2015/04/27 by Daniel Hienert, Hienert, Daniel, Dennis Wegener +3
Computer Science · Social Sciences · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Natural Language Processing Techniques #Topic Modeling #Wikis in Education and Collaboration #cs.CL #cs.IR
paper · pdf · doi:10.48550/arxiv.1504.07071
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. Würzburg: Ergon-Verl
arxiv created 2015/04/27 · openalex publication_date 2015/04/27 · arxiv updated 2015/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.