Ranking Very Many Typed Entities on Wikipedia

Hugo Zaragoza; H. Rode; Peter Mika; Jordi Atserias; Massimiliano Ciaramita; Guiseppe Attardi

doi:10.1145/1321440.1321599

Ranking Very Many Typed Entities on Wikipedia

Hugo Zaragoza, H. Rode, Peter Mika, Jordi Atserias, Massimiliano Ciaramita, Guiseppe Attardi

Databases (Former)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

57 Citations (Scopus)

Abstract

We discuss the problem of ranking very many entities of different types. In particular we deal with a heterogeneous set of types, some being very generic and some very specific. We discuss two approaches for this problem: i) exploiting the entity containment graph and ii) using a Web search engine to compute entity relevance. We evaluate these approaches on the real task of ranking Wikipedia entities typed with a state-of-the-art named-entity tagger. Results show that both approaches can greatly increase the performance of methods based only on passage retrieval.

Original language	Undefined
Title of host publication	Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, CIKM '07
Place of Publication	New York, NY, USA
Publisher	ACM Press
Pages	1015-1018
Number of pages	4
ISBN (Print)	978-1-59593-803-9
DOIs	https://doi.org/10.1145/1321440.1321599
Publication status	Published - Nov 2007
Event	16th ACM conference on Conference on Information and Knowledge Management, CIKM 2007 - Lisbon, Portugal Duration: 6 Nov 2007 → 9 Nov 2007 Conference number: 16

Publication series

Name
Publisher	ACM Press
Number	FS-07-05

Conference

Conference	16th ACM conference on Conference on Information and Knowledge Management, CIKM 2007
Abbreviated title	CIKM
Country/Territory	Portugal
City	Lisbon
Period	6/11/07 → 9/11/07

Keywords

EWI-11439
CR-H.3.3
METIS-245789
IR-62024
DB-XMLIR: XML INFORMATION RETRIEVAL

Access to Document

10.1145/1321440.1321599

Cite this

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title = "Ranking Very Many Typed Entities on Wikipedia",

abstract = "We discuss the problem of ranking very many entities of different types. In particular we deal with a heterogeneous set of types, some being very generic and some very specific. We discuss two approaches for this problem: i) exploiting the entity containment graph and ii) using a Web search engine to compute entity relevance. We evaluate these approaches on the real task of ranking Wikipedia entities typed with a state-of-the-art named-entity tagger. Results show that both approaches can greatly increase the performance of methods based only on passage retrieval.",

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author = "Hugo Zaragoza and H. Rode and Peter Mika and Jordi Atserias and Massimiliano Ciaramita and Guiseppe Attardi",

note = "10.1145/1321440.1321599 ; null ; Conference date: 06-11-2007 Through 09-11-2007",

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Zaragoza, H, Rode, H, Mika, P, Atserias, J, Ciaramita, M & Attardi, G 2007, Ranking Very Many Typed Entities on Wikipedia. in Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, CIKM '07., 10.1145/1321440.1321599, ACM Press, New York, NY, USA, pp. 1015-1018, 16th ACM conference on Conference on Information and Knowledge Management, CIKM 2007, Lisbon, Portugal, 6/11/07. https://doi.org/10.1145/1321440.1321599

Ranking Very Many Typed Entities on Wikipedia. / Zaragoza, Hugo; Rode, H.; Mika, Peter et al.

Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, CIKM '07. New York, NY, USA : ACM Press, 2007. p. 1015-1018 10.1145/1321440.1321599.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Attardi, Guiseppe

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AB - We discuss the problem of ranking very many entities of different types. In particular we deal with a heterogeneous set of types, some being very generic and some very specific. We discuss two approaches for this problem: i) exploiting the entity containment graph and ii) using a Web search engine to compute entity relevance. We evaluate these approaches on the real task of ranking Wikipedia entities typed with a state-of-the-art named-entity tagger. Results show that both approaches can greatly increase the performance of methods based only on passage retrieval.

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