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

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Advances in Information Retrieval (ECIR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10772))

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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.

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Notes

  1. 1.

    See https://www.lemurproject.org/lemur.php.

  2. 2.

    See https://nlp.stanford.edu/projects/glove/.

  3. 3.

    For the sake of space, we only consider NDCG@5 as the evaluation metric in this experiment. The learning curves with respect to the other metrics, e.g., ASD, are also similar.

References

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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.

Author information

Authors and Affiliations

  1. School of ECE, College of Engineering, University of Tehran, Tehran, Iran

    Mahsa S. Shahshahani & Azadeh Shakery

  2. Norwegian University of Science and Technology, Trondheim, Norway

    Faegheh Hasibi

  3. Center for Intelligent Information Retrieval, University of Massachusetts Amherst, Amherst, USA

    Hamed Zamani

Authors
  1. Mahsa S. Shahshahani
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  2. Faegheh Hasibi
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  3. Hamed Zamani
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  4. Azadeh Shakery
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Corresponding author

Correspondence to Mahsa S. Shahshahani .

Editor information

Editors and Affiliations

  1. Department of Informatics, Systems, and Communication, University of Milano-Bicocca, Milan, Italy

    Gabriella Pasi

  2. LIP6 – UPMC/CNRS, University Pierre et Marie Curie, Paris, France

    Benjamin Piwowarski

  3. University of Glasgow, Glasgow, United Kingdom

    Leif Azzopardi

  4. Technical University of Vienna, Vienna, Austria

    Allan Hanbury

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Shahshahani, M.S., Hasibi, F., Zamani, H., Shakery, A. (2018). Towards a Unified Supervised Approach for Ranking Triples of Type-Like Relations. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_66

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  • DOI: https://doi.org/10.1007/978-3-319-76941-7_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76940-0

  • Online ISBN: 978-3-319-76941-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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