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

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

Abstract

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.

Keywords

Knowledge bases Triple scoring Entity facts 

Notes

Acknowledgements

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.

References

  1. 1.
    Bast, H., Buchhold, B., Haussmann, E.: Relevance scores for triples from type-like relations. In: SIGIR 2015, pp. 243–252 (2015)Google Scholar
  2. 2.
    Bast, H., Buchhold, B., Haussmann, E.: Overview of the triple scoring task at the WSDM Cup 2017. In: WSDM Cup (2017)Google Scholar
  3. 3.
    Bota, H., Zhou, K., Jose, J.M.: Playing your cards right: the effect of entity cards on search behaviour and workload. In: CHIIR 2016, pp. 131–140 (2016)Google Scholar
  4. 4.
    Ding, B., Wang, Q., Wang, B.: Leveraging text and knowledge bases for triple scoring: an ensemble approach - the Bokchoy triple scorer at WSDM Cup 2017. In: WSDM Cup (2017)Google Scholar
  5. 5.
    Hasibi, F., Balog, K., Bratsberg, S.E.: Dynamic factual summaries for entity cards. In: SIGIR 2017, pp. 773–782 (2017)Google Scholar
  6. 6.
    Hasibi, F., Garigliotti, D., Zhang, S., Balog, K.: Supervised ranking of triples for type-like relations - the cress triple scorer. In: WSDM Cup (2017)Google Scholar
  7. 7.
    Heindorf, S., Potthast, M., Bast, H., Buchhold, B., Haussmann, E.: WSDM Cup 2017: vandalism detection and triple scoring. In: WSDM 2017, pp. 827–828 (2017)Google Scholar
  8. 8.
    Zmiycharov, V., Alexandrov, D., Nakov, P., Koychev, I., Kiprov, Y.: Finding people’s professions and nationalities using distant supervision - the Goosefoot triple scorer. In: WSDM Cup (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mahsa S. Shahshahani
    • 1
  • 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

Personalised recommendations