Discovering missing semantic relations between entities in Wikipedia
|Discovering missing semantic relations between entities in Wikipedia|
|Author(s)||Xu M., Wang Z., Bie R., Li J., Zheng C., Ke W., Zhou M.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Infobox, Linked Data, Wikipedia (Extra: Attribute values, Baseline methods, Infobox, Linked datum, Precision and recall, Semantic relations, Structured information, Wikipedia, Data handling, Hypertext systems, Semantic Web, Websites)|
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Discovering missing semantic relations between entities in Wikipedia is a 2013 conference paper written in English by Xu M., Wang Z., Bie R., Li J., Zheng C., Ke W., Zhou M. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Wikipedia's infoboxes contain rich structured information of various entities, which have been explored by the DBpedia project to generate large scale Linked Data sets. Among all the infobox attributes, those attributes having hyperlinks in its values identify semantic relations between entities, which are important for creating RDF links between DBpedia's instances. However, quite a few hyperlinks have not been anotated by editors in infoboxes, which causes lots of relations between entities being missing in Wikipedia. In this paper, we propose an approach for automatically discovering the missing entity links in Wikipedia's infoboxes, so that the missing semantic relations between entities can be established. Our approach first identifies entity mentions in the given infoboxes, and then computes several features to estimate the possibilities that a given attribute value might link to a candidate entity. A learning model is used to obtain the weights of different features, and predict the destination entity for each attribute value. We evaluated our approach on the English Wikipedia data, the experimental results show that our approach can effectively find the missing relations between entities, and it significantly outperforms the baseline methods in terms of both precision and recall.
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