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Learning contextual representations for entity retrieval

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Abstract

In this paper, we introduce Contextual Entity Ranking (CoER) for the task of entity retrieval. CoER utilizes a textual knowledge graph to learn entities’ representations that are contextualized based on a given query. With these contextual representations and the query, CoER includes a set of models that learn to rank relevant entities. The introduced ranking models measure semantic relevance between entities’ contextual representations and the textual query, between entities’ contextual representations along with entities’ non-contextual and general descriptions and the textual query, and finally, between entities’ contextual representations and their relevance to the entities in the given query. We empirically illustrate that CoER is effective in retrieving and ranking entities across different benchmark datasets compared with state-of-the-art models. We also report ablation studies that investigate the impact of the contextual representation model and the ranking models on the final performance.

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Data availability and access

Code and results available at https://github.com/ParastooSJ/CER

Notes

  1. Code and results available at https://github.com/ParastooSJ/CER

  2. https://github.com/informagi/GEEER

  3. https://github.com/teanalab/kewer

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Funding

The research leading to these results received funding from Natural Sciences and Engineering Research Council of Canada, under Grant No 140869.

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Authors and Affiliations

  1. Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, Canada

    Parastoo Jafarzadeh & Faezeh Ensan

  2. Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Zahra Amirmahani

Authors
  1. Parastoo Jafarzadeh
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  2. Zahra Amirmahani
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  3. Faezeh Ensan
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Contributions

Parastoo Jafarzadeh: Conceptualization, Implementation, Investigation, and Writing. Zahra Amirmahani: Implementation. Faezeh Ensan: Conceptualization, Investigation, Writing, Supervision.

Corresponding author

Correspondence to Faezeh Ensan.

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(Check journal-specific guidelines for which heading to use): The authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper.

Ethical and informed consent for data used

The data that has been used is from publicly available sources: Wikipedia and DBpedia and publicly available datasets: DBpedia-Entity V2

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Jafarzadeh, P., Amirmahani, Z. & Ensan, F. Learning contextual representations for entity retrieval. Appl Intell 54, 8820–8840 (2024). https://doi.org/10.1007/s10489-024-05430-0

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