Design and Use of a Semantic Similarity Measure for Interoperability Among Agents

  • Johannes Fähndrich
  • Sabine Weber
  • Sebastian Ahrndt
Conference paper

DOI: 10.1007/978-3-319-45889-2_4

Volume 9872 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Fähndrich J., Weber S., Ahrndt S. (2016) Design and Use of a Semantic Similarity Measure for Interoperability Among Agents. In: Klusch M., Unland R., Shehory O., Pokahr A., Ahrndt S. (eds) Multiagent System Technologies. MATES 2016. Lecture Notes in Computer Science, vol 9872. Springer, Cham

Abstract

The capability to identify the sense of polysemic words, i.e. words that have multiple meanings, is an essential part of intelligent systems, e.g. when updating an agent’s beliefs during conversations. This process is also called Word Sense Disambiguation and is approached by applying semantic similarity measures. Within this work, we present an algorithm to create such a semantic similarity measure using marker passing, that: (1) generates a semantic network out of a concepts used e.g. in semantic service descriptions, (2) sends markers through the networks to tag sub-graphs that are of relevance, and (3) uses these markers to create a semantic similarity measure. We will discuss the properties of the algorithm, elaborate its performance, and discuss the lifted properties for the algorithm to be used in WSD. To evaluate our approach, we compare it to state of the art measures using the Rubinstein1965 dataset. It is shown, that our approach outperforms these state of the art measures.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Johannes Fähndrich
    • 1
  • Sabine Weber
    • 1
  • Sebastian Ahrndt
    • 1
  1. 1.DAI-Laboratory, Department of Electrical Engineering and Computer ScienceTechnische Universität BerlinBerlinGermany