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KORE: Keyphrase Overlap Relatedness for Entity Disambiguation

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44631

Hoffart,  Johannes
Databases and Information Systems, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45462

Seufert,  Stephan
Databases and Information Systems, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons103083

Nguyen,  Dat Ba
Databases and Information Systems, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45609

Theobald,  Martin
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Zitation

Hoffart, J., Seufert, S., Nguyen, D. B., Theobald, M., & Weikum, G. (2012). KORE: Keyphrase Overlap Relatedness for Entity Disambiguation. In X.-W. Chen, G. Lebanon, H. Wang, & M. J. Zaki (Eds.), CIKM'12 (pp. 545-554). New York, NY: ACM.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0014-59A6-0
Zusammenfassung
Measuring the semantic relatedness between two entities is the basis for numerous tasks in IR, NLP, and Web-based knowledge extraction. This paper focuses on disambiguating names in a Web or text document by jointly mapping all names onto semantically related entities registered in a knowledge base. To this end, we have developed a novel notion of semantic relatedness between two entities represented as sets of weighted (multi-word) keyphrases, with consideration of partially overlapping phrases. This measure improves the quality of prior link-based models, and also eliminates the need for (usually Wikipedia-centric) explicit interlinkage between entities. Thus, our method is more versatile and can cope with long-tail and newly emerging entities that have few or no links associated with them. For efficiency, we have developed approximation techniques based on min-hash sketches and locality-sensitive hashing. Our experiments on semantic relatedness and on named entity disambiguation demonstrate the superiority of our method compared to state-of-the-art baselines.