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Ranking entities for web queries through text and knowledge
Schuhmacher, Michael
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Dietz, Laura
;
Ponzetto, Simone Paolo
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DOI:
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https://doi.org/10.1145/2806416.2806480
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URL:
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https://madoc.bib.uni-mannheim.de/39818
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Additional URL:
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http://dl.acm.org/citation.cfm?id=2806480
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URN:
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urn:nbn:de:bsz:180-madoc-398182
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Document Type:
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Conference or workshop publication
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Year of publication:
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2015
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Book title:
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Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM 2015, Melbourne, VIC, Australia, October 19 - 23, 2015
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Page range:
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1461-1470
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Date of the conference:
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October 19-23, 2015
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Publisher:
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Bailey, James
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Place of publication:
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New York, NY [u.a.]
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Publishing house:
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ACM
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ISBN:
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978-1-4503-3794-6
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Publication language:
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English
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Institution:
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School of Business Informatics and Mathematics > Semantic Web (Juniorprofessur) (Ponzetto 2013-2015)
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Subject:
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004 Computer science, internet
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Abstract:
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When humans explain complex topics, they naturally talk about involved entities, such as people, locations, or events. In this paper, we
aim at automating this process by retrieving and ranking entities that
are relevant to understand free-text web-style queries like Argentine British relations, which typically demand a set of heterogeneous
entities with no specific target type like, for instance,
Falklands-War or
Margaret_Thatcher, as answer. Standard approaches to
entity retrieval rely purely on features from the knowledge base.
We approach the problem from the opposite direction, namely by
analyzing web documents that are found to be query-relevant. Our
approach hinges on entity linking technology that identifies entity
mentions and links them to a knowledge base like Wikipedia. We
use a learning-to-rank approach and study different features that
use documents, entity mentions, and knowledge base entities – thus
bridging document and entity retrieval. Since established bench-
marks for this problem do not exist, we use TREC test collections
for document ranking and collect custom relevance judgments for
entities. Experiments on TREC Robust04 and TREC Web13/14 data
show that: i) single entity features, like the frequency of occurrence
within the top-ranked documents, or the query retrieval score against
a knowledge base, perform generally well; ii) the best overall per-
formance is achieved when combining different features that relate
an entity to the query, its document mentions, and its knowledge
base representation.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 | Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt. |
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