Semantic question answering using Wikipedia categories clustering
|Semantic question answering using Wikipedia categories clustering|
|Author(s)||Stratogiannis G., Siolas G., Stafylopatis A.|
|Published in||International Journal on Artificial Intelligence Tools|
|Keyword(s)||Question answering, semantic clustering, Wikipedia category vector representation (Extra: Search engines, World Wide Web, Intelligent clustering, Linear combinations, Noun phrase extractors, Question Answering, Related entity findings, Semantic clustering, Semantic information, Vector representations, Semantics)|
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Semantic question answering using Wikipedia categories clustering is a 2014 journal article written in English by Stratogiannis G., Siolas G., Stafylopatis A. and published in International Journal on Artificial Intelligence Tools.
We describe a system that performs semantic Question Answering based on the combination of classic Information Retrieval methods with semantic ones. First, we use a search engine to gather web pages and then apply a noun phrase extractor to extract all the candidate answer entities from them. Candidate entities are ranked using a linear combination of two IR measures to pick the most relevant ones. For each one of the top ranked candidate entities we find the corresponding Wikipedia page. We then propose a novel way to exploit Semantic Information contained in the structure of Wikipedia. A vector is built for every entity from Wikipedia category names by splitting and lemmatizing the words that form them. These vectors maintain Semantic Information in the sense that we are given the ability to measure semantic closeness between the entities. Based on this, we apply an intelligent clustering method to the candidate entities and show that candidate entities in the biggest cluster are the most semantically related to the ideal answers to the query. Results on the topics of the TREC 2009 Related Entity Finding task dataset show promising performance.
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