The Third International Workshop on the role of the Semantic Web in Provenance Management will be held in conjunction with the Ninth Extended Semantic Web Conference (ESWC-2012) on May 27 or 28 in Heraklion, Greece. The workshop’s objectives are to explore opportunities offered by the Semantic Web technologies in the context of the management and exploitation of provenance and document the role of provenance in real-world Semantic Web applications.
The one day workshop will include presentations of full research papers, short position papers, a panel on the W3C provenance working group proposals, and demonstrations of prototypes and working systems. Submit papers and demonstration proposals by 4 March 2012.
It’s very nice to see ebiquity alumna Akshaya Iyengar (MS, 2011) helping Wikipedia during its fund raising campaign. If you visit Wikipedia you might see her gracing a page you get, as I did just a minutes ago. See this screenshot and read her statement on why she has been donating to Wikipedia here. Her generosity has inspired me to contribute also.
A part-time, two person effort UMBC VP for Research Don Engel and his wife Marianne nearly won the DARPA Shredder Challenge. Their entry, Schroddon got a late start, but held the top leaderboard spot for quite a while before being bested by “All Your Shreds Are Belong To U.S.” at the end. The first prize was $50,000 and second was … well, priceless.
WWW, ISWC and WebDB are the top Web conferences based on Microsoft Academic Search citation data.
Last week HCI researcher Antti Oulasvirta has an interesting post on ranking HCI conferences using the average citations per paper based on data from Microsoft Academic Search (MAS). Some of the results surprised him, including that the venerable CHI was not the top conference in this group. His ranking metric for conference significance is essentially the impact factor used for journals, a measure of the average number of citations a paper in a given journal receives in a time period. The IF metric has become widely used in the scholarly journal publication industry since it was defined by Eugene Garfield and first implemented by the company he founded, the Institute for Scientific Information.
Microsoft Academic Search provides citation and publication numbers for conferences in sixteen different subjects domains and a number of sub-domains for each. For computer science, there are 24 sub-domains including one for “World Wide Web” conferences. Following Oulasvirta, we ranked Web technology conferences using the average number of citations received in the last ten years. Starting with 68 Web technology conferences in the MAS collection (not a complete list, btw), I narrowed the set to those that had at least 100 papers in the past ten years and some papers in the past five. This resulted in 26 conferences, eliminating many series that only ran a few times or have stopped. Here are the results.
The results should only be taken as a rough estimate of conference impact. One reason is that IF is only a measure and does not take into account all aspects of scientific importance. For example, as computed here, all citations count equally, including those from high- and low-ranking sources. Another is that while Thompson-Reuters (nee ISI) journal citation data is carefully collected and curated, the Microsoft Academic Search data is the result of a largely automated process that starts with data from Bing. When I tried using the citation information from the past five years, for example, I noted that it reported 23 papers in the past five years for Adaptive Hypermedia and Adaptive Web-Based Systems. This is because the conference merged with User Modeling in 2009 to become User Modeling, Adaptation, and Personalization. Yet another shortcoming is that the MAS list of Web conferences in not complete, for example, omitting the popular ESWC, which has been running since 2004.
The original excel spreadsheet (with full conference names hidden in column B) and a PDF version are available.
On Facebook, it’s 4.74 degrees of separation, not six, according to a new study by study by researchers at Facebook and the university of Milan.
“Think back to the last time you were in a crowded airport or bus terminal far from home. Did you consider that the person sitting next to you probably knew a friend of a friend of a friend of yours? In the 1960s, social psychologist Stanley Milgram’s “small world experiment” famously tested the idea that any two people in the world are separated by only a small number of intermediate connections, arguably the first experimental study to reveal the surprising structure of social networks.
With the rise of modern computing, social networks are now being mapped in digital form, giving researchers the ability to study them on a much grander, even global, scale. Continuing this tradition of social network research, Facebook, in collaboration with researchers at the Università degli Studi di Milano, is today releasing two studies of the Facebook social graph.
First, we measured how many friends people have, and found that this distribution differs significantly from previous studies of large-scale social networks. Second, we found that the degrees of separation between any two Facebook users is smaller than the commonly cited six degrees, and has been shrinking over the past three years as Facebook has grown. Finally, we observed that while the entire world is only a few degrees away, a user’s friends are most likely to be of a similar age and come from the same country.
A story in the New York Times, Separating You and Me? 4.74 Degrees points out how the scale of social network studies have grown.
“The original “six degrees” finding, published in 1967 by the psychologist Stanley Milgram, was drawn from 296 volunteers who were asked to send a message by postcard, through friends and then friends of friends, to a specific person in a Boston suburb. The new research used a slightly bigger cohort: 721 million Facebook users, more than one-tenth of the world’s population.”
The UMBC Ebiquity Lab is hosting the November meeting of the Lotico Central Maryland Semantic Web Meetup from 6:00-8:00 pm on Tuesday November 15 in room 456 of the ITE building (directions).
“This is free social network and meeting community open to industry, government and academia. The goal of the organizers is to create a vendor neutral environment for open discussion and provide the membership with a valuable resource of information on industry trends and ongoing research.”
All are welcome. If you want to attend, please join the Central MD Semantic Web Meetup group and RSVP. The meeting will start with a pizza social from 6:00pm to 6:45pm and then continue with a series of short presentations of current Semantic Web research being done in our lab.
The Semantic Web provides the technology and knowledge constructs to create a rich notion of context that goes beyond current networking applications focusing mostly on location. The context model includes location and surroundings, the presence of people and devices, inferred activities and the roles people fill in them.
Evidence for a table’s meaning can be found in its metadata but currently requires human interpretation. We describe techniques grounded in graphical models and probabilistic reasoning to infer meaning associated with a table. Using background knowledge from the Linked Open Data cloud, we automatically infer the semantics of column headers, table cell values (e.g., strings and numbers) and relations between columns and represent the inferred meaning as graph of RDF triples.
Users need better ways to explore linked open data collections and obtain information from it. Using SPARQL requires not only mastering its syntax and semantics but also understanding the RDF data model, the ontology used by the DBpedia, and URIs for entities of interest. Natural language question answering systems solve the problem, but these are still subjects of research. We are developing a compromise approach in which non-experts specify a graphical “skeleton” for a query and annotate it with freely chosen words, phrases and entity names. The combination reduces ambiguity and allows us to reliably produce an interpretation that can be translated into SPARQL.
We propose a semantically rich, policy-based framework to automate the lifecycle of cloud services. We have divided the IT service lifecycle into the five phases of requirements, discovery, negotiation, composition, and consumption. We detail each phase and describe the high level ontologies that we have developed to describe them. Our research complements previous work on ontologies for service descriptions in that it goes beyond simple matchmaking and is focused on supporting negotiation for the particulars of IT services.
See this map for the building location and information on visitor parking. The recommended lot is just across from the entrance to UMBC’s campus from I-95. To access it, turn right and then turn left at the first stop sign onto Administration Drive. You can park on the lower level after 3:30pm by putting two quarters into the box at the gate. The upper level has parking meters that take quarters ($1/hr) and a change machine is located near the entrance.
It still won’t be able to pass as a human like the Nexus 6, but Honda’s Asimo robot now enjoys more autonomy.
Honda Motor Co., Ltd. today unveiled an all-new ASIMO humanoid robot newly equipped with the world’s first1 autonomous behavior control technology. With a further advance in autonomy, the all-new ASIMO can now continue moving without being controlled by an operator. Moreover, with significantly improved intelligence and the physical ability to adapt to situations, ASIMO took another step closer to practical use in an office or a public space where many people come and go.
If you are in the DC area this weekend and are interested in using Semantic Web technologies, you should come to the AAAI 2011 Fall Symposium on Open Government Knowledge: AI Opportunities and Challenges. It runs from Friday to Sunday midday at the he Westin Arlington Gateway in Arlington, Virginia.
Join us to meet the thought governmental and business leaders in US open government data activities, and discuss the challenges. The symposium features Friday (Nov 4) as governmental day with speakers on Data.gov, openEi.org, open gov data activities in NIH/NCI and NASA and Saturday (Nov 5) as R&D day with speakers from industry, including Google and Microsoft, as well international researchers.
This symposium will explore how AI technologies such as the Semantic Web, information extraction, statistical analysis and machine learning, can be used to make the valuable knowledge embedded in open government data more explicit, accessible and reusable.
See the OGK website for complete details.
Computer Science pioneer John McCarthy died at his home in his sleep on Monday. He was 84. He is noted for creating the Lisp programming language, making ground-breaking contributions to Artificial Intelligence (including naming the field), adding important results to the mathematical theory of computation, and helping to develop computer time sharing. He studied mathematics under John Nash at Princeton
McCarthy held the first “computer-chess” match in the mid-1960s between scientists in the US and the USSR, transmitting the moves by telegraph. The soviet team ran on inferior hardware and used Claude Shannon’s brute-force Type-A strategy while the MIT team had an IBM 7090 implemented Shannon’s more sophisticed Type-B approach that used a heuristic plausible move generator. The Soviets won.
McCarthy was born in 1927 in Boston and taught himself higher math using Caltech textbooks when his family moved to the area, allowing him to take advanced classes when he enrolled as a teenager. He received a Ph.D. from Princeton in 1951.
He won the Turing Award from the Association for Computing Machinery in 1972 and the National Medal of Science in 1991. Over the years, he held faculty appointments at Princeton, M.I.T., Dartmouth, and Stanford University, where he spend his las 39 years.
Yesterday I made a purchase at the CVS store on Edmondson Avenue in Catonsville using Google Wallet on a Nexus S 4G phone with NFC.
NFC is near field communication, an RFID technology that allows communication and data exchange between two devices in close proximity, e.g., within a few inches.
Several current smartphones have NFC chips including the Samsung's Google-branded Nexus S 4G and more are expected to include it in the coming months and years.
The first, and perhaps most significant, use of NFC will be enabling mobile phones to serve as "virtual credit cards", especially for small amounts that don't require a signature. The range of potential applications is much greater and will no doubt evolve as mobile NFC-enabled devices become ubiquitous.
Buying something at the CVS (OK, … it was candy) this way was fun. My phone made satisfying noises as it talked to CVS's payment station and the clerk, who had not had anyone use a NFC device, was properly mystified. Using it was marginally easier than swiping a credit card, but maybe even a small amount of increased convenience is worth it for such an everyday transaction.
One limitation of Google Wallet is that it currently only works with Sprint on a Nexus S 4G and with either a Citi® MasterCard® card or a Google Prepaid Card. You can load money into the latter with most any credit card and Google will get you started by adding $10 to it as an incentive.
By the way, for what it’s worth, I only recently realized that the robots in Philip K. Dick’s novel “Do Androids Dream of Electric Sheep?” were called androids and the dangerously independent new model was the Nexus-6, developed by designed by the Tyrell Corporation.
mincemeat.py is a super-lightweight, open source Python implementation of the popular MapReduce distributed computing framework that only depend on the Python Standard Library.
Just install the single source file on a set of machines and invoke the script on them with a password (for authentication) and the IP address of the host and your workers are good to go. Then, using the same package, run simple server program that defines map, reduce and your data source.
While it’s only 350 lines of Python, the package looks great for teaching or experimenting with the MapReduce concept as well as being potentially useful if you work in Python.