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Knowledge Graph Induction Enabling Recommending and Trend Analysis: A Corporate Research Community Use Case

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The Semantic Web – ISWC 2022 (ISWC 2022)

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Abstract

A research division plays an important role of driving innovation in an organization. Drawing insights, following trends, keeping abreast of new research, and formulating strategies are increasingly becoming more challenging for both researchers and executives as the amount of information grows in both velocity and volume. In this paper we present a use case of how a corporate research community, IBM Research, utilizes Semantic Web technologies to induce a unified Knowledge Graph from both structured and textual data obtained by integrating various applications used by the community related to research projects, academic papers, datasets, achievements and recognition. In order to make the Knowledge Graph more accessible to application developers, we identified a set of common patterns for exploiting the induced knowledge and exposed them as APIs. Those patterns were born out of user research which identified the most valuable use cases or user pain points to be alleviated. We outline two distinct scenarios: recommendation and analytics for business use. We will discuss these scenarios in detail and provide an empirical evaluation on entity recommendation specifically. The methodology used and the lessons learned from this work can be applied to other organizations facing similar challenges.

N. Mihindukulasooriya, M. Sava, G. Rossiello and Md. F. M. Chowdhury—Equal contributions.

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Notes

  1. 1.

    https://blog.dblp.org/2022/03/02/dblp-in-rdf/.

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Author information

Authors and Affiliations

  1. IBM Research AI, Yorktown Heights, NY, USA

    Nandana Mihindukulasooriya, Mike Sava, Gaetano Rossiello, Md. Faisal Mahbub Chowdhury, Irene Yachbes, Aditya Gidh, Jillian Duckwitz, Kovit Nisar, Michael Santos & Alfio Gliozzo

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  1. Nandana Mihindukulasooriya
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  2. Mike Sava
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  3. Gaetano Rossiello
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  4. Md. Faisal Mahbub Chowdhury
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  5. Irene Yachbes
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  6. Aditya Gidh
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  7. Jillian Duckwitz
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  8. Kovit Nisar
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  9. Michael Santos
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  10. Alfio Gliozzo
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Corresponding author

Correspondence to Nandana Mihindukulasooriya .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Ulrike Sattler

  2. University of Chile, Santiago, Chile

    Aidan Hogan

  3. University of Cape Town, Cape Town, South Africa

    Maria Keet

  4. University of Bologna, Bologna, Italy

    Valentina Presutti

  5. Universidade Federal do Espírito Santo, Vitória, Brazil

    João Paulo A. Almeida

  6. National Institute of Informatics, Tokyo, Japan

    Hideaki Takeda

  7. Orange, Belfort, France

    Pierre Monnin

  8. Sapienza University of Rome, Rome, Italy

    Giuseppe Pirrò

  9. University of Bari, Bari, Italy

    Claudia d’Amato

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Mihindukulasooriya, N. et al. (2022). Knowledge Graph Induction Enabling Recommending and Trend Analysis: A Corporate Research Community Use Case. In: Sattler, U., et al. The Semantic Web – ISWC 2022. ISWC 2022. Lecture Notes in Computer Science, vol 13489. Springer, Cham. https://doi.org/10.1007/978-3-031-19433-7_47

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  • DOI: https://doi.org/10.1007/978-3-031-19433-7_47

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