2008 LearningToLinkWithWikipedia
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- (Milne & Witten, 2008a) ⇒ David N. Milne, and Ian H. Witten. (2008). “Learning to Link with Wikipedia.” In: Proceeding of the 17th ACM Conference on Information and Knowledge Management, (CIKM 2008). doi:10.1145/1458082.1458150
Subject Headings: Wikipedia-based Term Mention Linking Algorithm, Wikipedia-based Term Mention Recognition Algorithm, Supervised Concept Mention Linking Algorithm.
Notes
- Its Video Lecture is available http://videolectures.net/cikm08_milne_ltlww/
- Its Presentation Slides are available at http://carbon.videolectures.net/2008/active/cikm08_napa_valley/milne_ltlww/cikm08_milne_ltlww_01.ppt
- A demo version is available online (Wikipedia Miner Wikify Service) is available at http://wdm.cs.waikato.ac.nz:8080/service?task=wikify
- It makes use of the Topic Similarity Measure proposed in (Milne & Witten, 2008b), which in turn is based on the Normalized Google Distance Measure described in (Cilibrasi & Vitanyi, 2007).
- The Semantic Similarity Measure has Similarity Values that range from Zero (highest similarity) to Infinity (highest dissimilarity).
- Evaluation Data: Milne & Witten, 2008a - Data.
- It is based on 50 Associated Press Newswire articles from the AQUAINT Corpus.
- Its Ground Truth Data can be found at http://www.nzdl.org/wikification/data/wikifiedStories.zip
Cited By
2010
- (Melli & Ester, 2010) ⇒ Gabor Melli, Martin Ester. (2010). “Supervised Identification and Linking of Concept Mentions to a Domain-Specific Ontology.” In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM 2010). doi:10.1145/1871437.1871712
2009
- (Medelyan et al., 2009) ⇒ Olena Medelyan, David Milne, Catherine Legg, and Ian H. Witten. (2009). “Mining Meaning from Wikipedia.” In: International Journal of Human-Computer Studies, 67(9). doi:10.1016/j.ijhcs.2009.05.004
- (Kulkarni et al., 2009) ⇒ Sayali Kulkarni, Amit Singh, Ganesh Ramakrishnan, and Soumen Chakrabarti. (2009). “Collective Annotation of Wikipedia Entities in Web Text.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557073
- QUOTE: A limited form of collective disambiguation proposed by Milne and Witten [15 yields considerable improvement beyond Wikify!. M&W propose a relatedness score r(γ, γ) between two entities. From the set of all spots S0, they identify the subset S! of so-called context spots that can refer to exactly one entity each (let this entity set be Γ!). They define a notion of coherence of a context spot γ In Γ! based on its relatedness to other context spots. For an ambiguous spot [math]\displaystyle{ s }[/math] NotIn S!, the score of a candidate entity γ NotIn Γ! is strongly influenced by its mention-independent prior probability Pr0(γ|s), its relatedness to context entities on the page, their coherence, and a measure of overall quality of context entities. M&W also propose a link detector (a function similar to keyword extraction in Wikify!) that, like SemTag and Wikify!, sacrifices recall for high precision. For the spots picked by M&W for labeling, even random disambiguation achieves an F1 score of 0.53.
Quotes
Keywords:
Abstract
This paper describes how to automatically cross-reference documents with Wikipedia: the largest knowledge base ever known. It explains how machine learning can be used to identify significant terms within unstructured text, and enrich it with links to the appropriate Wikipedia articles. The resulting link detector and disambiguator performs very well, with recall and precision of almost 75%. This performance is constant whether the system is evaluated on Wikipedia articles or “real world” documents.
This work has implications far beyond enriching documents with explanatory links. It can provide structured knowledge about any unstructured fragment of text. Any task that is currently addressed with bags of words - indexing, clustering, retrieval, and summarization to name a few - could use the techniques described here to draw on a vast network of concepts and semantics.
References
- 1. (Auer et al., 2007) ⇒ Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. (2007). “DBpedia: A Nucleus for a Web of Open Data.” In: Proceedings of the 6th International Semantic Web Conference.
- 2. Somnath Banerjee, Krishnan Ramanathan, Ajay Gupta, Clustering short texts using wikipedia, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands doi:10.1145/1277741.1277909.
- 3. Jeff Barr, Luis Felipe Cabrera, AI gets a brain, Queue, v.4 n.4, May 2006 doi:10.1145/1142055.1142067
- 4. C. David, L. Giroux, S. Bertrand-Gastaldy, and D. Lanteigne (1995) Indexing as problem solving: A cognitive approach to consistency. In: Proceedings of the ASIS Annual Meeting, Medford, NJ, pp. 49--55.
- 5. S. Dolan. (2008) Six Degrees of Wikipedia. Retrieved June 2008 from www.netsoc.tcd.ie/~mu/wiki/.
- 6. (Drenner et al., 2006) ⇒ Sara Drenner, Max Harper, Dan Frankowski, John Riedl, and Loren Terveen. (2006). “Insert Movie Reference Here: A system to bridge conversation and item-oriented web sites.” In: Proceedings of the SIGCHI conference on Human Factors in computing systems (SIGCHI 2006) doi:10.1145/1124772.1124914
- 7. (Gabrilovich & Markovitch, 2006) ⇒ Evgeniy Gabrilovich, and Shaul Markovitch. (2006). “Overcoming the Brittleness Bottleneck Using Wikipedia: Enhancing text categorization with encyclopedic knowledge.” In: Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI 2006).
- 8. J. Howe. (2006) The Rise of Crowdsourcing. In Wired Magazine 14(6).
- 9. A. Lih. (2004). Wikipedia as Participatory Journalism: Reliable Sources? Metrics for evaluating collaborative media as a news resource. In: Proceedings of the 5th International Symposium on Online Journalism, Austin, Texas.
- 10. M. E. Maron. (1977) On indexing, retrieval and the meaning of about. In Journal of the American Society for Information Science 28(1), pp. 38--43
- 11. (Medelyan, 2008) ⇒ Olena Medelyan, Ian H. Witten, and David N. Milne. (2008). “Topic Indexing with Wikipedia.” In: Proceedings of the AAAI 2008 Workshop on Wikipedia and Artificial Intelligence (WIKIAI 2008).
- 12. Rada Mihalcea, Andras Csomai, Wikify!: linking documents to encyclopedic knowledge, Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, November 06-10, 2007, Lisbon, Portugal doi:10.1145/1321440.1321475.
- 13. David N. Milne, Ian H. Witten, David M. Nichols, A knowledge-based search engine powered by wikipedia, Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, November 06-10, 2007, Lisbon, Portugal doi:10.1145/1321440.1321504
- 14. David N. Milne, and Witten, I. H. (2008) An effective, low-cost measure of semantic relatedness obtained from Wikipedia links. In: Proceedings of the AAAI 2008 Workshop on Wikipedia and Artificial Intelligence (WIKIAI 2008), Chicago, IL.
- 15. W. Mossberg. (2001) New Windows XP Feature Can Re-Edit Others' Sites. The Wall Street Journal, June 2001
- 16. S. P. Ponzetto, and M. Strube. (2007) Deriving a Large Scale Taxonomy from Wikipedia. In: Proceedings of the 22st National Conference on Artificial Intelligence (AAAI'07), Vancouver, British Columbia, pp. 1440--1445.
- 17. J. Ross Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1993.
- 18. Fabian M. Suchanek, Gjergji Kasneci, Gerhard Weikum, Yago: a core of semantic knowledge, Proceedings of the 16th International Conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada doi:10.1145/1242572.1242667.
- 19. Max Völkel, Markus Krötzsch, Denny Vrandecic, Heiko Haller, Rudi Studer, Semantic Wikipedia, Proceedings of the 15th International Conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland doi:10.1145/1135777.1135863,