Comparison of Word Embeddings from Different Knowledge Graphs

  • Kiril Simov
  • Petya Osenova
  • Alexander Popov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10318)


The paper focuses on the manipulation of a WordNet-based knowledge graph by adding, changing and combining various semantic relations. This is done in the context of measuring similarity and relatedness between words, based on word embedding representations trained on a pseudo corpus generated from the knowledge graph. The UKB tool is used for generating pseudo corpora that are then used for learning word embeddings. The results from the performed experiments show that the addition of more relations generally improves performance along both dimensions – similarity and relatedness. In line with previous research, our survey confirms that paradigmatic relations predominantly improve similarity, while syntagmatic relations benefit relatedness scores.


Knowledge-based word embedding Semantic relations Similarity Association 



This research has received partial support by the grant 02/12—Deep Models of Semantic Knowledge (DemoSem), funded by the Bulgarian National Science Fund in 2017–2019. We are grateful to the anonymous reviewers for their remarks, comments, and suggestions. All errors remain our own responsibility.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Information and Communication Technologies, BASSofiaBulgaria

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