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Knowledge Graph Representation Learning via Generated Descriptions

  • Conference paper
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Natural Language Processing and Information Systems (NLDB 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13913))

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Abstract

Knowledge graph representation learning (KGRL) aims to project the entities and relations into a continuous low-dimensional knowledge graph space to be used for knowledge graph completion and detecting new triples. Using textual descriptions for entity representation learning has been a key topic. However, the current work has two major constraints: (1) some entities do not have any associated descriptions; (2) the associated descriptions are usually phrases, and they do not contain enough information. This paper presents a novel KGRL method for learning effective embeddings by generating meaningful descriptive sentences from entities’ connections. The experiments using four public datasets and a new proposed dataset show that the New Description-Embodied Knowledge Graph Embedding (NDKGE for short) approach introduced in this paper outperforms most of the existing work in the task of link prediction. The code and datasets of this paper can be obtained from GitHub (https://github.com/MiaoHu-Pro/NDKGE.)

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Notes

  1. 1.

    www.freebase.com.

  2. 2.

    https://wordnet.princeton.edu/.

  3. 3.

    https://issues.apache.org/.

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Authors and Affiliations

  1. School of Mathematics and Physics, Queen’s University Belfast, Belfast, UK

    Miao Hu, Zhiwei Lin & Adele Marshall

Authors
  1. Miao Hu
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  2. Zhiwei Lin
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  3. Adele Marshall
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Corresponding author

Correspondence to Miao Hu .

Editor information

Editors and Affiliations

  1. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

  2. University of Derby, Derby, UK

    Farid Meziane

  3. Oakland University, Rochester, NY, USA

    Vijayan Sugumaran

  4. University of Derby, Derby, UK

    Warren Manning

  5. University of Derby, Derby, UK

    Stephan Reiff-Marganiec

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Hu, M., Lin, Z., Marshall, A. (2023). Knowledge Graph Representation Learning via Generated Descriptions. In: Métais, E., Meziane, F., Sugumaran, V., Manning, W., Reiff-Marganiec, S. (eds) Natural Language Processing and Information Systems. NLDB 2023. Lecture Notes in Computer Science, vol 13913. Springer, Cham. https://doi.org/10.1007/978-3-031-35320-8_26

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  • DOI: https://doi.org/10.1007/978-3-031-35320-8_26

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  • Online ISBN: 978-3-031-35320-8

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