Extracting Semantic Information for e-Commerce

  • Bruno Charron
  • Yu Hirate
  • David Purcell
  • Martin Rezk
Conference paper

DOI: 10.1007/978-3-319-46547-0_27

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9982)
Cite this paper as:
Charron B., Hirate Y., Purcell D., Rezk M. (2016) Extracting Semantic Information for e-Commerce. In: Groth P. et al. (eds) The Semantic Web – ISWC 2016. ISWC 2016. Lecture Notes in Computer Science, vol 9982. Springer, Cham

Abstract

Rakuten Ichiba uses a taxonomy to organize the items it sells. Currently, the taxonomy classes that are relevant in terms of profit generation and difficulty of exploration are being manually extended with data properties deemed helpful to create pages that improve the user search experience and ultimately the conversion rate. In this paper we present a scalable approach that aims to automate this process, automatically selecting the relevant and semantically homogenous subtrees in the taxonomy, extracting from semi-structured text in items descriptions a core set of properties and a popular subset of their ranges, then extending the covered range using relational similarities in free text. Additionally, our process automatically tags the items with the new semantic information and exposes them as RDF triples. We present a set of experiments showing the effectiveness of our approach in this business context.

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Bruno Charron
    • 1
  • Yu Hirate
    • 1
  • David Purcell
    • 1
  • Martin Rezk
    • 1
  1. 1.Rakuten Inc.TokyoJapan

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