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2016 | OriginalPaper | Chapter

Enriching Topic Models with DBpedia

Authors : Alexandru Todor, Wojciech Lukasiewicz, Tara Athan, Adrian Paschke

Published in: On the Move to Meaningful Internet Systems: OTM 2016 Conferences

Publisher: Springer International Publishing

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Abstract

Traditional Topic Modeling approaches only consider the words in the document. By using an entity-topic modeling approach and including background knowledge about the entities such as the occupation of persons, the location of organizations, the band of a musician etc., we can better cluster related documents together, and produce semantic topic models that can be represented in a knowledge base. In our approach we first reduce the text documents to a set of entities and then enrich this set with background knowledge from DBpedia. Topic modeling is performed on the enriched set of entities and various feature combinations are evaluated in order to determine the combination that achieves the best classification precision or perplexity compared to using word-based topic models alone.

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previous chapter LD-LEx: Linked Dataset Link Extractor (Short Paper)

next chapter FuhSen: A Federated Hybrid Search Engine for Building a Knowledge Graph On-Demand (Short Paper)

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Title: Enriching Topic Models with DBpedia
Authors: Alexandru Todor
Wojciech Lukasiewicz
Tara Athan
Adrian Paschke
Publisher: Springer International Publishing
Book: On the Move to Meaningful Internet Systems: OTM 2016 Conferences
Print ISBN: 978-3-319-48471-6

Electronic ISBN: 978-3-319-48472-3

Copyright Year: 2016
DOI: https://doi.org/10.1007/978-3-319-48472-3_46

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