Difference between revisions of "Leveraging Fine-Grained Wikipedia Categories for Entity Search"

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{{Infobox work
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| title = Leveraging Fine-Grained Wikipedia Categories for Entity Search
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| date = 2018
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| authors = [[Denghao Ma]]<br />[[Yueguo Chen]]<br />[[Kevin Chen Chuan Chang]]<br />[[Xiaoyong Du]]
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| doi = 10.1145/3178876.3186074
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| link = https://dl.acm.org/citation.cfm?doid=3178876.3186074
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}}
 
'''Leveraging Fine-Grained Wikipedia Categories for Entity Search''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Denghao Ma]], [[Yueguo Chen]], [[Kevin Chen Chuan Chang]] and [[Xiaoyong Du]].
 
'''Leveraging Fine-Grained Wikipedia Categories for Entity Search''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Denghao Ma]], [[Yueguo Chen]], [[Kevin Chen Chuan Chang]] and [[Xiaoyong Du]].
  
 
== Overview ==
 
== Overview ==
 
Ad-hoc entity search, which is to retrieve a ranked list of relevant entities in response to a query of natural language question, has been widely studied. It has been shown that category matching of entities, especially when matching to fine-grained entity [[categories]], is critical to the performance of entity search. However, the potentials of fine-grained [[Wikipedia categories]], has not been well exploited by existing studies. Based on the observation of how people describe entities of a specific type, authors propose a headword-and-modifier model to deeply interpret both queries and fine-grained entity categories. Probabilistic generative models are designed to effectively estimate the relevance of headwords and modifiers as a pattern-based matching problem, taking the [[Wikipedia]] type taxonomy as an important input to address the ad-hoc representations of concepts/entities in queries. Extensive experimental results on three widely-used test sets: INEX-XER 2009, SemSearch-LS and TREC-Entity, show that method achieves a significant improvement of the entity search performance over the state-of-the-art methods.
 
Ad-hoc entity search, which is to retrieve a ranked list of relevant entities in response to a query of natural language question, has been widely studied. It has been shown that category matching of entities, especially when matching to fine-grained entity [[categories]], is critical to the performance of entity search. However, the potentials of fine-grained [[Wikipedia categories]], has not been well exploited by existing studies. Based on the observation of how people describe entities of a specific type, authors propose a headword-and-modifier model to deeply interpret both queries and fine-grained entity categories. Probabilistic generative models are designed to effectively estimate the relevance of headwords and modifiers as a pattern-based matching problem, taking the [[Wikipedia]] type taxonomy as an important input to address the ad-hoc representations of concepts/entities in queries. Extensive experimental results on three widely-used test sets: INEX-XER 2009, SemSearch-LS and TREC-Entity, show that method achieves a significant improvement of the entity search performance over the state-of-the-art methods.

Revision as of 06:04, 19 February 2021


Leveraging Fine-Grained Wikipedia Categories for Entity Search
Authors
Denghao Ma
Yueguo Chen
Kevin Chen Chuan Chang
Xiaoyong Du
Publication date
2018
DOI
10.1145/3178876.3186074
Links
Original

Leveraging Fine-Grained Wikipedia Categories for Entity Search - scientific work related to Wikipedia quality published in 2018, written by Denghao Ma, Yueguo Chen, Kevin Chen Chuan Chang and Xiaoyong Du.

Overview

Ad-hoc entity search, which is to retrieve a ranked list of relevant entities in response to a query of natural language question, has been widely studied. It has been shown that category matching of entities, especially when matching to fine-grained entity categories, is critical to the performance of entity search. However, the potentials of fine-grained Wikipedia categories, has not been well exploited by existing studies. Based on the observation of how people describe entities of a specific type, authors propose a headword-and-modifier model to deeply interpret both queries and fine-grained entity categories. Probabilistic generative models are designed to effectively estimate the relevance of headwords and modifiers as a pattern-based matching problem, taking the Wikipedia type taxonomy as an important input to address the ad-hoc representations of concepts/entities in queries. Extensive experimental results on three widely-used test sets: INEX-XER 2009, SemSearch-LS and TREC-Entity, show that method achieves a significant improvement of the entity search performance over the state-of-the-art methods.