Exploring semantically-related concepts from Wikipedia: the case of SeRE
Abstract
In this paper we present our web application SeRE designed to explore semantically related concepts. Wikipedia and DBpedia are rich data sources to extract related entities for a given topic, like in- and out-links, broader and narrower terms, categorisation information etc. We use the Wikipedia full text body to compute the semantic relatedness for extracted terms, which results in a list of entities that are most relevant for a topic. For any given query, the user interface of SeRE visualizes these related concepts, ordered by semantic relatedness; with snippets from Wikipedia articles that explain the connection between those two entities. In a user study we examine how SeRE can be used to find important entities and their relationships for a given topic and to answer the question of how the classification system can be used for filtering.
Cite
@article{arxiv.1504.07071,
title = {Exploring semantically-related concepts from Wikipedia: the case of SeRE},
author = {Daniel Hienert and Dennis Wegener and Siegfried Schomisch},
journal= {arXiv preprint arXiv:1504.07071},
year = {2015}
}
Comments
In Classification & visualization : interfaces to knowledge ; proceedings of the International UDC Seminar 24 - 25 October 2013, The Hague, The Netherlands, edited by Aida Slavic, Almila Akdag Salah, and Sylvie Davies, 153-165. W\"urzburg: Ergon-Verl