Posted:February 29, 2008

Resurrecting Old Posts Brings a Smile, and Some Shudders

(Holy Leap Year, Batman!)

I’ve stated many times I hate WordPress upgrades. I know the sponsors have tried to make it easier over time, but upgrades are still painful, wrought with risk and error, and always force me to research and figure out what went wrong.

Why the Upgrade?

I last upgraded to WP v. 2.2.1, and with a real rant to accompany it.

Since then, some of us had been seeing some insidious stuff getting inserted into our RSS feeds, but had not been able to stem it. Then, I was doing my normal morning systems check and saw that my site was completely down, completely blank. Grrrr. Who knows what that specific problem was.

Version 2.3.3. had been announced with a fix for the RSS feed spam problem, so, rather than trying to diagnose and fix my current version, it was time to upgrade. (Grrrr.)

But, then I realized, possibly by doing so, I might also see a fix to a longstanding issue I had had with plug-ins somehow limiting my chronological listing of past posts. (Hooray!) That one had really been sticking in my craw, had caused me to de-activate some plug-ins I thought useful, and had led to only a handful of prior posts appearing.

The Benefits (sort of)

So, the upgrade was made. Sadly, no problems (other than the XML-RPC implementation issue) were solved. And, unfortunately, my chronological listings still only displayed when throttled back to the past 30 or so. (Grrrr.)

Well, s**t. So after (for what was for me, with some of my more complicated site aspects) nearly a two hour minor upgrade, the only real benefit I or my readers would see is that the site was no longer blank! This hardly looked like a good deal.

So, assuming the chronology problem fix was not near at hand, I decided to manually add the past entries back to my chronology page. (Actually, this sounds worse than it really is since I have learned some quick tricks for gleaning listings from other sites; I just turned those techniques on my own blog!). While grinding teeth to nubs, I did what everyone who works intimately with software often does: I did the workaround.

So, now all full listings have been restored (though still with some recent postings overlap; Grrrr).

What brought a smile was seeing some posts from a year or two ago that I liked and had completely forgotten; some others brought a shudder. Here are some older personal favorites:

Nonetheless, now all 250 or so posts on my site from Day 1 in early 2005 can now be seen again; it has been awhile! :)

The Problems

Naturally, that was not the end of the saga.

After making the upgrade, I noticed that all category listings and lookups had been wiped off my blog. I could see them in the MySQL and the editor still had the listing, but the site itself and the admin panel were blank.

Grrrr. (Try to stay calm and not panic.)

It’s another one of those deals where it is time to search like crazy and hope that someone more knowledgable than me has encountered the same problem and fixed it. Sure enough, in an obscure reference, I got the glimmer that maybe re-starting MySQL could fix the problem.

Well, it did. But go figure. . . .

Advanced TinyMCE

Thankfully, my Advanced TinyMCE plug-in that gives more editing functions works great for me in WP v. 2.3.3. At least that is a relief!

And so, we end on an anti-Grrrr note. :) Sweet dreams.

Posted by AI3's author, Mike Bergman Posted on February 29, 2008 at 12:44 am in Blogs and Blogging | Comments (0)
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Posted:February 20, 2008


Here’s a Sneak Peek at Some UMBEL Subject Graphs

We are proceeding apace with the first release of the UMBEL (Upper-level Mapping and Binding Exchange Layer) lightweight subject concept ontology. The internal working version presently has 21,580 subject nodes, though further review will certainly change that number before public release of the first draft.

UMBEL defines “subject concepts” as a distinct subset of the more broadly understood concept such as used in the SKOS RDFS controlled vocabulary or formal concept analysis or the very general concepts common to some upper ontologies. Subject concepts are a special kind of concept: ones that are concrete, subject-related and non-abstract. We further contrast these with named entities, which are the real things or instances in the world that are members of these subject concept classes.

Thus, in UMBEL parlance, there are abstract concepts, subject concepts and named entities.

The “backbone” to UMBEL is its set of these reference (“canonical” if you will) subject concepts. These subject concepts are being derived from the OpenCyc version of the Cyc knowledge base. The resulting 22 K nodes of this subject structure are related via the predicates of subclassof and type; these are the graph’s edges. The graph pictures herein are the first glimpse of this UMBEL backbone structure.

The Deep Dive

We can take the full network graph and do a bit of simulation of diving deep into its structure, as the following figures show.

The Big Graph

So, here is the big graph, with all nodes and edges (blue) displayed. This is just about at the limit of our graphing program, Cytoscape, which we estimate is limited to about 30 K nodes:

UMBEL Big Braph

The Top 750

Through the manipulation of the topological coefficient, which is a relative measure for the extent to which a node shares neighbors with other nodes, we can zoom in on the Top 750 (actually, 759!) node gateways or hubs. There are other ways to evaluate key nodes in a network, but this one fairly nicely approximates the upper structure or hierarchy within the graph:

UMBEL Top 750

The Top 350

By tightening the coefficient further, we can get a view of the Top 350 (actually, the top 336). Were the system live and not a captured jpeg, we could zoom in and read the actual node labels.

UMBEL Top 350

Two Degrees of Separation: Saab Example

The real value from a graph structure, of course, is that now we can make selections based on relationships, neighbors and distances for various reasoning, inference or relatedness purposes. This diagram begins by inputting “saab” as my car concept, and then getting all nodes within two links:

UMBEL Big Saab

The Saab Neighborhood

Alternatively, for the same “saab” car concept, I asked for all directly related links (in yellow) and did some pruning of car types to make the subgraph more readable and interesting:

UMBEL Saab Neighborhood

This ability to manipulate and navigate this large subject backbone at will should bring immense benefits. And, because of its common sense grounding, the early explorations of this first-glimpse UMBEL structure look very logical and clean.

UMBEL Status

Once we complete the next packaging and draft release steps, anyone will be able to play with and manipulate this UMBEL structure at will. The ontology and the tools we are using to manipulate it are all open source.

Our next steps on UMBEL will have us publishing the technical report (TR) of how we screened and vetted the subject concepts from the Cyc knowledge base, using an updated OpenCyc version. That document will hopefully gain some broader review and scrutiny for the canonical listing of subject concepts.

Of course, all of that is merely leading up to the Release 0 of the published ontology. We are working diligently to get that posted as well in the very near future.

A Note on the Graphs

These graphs were built using the super Cytoscape large-graph visualization framework, which I previously reviewed with glowing praise. The subgraph extractions were greatly aided by a fantastic add-in called NetworkAnalyzer from the Max-Planck-Institut für Informatik. I will be writing more about this add-in at a later time, including some guidance for how to use it for meaningful ontology analysis. But, in the meantime, do check this add-in tool out. Mucho cool, and another winner !

Posted by AI3's author, Mike Bergman Posted on February 20, 2008 at 4:21 pm in Adaptive Information, Semantic Web, Structured Web, UMBEL | Comments (1)
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Danny Ayers Has Penned a Must Read Semantic Web 101

Danny Ayers has just posted perhaps the best and most succinct explanation of the semantic Web and its relation to Linked Data I have seen: Semantic Web…in a Nutshell?

Please, all, I encourage you to read the bottom portion of this short posting carefully and to bookmark it for future reference. It doesn’t get much shorter or sweeter than this.

Nice job, Danny! Now, do you care to take on the update of the layer cake? :)

Posted by AI3's author, Mike Bergman Posted on February 20, 2008 at 11:08 am in Semantic Web | Comments (1)
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Posted:February 18, 2008

W3C Semantic WebWikipedia

Most Comprehensive Reference List Available Shows Impressive Depth, Breadth

Since about 2005 — and at an accelerating pace — Wikipedia has emerged as the leading online knowledge base for conducting semantic Web and related research. The system is being tapped for both data and structure. Wikipedia has arguably replaced WordNet as the leading lexicon for concepts and relations. Because of its scope and popularity, many argue that Wikipedia is emerging as the de facto structure for classifying and organizing knowledge in the 21st century.

Our work on the UMBEL lightweight reference subject concept structure has stated since the project’s announcement in July 2007 that Wikipedia is a key intended resource for identifying subject concepts and entities. For the past few months I have been scouring the globe attempting to find every drop of research I could find on the use of Wikipedia for semantic Web, information extraction, categorization and related issues.

Thus, I’m pleased to offer up herein the most comprehensive such listing available anywhere: more than 99 resources and counting! (I say “more than” because some entries below have multiple resources; I just liked the sound of 99 as a round number!)

Wikipedia itself maintains a listing of academic studies using Wikipedia as a resource; fewer than one-third of the listings below are on that list (which itself may be an indication of the current state of completeness within Wikipedia). Some bloggers and other sources around the Web also maintain listings in lesser degrees of completeness.

It is well documented the tremendous growth of content and topics within Wikipedia (see, as examples, the W1, W2, W3, W4, W5, W6 and W7 internal Wikipedia sources for gory details), with as of early 2008 about 2.25 million articles in English and versions in 256 languages and variants.

Download access to the full knowledge base has enabled the development of notable core references to the Linked Data aspects of the semantic Web such as DBpedia [5,6] and YAGO [72,73]. Entire research teams, such as Ponzetto and Strube [61-65] (and others as well; see below) are moving toward creating a full-blown ontologies or structured knowledge bases useful for semantic Web purposes based on Wikipedia. So, one of the first and principle uses of Wikipedia to date has been as a data source of concepts, entities and relations.

But much broader data mining and text mining and analysis is being conducted against Wikipedia, that is currently defining the state-of-the-art in these areas, too:

  • Ontology development and categorization
  • Word sense disambiguation
  • Named entity recognition
  • Named entity disambiguation
  • Semantic relatedness and relations.

These objectives, in turn, are mining and extracting these various kinds of structure for these purposes in Wikipedia:

  • Articles
    • First paragraph — Definitions
    • Full text — Description of meaning; related terms; translations
    • Redirects — Synonymy; spelling variations, misspellings; abbreviations
    • Title — Named entities; domain specific terms or senses
    • Subject — Category suggestion (phrase marked in bold or in first paragraph)
    • Section heading — Category suggestions
  • Article links
    • Context — Related terms; co-occurrences
    • Label — Synonyms; spelling variations; related terms
    • Target — Link graph; related terms
    • LinksTo — Category suggestion
    • LinkedBy — Category suggestion
  • Categories
    • Category — Category suggestion
    • Contained articles — Semantically related terms (siblings)
    • Hierarchy — Hyponymic and meronymic relations between terms
  • Disambiguation pages
    • Article links — Sense inventory
  • Infobox Templates
    • Name –
    • Item — Category suggestion; entity suggestion
  • Lists
    • Hyponyms

These are some of the specific uses that are included in the 99 resources listed below.

This is an exciting (and, for most all of us just a few years back, unanticipated) use of the Web in socially relevant and contextual knowledge and research. I’m sure such a listing one year by now will be double in size or larger!

BTW, suggestions for new or overlooked entries are very much welcomed! :)

A – B

  1. Sisay Fissaha Adafre and Maarten de Rijke, 2006. Finding Similar Sentences across Multiple Languages in Wikipedia, in EACL 2006 Workshop on New Text–Wikis and Blogs and Other Dynamic Text Sources, April 2006.See
  2. Sisay Fissaha Adafre, V. Jijkoun and M. de Rijke. Fact Discovery in Wikipedia, in 2007 IEEE/WIC/ACM International Conference on Web Intelligence. See
  3. Sisay Fissaha Adafre and Maarten de Rijke, 2005. Discovering Missing Links in Wikipedia, in LinkKDD 2005, August 21, 2005, Chicago, IL. See
  4. David Ahn, Valentin Jijkoun, Gilad Mishne, Karin Müller, Maarten de Rijke, and Stefan Schlobach. 2004. Using Wikipedia at the TREC QA Track, in Proceedings of TREC 2004.
  5. Sören Auer, Chris Bizer, Jens Lehmann, Georgi Kobilarov, Richard Cyganiak and Zachary Ives, 2007. DBpedia: A nucleus for a web of open data, in Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea, volume 4825 of LNCS, pages 715–728, November 2007. See
  6. Sören Auer and Jens Lehmann, 2007. What Have Innsbruck and Leipzig in Common? Extracting Semantics from Wiki Content, in The Semantic Web: Research and Applications, pages 503-517, 2007. See
  7. Somnath Banerjee, 2007. Boosting Inductive Transfer for Text Classification Using Wikipedia, at the Sixth International Conference on Machine Learning and Applications (ICMLA). See
  8. Somnath Banerjee, Krishnan Ramanathan, Ajay Gupta, 2007. Clustering Short Texts using Wikipedia, poster presented at Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Amsterdam, The Netherlands, pp. 787-788.
  9. F. Bellomi and R. Bonato, 2005. Lexical Authorities in an Encyclopedic Corpus: A Case Study with Wikipedia, online reference not found.
  10. F. Bellomi and R. Bonato, 2005. Network Analysis for Wikipedia, presented at Wikimania 2005; see
  11. Bibauw (2005) analyzed the lexicographical structure of Wiktionary (in French).
  12. Abhijit Bhole, Blaž Fortuna, Marko Grobelnik and Dunja Mladenić, 2007.Mining Wikipedia and Relating Named Entities over Time, see
  13. Razvan Bunescu and Marius Pasca. 2006. Using Encyclopedic Knowledge for Named Entity Disambiguation, in Proceedings of the 11th Conference of the EACL, pages 9-16, Trento, Italy.
  14. Razvan Bunescu, 2007. Learning for Information Extraction: From Named Entity Recognition and Disambiguation To Relation Extraction, Ph.D. thesis for the University of Texas, August 2007, 168 pp. See
  15. Luciana Buriol, Carlos Castillo, Debora Donato, Stefano Leonardi, and Stefano Millozzi. 2006. Temporal Analysis of the Wikigraph, in Proceedings of Web Intelligence, Hong Kong.
  16. Davide Buscaldi and Paolo Rosso, 2007. A Comparison of Methods for the Automatic Identification of Locations in Wikipedia, in Proceedings of GIR’07, November 9, 2007, Lisbon, Portugal, pp 89-91. See

C – F

  1. Ruiz-Casado, M., Alfonseca, E., and Castells, P. 2005. Automatic Assignment of Wikipedia Encyclopedic Entries to WordNet Synsets, in AWIC, pages 380-386. See
  2. Maria Ruiz-Casado, Enrique Alfonseca and Pablo Castells, 2006. From Wikipedia to Semantic Relationships: a Semi-automated Annotation Approach, in ESWC2006.
  3. Maria Ruiz-Casado, Enrique Alfonseca and Pablo Castells, 2007. Automatising the Learning of Lexical Patterns: an Application to the Enrichment of WordNet by Extracting Semantic Relationships from Wikipedia. See
  4. Sergey Chernov, Tereza Iofciu, Wolfgang Nejdl, and Xuan Zhou, 2006. Extracting Semantic Relationships between Wikipedia Categories, from SEMWIKI 2006.
  5. Aron Culotta, Andrew McCallum and Jonathan Betz, 2006. Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text, in Proceedings of HLT-NAACL-2006. See
  6. Silviu Cucerzan, 2007. Large-Scale Named Entity Disambiguation Based on Wikipedia Data, in Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL). See
  7. Cyclopedia, from the Cyc Foundation, an online version of Wikipedia that enables browsing the encyclopedia by concepts. See
  8. Wisam Dakka and Silviu Cucerzan, 2008. Augmenting Wikipedia with Named Entity Tags, to be published in IJCNLP. See
  9. Wisam Dakka and Silviu Cucerzan, 2008. Also, see the online tool (not yet implemented).
  10. Turdakov Denis, 2007. Recommender System Based on User-generated Content. See
  11. EachWiki, online system from Fu et al.
  12. Linyun Fu, Haofen Wang, Haiping Zhu, Huajie Zhang, Yang Wang and Yong Yu, 2007. Making More Wikipedians: Facilitating Semantics Reuse for Wikipedia Authoring, at ISWC 2007. See

G – H

  1. Evgeniy Gabrilovich and Shaul Markovitch. 2006. Overcoming the Brittleness Bottleneck using Wikipedia: Enhancing Text Categorization with Encyclopedic Knowledge, in AAAI, pages 1301-1306, Boston, MA.
  2. Evgeniy Gabrilovich and Shaul Markovitch. 2007. Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis, in Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India, January 2007.
  3. Evgeniy Gabrilovich, 2006. Feature Generation for Textual Information Using World Knowledge, Ph.D. Thesis for The Technion – Israel Institute of Technology, Haifa, Israel, December 2006, 218 pp. See (There is also an informative video at
  4. See also Gabrilovich’s Perl Wikipedia tool, WikiPrep.
  5. Rudiger Gleim, Alexander Mehler and Matthias Dehmer, 2007. Web Corpus Mining by Instance of Wikipedia, in Proc. 2nd Web as Corpus Workshop at EACL 2006. See
  6. Andrew Gregorowicz and Mark A. Kramer, 2006. Mining a Large-Scale Term-Concept Network from Wikipedia, Mitre Technical Report, October 2006. See
  7. Andreas Harth, Hannes Gassert, Ina O’Murchu, John Breslin and Stefan Decker, 2005. WikiOnt: An Ontology for Describing and Exchanging Wiki Articles, presented at Wikimania, Frankfurt, 5th August 2005. See
  8. Martin Hepp, Daniel Bachlechner and Katharina Siorpaes, 2006. Harvesting Wiki Consensus – Using Wikipedia Entries as Ontology Elements, See
  9. Martin Hepp, Katharina Siorpaes, Daniel Bachlechner, 2007. Harvesting Wiki Consensus: Using Wikipedia Entries as Vocabulary for Knowledge Management, in IEEE Internet Computing, Vol. 11, No. 5, pp. 54-65, Sept-Oct 2007. See Also, sample data at
  10. A. Herbelot and Ann Copestake, 2006. Acquiring Ontological Relationships from Wikipedia Using RMRS, in Proc. International Semantic Web Conference 2006 Workshop, Web Content Mining with Human Language Technologies, Athens, GA, 2006. See
  11. Ryuichiro Higashinaka, Kohji Dohsaka and Hideki Isozaki, 2007. Learning to Rank Definitions to Generate Quizzes for Interactive Information Presentation, in Companion Volume to the Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics; see pages 117-120 within
  12. Todd Holloway, Miran Bozicevic, and Katy Börner. 2005. Analyzing and Visualizing the Semantic Coverage of Wikipedia and Its Authors. ArXiv Computer Science e-prints, cs/0512085.
  13. Wei Che Huang, Andrew Trotman, and Shlomo Geva, 2007. Collaborative Knowledge Management: Evaluation of Automated Link Discovery in the Wikipedia, in SIGIR 2007 Workshop on Focused Retrieval, July 27, 2007, Amsterdam, The Netherlands. See

I – K

  1. Jonathan Isbell and Mark H. Butler, 2007. Extracting and Re-using Structured Data from Wikis, Hewlett-Packard Technical Report HPL-2007-182, 14th November, 2007, 22 pp. See
  2. Maciej Janik and Krys Kochut, 2007. Wikipedia in Action: Ontological Knowledge in Text Categorization, University of Georgia, Computer Science Department Technical Report no. UGA-CS-TR-07-001. See
  3. Gjergji Kasneci, Fabian M. Suchanek, Georgiana Ifrim, Maya Ramanath and Gerhard Weikum, 2007. NAGA: Searching and Ranking Knowledge, Technical Report, Max-Planck-Institut f¨ur Informatik, MPI–I–2007–5–001, March 2007, 42 pp.See
  4. Jun’ichi Kazama and Kentaro Torisawa, 2007. Exploiting Wikipedia as External Knowledge for Named Entity Recognition, in Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 698–707, Prague, June 2007.See
  5. Daniel Kinzler, 2005. WikiSense Mining the Wiki, V 1.1, presented at Wikimania 2005, 10 pp. See
  6. A. Krizhanovsky, 2006. Synonym Search in Wikipedia: Synarcher, in 11th International Conference “Speech and Computer” SPECOM’2006. Russia, St. Petersburg, June 25-29, 2006, pp. 474-477. See (also PDF).
  7. Natalia Kozlova, 2005. Automatic Ontology Extraction for Document Classification, a Master’s Thesis for Saarland University, February 2005, 90 pp. See$FILE/Masterarbeit-Kozlova-Nat-2005.pdf. [READ]
  8. M. Krötzsch, D. Vrandečić and M. Völkel, 2005. Wikipedia and the Semantic Web: the Missing Links, at Wikimania 2005.Wikipedia and the Semantic Web: the Missing Links, at Wikimania 2005. See

L – N

  1. Rada Mihalcea and Andras Csomai, 2007. Wikify! Linking Documents to Encyclopedic Knowledge, in Proceedings of the Sixteenth ACM conference on Conference on information and knowledge management CIKM ’07 , November 6-8, 2007, pp. 233-241. See ACM portal retrieval (
  2. Rada Mihalcea, 2007. Using Wikipedia for Automatic Word Sense Disambiguation, in Proceedings of NAACL HLT 2007, pages 196–203, April 2007.See
  3. David Milne, 2007. Computing Semantic Relatedness using Wikipedia Link Structure; see also the Wikipedia Miner Toolkit ( provided by the author
  4. D. Milne, O. Medelyan and I. H. Witten, 2006. Mining Domain-Specific Thesauri from Wikipedia: A Case Study, in Proceedings of the International Conference on Web Intelligence (IEEE/WIC/ACM WI’2006), Hong Kong. Also Olena Medelyan’s home page,
  5. David Milne, Ian H. Witten and David M. Nichols, 2007. A Knowledge-Based Search Engine Powered by Wikipedia, at CIKM ’07.
  6. Lev Muchnik, Royi Itzhack, Sorin Solomon and Yoram Louzoun, 2007. Self-emergence of Knowledge Trees: Extraction of the Wikipedia Hierarchies, in Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), Vol. 76, No. 1.
  7. Nadeau, D., Turney, P., Matwin, S., 2006. Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity, at 19th Canadian Conference on Artificial Intelligence. Québec City, Québec, Canada. June 7, 2006.See Doesn’t specifically use Wikipedia, but techniques are applicable.
  8. Kotaro Nakayama, Takahiro Hara and Shojiro Nishio, 2007. Wikipedia Mining for an Association Web Thesaurus Construction, in Web Information Systems Engineering – WISE 2007, Vol. 4831 (2007), pp. 322-334.
  9. Dat P.T. Nguyen, Yutaka Matsuo and Mitsuru Ishizuka, 2007. Relation Extraction from Wikipedia Using Subtree Mining, from AAAI ’07.

O – P

  1. Yann Ollivier and Pierre Senellar, 2007. Finding Related Pages Using Green Measures: An Illustration with Wikipedia, in Proceedings of the AAAI-07 Conference. See See also for tools and data.
  2. Simon Overell and Stefan Ruger, 2006. Identifying and Grounding Descriptions of Places, in SIGIR Workshop on Geographic Information Retrieval, pages 14–16, 2006.See
  3. Simone Paolo Ponzetto and Michael Strube, 2006. Exploiting Semantic Role Labeling, WordNet and Wikipedia for Coreference Resolution, in NAACL 2006. See
  4. Simone Paolo Ponzetto and Michael Strube, 2007a. Deriving a Large Scale Taxonomy from Wikipedia, in Association for the Advancement of Artificial Intelligence (AAAI2007).
  5. Simone Paolo Ponzetto and Michael Strube, 2007b. Knowledge Derived From Wikipedia For Computing Semantic Relatedness, in Journal of Artificial Intelligence Research 30 (2007) 181-212. See also these PPT slides (in PDF format): Part I, Part II, and References.
  6. Simone Paolo Ponzetto and Michael Strube, 2007c. An API for Measuring the Relatedness of Words in Wikipedia, in Companion Volume to the Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, See pages 49-52 within
  7. Simone Paolo Ponzetto, 2007. Creating a Knowledge Base from a Collaboratively Generated Encyclopedia, in Proceedings of the NAACL-HLT 2007 Doctoral Consortium, pp 9-12, Rochester, NY, April 2007. See

R – S

  1. Tyler Riddle. 2006. Parse::mediawikidump. URL
  2. Ralf Schenkel, Fabian Suchanek and Gjergji Kasneci, 2007. YAWN: A Semantically Annotated Wikipedia XML Corpus, in BTW2007.
  3. Péter Schönhofen, 2006. Identifying Document Topics Using the Wikipedia Category Network, in Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 456-462, 2006.See
  4. Börkur Sigurbjörnsson, Jaap Kamps, and Maarten de Rijke. 2006. Focused Access to Wikipedia. In Proceedings DIR-2006.
  5. Suzette Kruger Stoutenburg, 2007. Research Proposal: Acquiring the Semantic Relationships of Links between Wikipedia Articles, class proposal to
  6. Strube, M. and Ponzetto, S. P. 2006. WikiRelate! Computing Semantic Relatedness Using Wikipedia, in AAAI, pages 1419-1424, Boston, MA.
  7. Fabian M. Suchanek, Gjergji Kasneci and Gerhard Weikum, Yago – A Core of Semantic Knowledge, in 16th international World Wide Web Conference (WWW 2007), Banff, Alberta. See
  8. Fabian M. Suchanek, Gjergji Kasneci and Gerhard Weikum, 2007. Yago: A Large Ontology from Wikipedia and WordNet, in Technical Report, submitted to the Elsevier Journal of Web Semantics 67 pp. See
  9. Fabian M. Suchanek, Georgiana Ifrim and Gerhard Weikum, 2006. Combining Linguistic and Statistical Analysis to Extract Relations from Web Documents, in Knowledge Discovery and Data Mining (KDD 2006). See
  10. S. Suh, H. Halpin and E. Klein, 2006. Extracting Common Sense Knowledge from Wikipedia, in Proc. International Semantic Web Conference 2006 Workshop, Web Content Mining with Human Language Technologies, Athens, GA, 2006. See
  11. Zareen Syed, Tim Finin, and Anupam Joshi, 2008. Wikipedia as an Ontology for Describing Documents, from Proceedings of the Second International Conference on Weblogs and Social Media, AAAI, March 31, 2008. See

T – V

  1. J. A. Thom, J, Pehcevski and A. M. Vercoustre, 2007. Use of Wikipedia Categories in Entity Ranking. in Proceedings of the 12th Australasian Document Computing Symposium, Melbourne, Australia (2007). See
  2. Antonio Toral and Rafael Muñoz, 2007. Towards a Named Entity Wordnet (NEWN), in Proceedings of the 6th International Conference on Recent Advances in Natural Language Processing (RANLP). Borovets (Bulgaria). pp. 604-608 . September 2007.See
  3. Antonio Toral and Rafael Muñoz, 2006. A Proposal to Automatically Build and Maintain Gazetteers for Named Entity Recognition by using Wikipedia, in Workshop on New Text, 11th Conference of the European Chapter of the Association for Computational Linguistics, Trento (Italy). April 2006.See
  4. Anne-Marie Vercoustre, Jovan Pehcevski and James A. Thom, 2007. Using Wikipedia Categories and Links in Entity Ranking, in Pre-proceedings of the Sixth International Workshop of the Initiative for the Evaluation of XML Retrieval (INEX 2007), Dec 17, 2007. See
  5. Anne-Marie Vercoustre, James A. Thom and Jovan Pehcevski, 2008. Entity Ranking in Wikipedia, in SAC’08 March 16-20, 2008, Fortaleza, Ceara, Brazil. See
  6. Max Völkel, Markus Krötzsch, Denny Vrandecic, Heiko Haller and Rudi Studer, 2006. Semantic Wikipedia, in Proceedings of WWW2006, pp 585-594.See
  7. Jakob Voss. 2006. Collaborative Thesaurus Tagging the Wikipedia Way. ArXiv Computer Science e-prints, cs/0604036. See
  8. Denny Vrandecic, Markus Krötzsch and Max Völkel, 2007. Wikipedia and the Semantic Web, Part II, in Phoebe Ayers and Nicholas Boalch, Proceedings of Wikimania 2006 – The Second International Wikimedia Conference, Wikimedia Foundation, Cambridge, MA, USA, August 2007.See

W – Z

  1. Wang Y , Wang H , Zhu H , Yu Y, 2007. Exploit Semantic Information for Category Annotation Recommendation in Wikipedia, in Natural Language Processing and Information Systems (2007), pp. 48-60.
  2. Gang Wang, Yong Yu and Haiping Zhu, 2007. PORE: Positive-Only Relation Extraction from Wikipedia Text, at ISWC 2007, See
  3. Yotaro Watanabe, Masayuki Asahara and Yuji Matsumoto, 2007. A Graph-based Approach to Named Entity Categorization in Wikipedia Using Conditional Random Fields, in EMNLP-CoNLL 2007, 29th June 2007, Prague, Czech. Has a useful CRF overview. See and
  4. Timothy Weale, 2006. Utilizing Wikipedia Categories for Document Classification.
  5. Nicolas Weber and Paul Buitelaar, 2006. Web-based Ontology Learning with ISOLDE. See
  6. Wikify!, online service to automatically turn selected keywords into Wikipedia-style links. See
  7. Wikimedia Foundation. 2006. Wikipedia. URL
  8. Wikipedia, full database. See
  9. Wikipedia, general references. See especially (but only a small subset) for the Semantic Web, RDF, RDF Schema, OWL, SPARQL, GRDDL, W3C, Linked Data, many, many ontologies and controlled vocabularies such as FOAF, SKOS, SIOC, Dublin Core, etc; description logic areas (such as FOL), etc., etc. etc.
  10. Wikipedia, (as a) research source; see
  11. Fei Wu and Daniel S. Weld, 2007. Autonomously Semantifying Wikipedia.
  12. Hugo Zaragoza, Henning Rode, Peter Mika, Jordi Atserias, Massimiliano Ciaramita & Giuseppe Attardi, 2007. Ranking Very Many Typed Entities on Wikipedia, in CIKM ’07: Proceedings of the Sixteenth ACM International Conference on Information and Knowledge Management. See
  13. Torsten Zesch, Iryna Gurevych, Max Mühlhäuser, 2007. Analyzing and Accessing Wikipedia as a Lexical Semantic Resource, and the longer technical report. See
  14. Torsten Zesch and Iryna Gurevych, 2007. Analysis of the Wikipedia Category Graph for NLP Applications, in Proceedings of the TextGraphs-2 Workshop (NAACL-HLT).
  15. Vinko Zlatic, Miran Bozicevic, Hrvoje Stefancic, and Mladen Domazet. 2006. Wikipedias: Collaborative Web-based Encyclopedias as Complex Networks, in Physical Review E, 74:016115.
Posted:February 3, 2008

LinkedData Planet and LDOW Set the Pace for 2008

Linked Data follows recommended practices for identifying, exposing and connecting data on the semantic Web. A robust Linking Open Data (LOD) community has rapidly developed around the practice since its approval as a formal project of the W3C’s Semantic Web Education and Outreach (SWEO) Interest Group in March 2007. Though counts rapidly become dated, today, in less than a year, the size of the Linked Data on the Web exceeds several billion RDF triples.

This foundation of interlinkable data comes from the highest value reference sources available, and includes most notable place, people, event, book, music, cultural, language and government entities. The following official figure of the LOD community, maintained by one of its founders, Richard Cyganiak, is updated frequently (click on the figure below to get the most recent interactive version), and shows well the breadth of this data value:

Linked Data Web

It would be putting it mildly to say that the LOD project has been a roaring success. New initiatives like the Billion Triple Challenge will continue to rapidly push forward the size and frontiers of Linked Data. We also have two signal events coming up in 2008 that demonstrate just how much Linked Data is coming of age.

LinkedData Planet Conference

LinkedData Planet Conference

The newly announced LinkedData Planet Conference and Expo being held in New York City on June 17th and 18th is notable for a number of reasons (besides Tim Berners-Lee being the special keynote speaker).

First, the conference represents the first direct exposure of Linked Data to the business and enterprise community. For years, the semantic Web community has largely been an academic one with its own set of meetings and venues. That began to change with the recent series on the Semantic Technology Conferences, held as usual in San Jose in May (this year’s is May 18-22). That meeting in 2007 drew more than 800 attendees and marked the first time that significant presence from the academic and research communities occurred.

Though valuable and chock-a-block with enterprise case studies, the Semantic Technology conference also is challenged by the amorphous understanding of what is the “semantic Web”. Reaching common understandings and getting cross-fertilization between the business and research communities can be a challenge.

Second, the LinkedData Planet Conference is occurring in NYC with an anticipated strong participation from East Coast financial interests. Like the Semantic Technology Conferences, it is important that the nascent technologies and applications supporting Linked Data receive the venture and funding attention they deserve.

Third, Jupitermedia is the event manager for the conference. Jupitermedia has a long history in producing quality industry events such as Internet World, Search Engine Strategies and ISPcon. Its meetings typically blend excellent content with strong community outreach and participation.

But, last, and to my mind most important, the very topic of Linked Data is very focused and pragmatic. There are real methods, real techniques and real applications available now to take advantage of Linked Data now. The business community need not wait on full semantics and total data exposure and automation in order to receive real value today.

Last July I wrote a piece entitled, More Structure, More Terminology and (hopefully) More Clarity. It, and related posts on the structured Web, had as its thesis that the Web was naturally evolving from a document-centric basis to a “Web of Data”. We already have much structured data available and the means through RDFizers and other techniques to convert that structure to Linked Data. Linked Data thus represented a very doable and pragmatic way station on the road to the semantic Web. It is a journey we can take today; indeed, many already are as the growth figures noted attest.

Here is a repeat of the diagram I used last July to make that argument (now highlighting the Linked Data phase in red):

Transition in Web Structure
Document Web Structured Web
Semantic Web
    Linked Data  

  • Document-centric
  • Document resources
  • Unstructured data and semi-structured data
  • HTML
  • URL-centric
  • circa 1993
  • Data-centric
  • Structured data
  • Semi-structured data and structured data
  • XML, JSON, RDF, etc
  • URI-centric
  • circa 2003
  • Data-centric
  • Linked data
  • Semi-structured data and structured data
  • RDF, RDF-S
  • URI-centric
  • circa 2007
  • Data-centric
  • Linked data
  • Semi-structured data and structured data
  • URI-centric
  • circa ???

Hopefully, the LinkedData Planet Conference will act as a similar catalyst within the business community as the LOD project has been in the research one. And, hopefully, academia, research, venture and business interests can all come together over these two days to exploit the Linked Data value so readily at hand.

LDOW at WWW2008

Another signal event coming up is the Linked Data on the Web (LDOW) workshop at the 17th International World Wide Web Conference (WWW2008) in Bejing. LDOW is a full-day session involving a mix of papers and demos on April 22. A significant roster of very interesting submissions has been made (disclosure: I am both on the program committee and a submitting author).

Linked Data was arguably the highlight at WWW2007 in Banff, and LDOW will certainly show just how far this approach has come in one short year. LDOW will likely provide a preview of many of the applications and approaches that will receive fuller attention at the LinkedData Planet Conference two months later.

* * * *

It is exciting to see Linked Data emerging as today’s pragmatic focus for bringing further structure, connections and semantics to the Web. For those of you new to the concept, I encourage you to become active and involved in 2008. And, for those of you already active, I look forward personally to working with you further in the coming year.

Posted by AI3's author, Mike Bergman Posted on February 3, 2008 at 1:18 pm in Semantic Web, Structured Web | Comments (4)
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