Difference between revisions of "Related Entity Finding Using Semantic Clustering based on Wikipedia Categories"
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| − | '''Related Entity Finding Using Semantic Clustering based on Wikipedia Categories''' - scientific work related to Wikipedia quality published in 2013, written by Georgios Stratogiannis, Georgios Siolas and Andreas Stafylopatis. | + | '''Related Entity Finding Using Semantic Clustering based on Wikipedia Categories''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Georgios Stratogiannis]], [[Georgios Siolas]] and [[Andreas Stafylopatis]]. |
== Overview == | == Overview == | ||
| − | Authors present a system that performs Related Entity Finding, that is, Question Answering that exploits Semantic Information from the WWW and returns URIs as answers. Authors system uses a search engine to gather all candidate answer entities and then a linear combination of Information Retrieval measures to choose the most relevant. For each one authors look up its Wikipedia page and construct a novel vector representation based on the tokenization of the Wikipedia category names. This novel representation gives system the ability to compute a measure of semantic relatedness between entities, even if the entities do not share any common category. Authors use this property to perform a semantic clustering of the candidate entities and show that the biggest cluster contains entities that are closely related semantically and can be considered as answers to the query. Performance measured on 20 topics from the 2009 TREC Related Entity Finding task shows competitive results. | + | Authors present a system that performs Related Entity Finding, that is, Question Answering that exploits Semantic Information from the WWW and returns URIs as answers. Authors system uses a search engine to gather all candidate answer entities and then a linear combination of Information Retrieval [[measures]] to choose the most relevant. For each one authors look up its [[Wikipedia]] page and construct a novel vector representation based on the tokenization of the Wikipedia category names. This novel representation gives system the ability to compute a measure of semantic [[relatedness]] between entities, even if the entities do not share any common category. Authors use this property to perform a semantic clustering of the candidate entities and show that the biggest cluster contains entities that are closely related semantically and can be considered as answers to the query. Performance measured on 20 topics from the 2009 TREC Related Entity Finding task shows competitive results. |
Revision as of 09:30, 3 July 2020
Related Entity Finding Using Semantic Clustering based on Wikipedia Categories - scientific work related to Wikipedia quality published in 2013, written by Georgios Stratogiannis, Georgios Siolas and Andreas Stafylopatis.
Overview
Authors present a system that performs Related Entity Finding, that is, Question Answering that exploits Semantic Information from the WWW and returns URIs as answers. Authors system uses a search engine to gather all candidate answer entities and then a linear combination of Information Retrieval measures to choose the most relevant. For each one authors look up its Wikipedia page and construct a novel vector representation based on the tokenization of the Wikipedia category names. This novel representation gives system the ability to compute a measure of semantic relatedness between entities, even if the entities do not share any common category. Authors use this property to perform a semantic clustering of the candidate entities and show that the biggest cluster contains entities that are closely related semantically and can be considered as answers to the query. Performance measured on 20 topics from the 2009 TREC Related Entity Finding task shows competitive results.
