Towards a Unified Supervised Approach for Ranking Triples of Type-Like Relations

  • Mahsa S. ShahshahaniEmail author
  • Faegheh Hasibi
  • Hamed Zamani
  • Azadeh Shakery
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10772)


Knowledge bases play a crucial role in modern search engines and provide users with information about entities. A knowledge base may contain many facts (i.e., RDF triples) about an entity, but only a handful of them are of significance for a searcher. Identifying and ranking these RDF triples is essential for various applications of search engines, such as entity ranking and summarization. In this paper, we present the first effort towards a unified supervised approach to rank triples from various type-like relations in knowledge bases. We evaluate our approach using the recently released test collections from the WSDM Cup 2017 and demonstrate the effectiveness of the proposed approach despite the fact that no relation-specific feature is used.


Knowledge bases Triple scoring Entity facts 



This work was partially supported in part by the Center for Intelligent Information Retrieval. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsors.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mahsa S. Shahshahani
    • 1
    Email author
  • Faegheh Hasibi
    • 2
  • Hamed Zamani
    • 3
  • Azadeh Shakery
    • 1
  1. 1.School of ECE, College of EngineeringUniversity of TehranTehranIran
  2. 2.Norwegian University of Science and TechnologyTrondheimNorway
  3. 3.Center for Intelligent Information RetrievalUniversity of Massachusetts AmherstAmherstUSA

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