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.
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