic Web Gang » Blog Archive » August 2008: the Semantic Web Gang discusses search and the Semantic Web – Podcast on Semantic Search w/ the Semantic Web Gang.
• SearchMonkey Rides with the Semantic Web Gang (Yahoo! Developer Network Blog) –
• Calais | drupal.org – Calais modules to integrate Calais with Drupal. Automated node tagging, assists in content aggregation, blogging.
• Calais Ecosystem: Gnosis for Firefox & IE « context:forge – Blog posing on Gnosis – a Firefox and IE interface that uses Calais for tagging and searching web content
• State of the Semantic Web: Know Where to Look – Good quick overview on State of the Semantic Web – basic premise is that the technology is bubbling up all over – I buy it.

If you’re like me you spend a lot of your time on the web reading the news, reviews and blog postings. It’s great – but sometimes I wish I had my own research assistant to highlight the important stuff and do a little research for me. If I’m reading about a person or place or company I’m interested in I find myself doing a lot of copying, going to Google or Wikipedia, pasting, searching, finding the tab I was originally on, finding my place in the article, etc, etc. And I’m mostly just reading because I’m interested -  researchers, bloggers and journalists spend many hours at a stretch doing this.

Gnosis isn’t quite as good as your own personal research assistant – but it’s a step in the right direction.

Built as a plugin for both Firefox and IE, Gnosis sits in the background and analyzes what you’re reading. Using the Calais web service it finds the people, companies, organizations, locations and quite a few other things in the text and marks them with a fairly subtle underline.

When you hover over one of those items Gnosis pops up a smart and contextually relevant information box that lets you search for companies in places that know about companies, people in places that know about people, locations in things that know about locations. You get the idea.

You can do this on demand when you’re reading something – or you can update the Gnosis preferences and tell it to do it automatically on specific sites. I’ve set mine for automatic tagging on most of the major news sites, a few blogs and Wikipedia. A small warning – Gnosis sometimes breaks on Ajax heavy sites like the Google RSS reader. We’re working on that.

Speaking of Wikipedia – Gnosis is a great tool for use there. While the individuals creating Wikipedia articles try to do a good job hyperlinking items in the article to other relevant Wikipedia articles – they often miss the boat. Many of the items in the article that should be hyperlinked are not – forcing you once again into a cycle of cut, paste, search, etc. Gnosis solves that by automatically hyperlinking relevant items and allowing you to navigate directly to the appropriate Wikipedia page.

If you want a quick snapshot of all of the people, places, things, etc mentioned in an article then just open the Gnosis sidebar. It will give you a quick overview of everything it has found and allow you to navigate directly to the things you’re interested in.

That’s the description: here’s what’s cool. Gnosis let’s you apply the power of high end natural language processing and semantic analysis in a simple way to an everyday task – reading on the web. You don’t need to understand RDF triples or the semantic stack – it just helps you get something done. And – the current version of Gnosis is just the start. Future releases will draw on the expanded capabilities of Calais to tell you what the most relevant items are in what you’re reading and to link those items to the growing linked data ecosystem. Stay tuned.

The Gnosis homepage is locate here.

• Feeds | Reuters Spotlight – Reuter API for accessing content
• NewsKnowledge Full Text News Pricing – newsknowledge.com –
• http://pipes.deri.org:8080/pipes/ – Pipes for semantic data. Have to start experimenting with this.
• MacBook Pro ExpressCard EVDO setup & review – I have a new 3g card. I am connected everywhere. I am happy.
• Instapaper – Store links to things you want to read later. Then read them on your iPhone or Touch. Now you have no excuse not to go into complete information overload.
• KOKOGIAK – Alan Taylor's blog – a creative guy – creator of Boston.com Big Picture
• Photographers | Blogs | Reuters.com – On a photoblog kick today – another good one
• Waxy.org: Andy Baio lives here –
• The Big Picture – Boston.com – Great photoblog at Boston.com – Gigantic images, clean design

• Pointing the internet in a new direction | Technology | The Guardian –
• OK, computer: Automated news to grow – Press Gazette –
• » Tagaroo: Great blogging toolBlueprint » Blog Archive –
• Free! Why $0.00 Is the Future of Business –
• Five Ways To Calais V01 –
• Reuters' Labs brews more content mashups | PDA: The Digital Content Blog | guardian.co.uk –
• Web 3.0 is only partly about semantics – Good (actually great) article by Vincent maher on use of semantic tools in the re-launch of the Mail & Guardian Online
• LinkedFacts.com – Something cool is coming…
• Calais Ecosystem: Calais for Drupal « context:forge –
• Amazon Confirms Student Version Of Kindle –

Calais is an initiative by Thomson Reuters to provide one of the core building blogs of the Semantic Web: semantic metadata generation. At the core of Calais is a web service that ingests text content, analyzes using natural language processing, machine learning, lexicons and statistical analysis to extract semantic data from the text and return it as structured information – primarily as RDF. Enough about Calais – I’ll write a big long post about it in the near future.

One of our biggest goals with Calais is to develop – or help others develop – tools that translate this from geekdom to real world usability. One of the areas of focus for that is to integrate Calais within a variety of content presentation and management platforms. There’s a wide range of those platforms – but Drupal stands out as being one of the fastest growing ones in the mid-tier publishing space.

Shortly after Calais was released two members of the Phase2Technology team – Frank Febbraro and Irakli Nadareishvili just stepped up and made it happen by building the Calais Modules for Drupal.

These modules provide a strong building block for construction semantically-enabled Calais applications. The modules provide seamless integration between a range of Drupal node types and the Calais service.

From their description…

The Calais module lets you configure which Content Types you want to request Calais metadata on update. The entities returned can then be automatically assigned to vocabularies related to the Content Types, or it can only suggest terms based on the Calais metadata and allow the user to select the terms you want to associate (think of del.icio.us recommending tags). A flexible set of hooks allows 3rd party modules to make modifications before or after Calais terms have been applied. There are many level of configuration and integration and this is just the beginning.

The Calais Tag Modifier module allows for basic blacklisting of tags, so that you never get terms suggested that you don’t care about. The term substitution mechanism also allows you to modify returned metadata before it gets assigned or suggested.

Beyond what Phase2 has developed to date, the Calais Initiative and Phase2 have agreed to work together over the coming six months to release a series of significant enhancements built on the Calais modules. These enhancements will be oriented toward even tighter integration of Calais with Drupal and providing a comprehensive Calais-powered set of capabilities such as topic hubs and other publisher-oriented features.

So – that’s the description: here’s what’s cool. One of the hottest publishing platforms in the world is integrated with Calais. Users can get access to Calais’ capabilities with essentially zero effort. And – all of this was built buy two highly motivated guys that saw a need and just moved in and got it done.