Enriching Topic Models with DBpedia

  • Alexandru TodorEmail author
  • Wojciech Lukasiewicz
  • Tara Athan
  • Adrian Paschke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10033)


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.


Topic Model Latent Dirichlet Allocation Feature Combination Cluster Accuracy Unique Entity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work has been partially supported by the “InnoProfileTransfer Corporate Smart Content" project funded by the German Federal Ministry of Education and Research (BMBF) and the BMBF Innovation Initiative for the New German Länder - Entrepreneurial Regions.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Alexandru Todor
    • 1
    Email author
  • Wojciech Lukasiewicz
    • 1
  • Tara Athan
    • 1
  • Adrian Paschke
    • 1
  1. 1.Institute for Computer ScienceFreie Universität Berlin, AG Corporate Semantic WebBerlinGermany

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