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European Semantic Web Conference

ESWC 2022: The Semantic Web pp 39–55Cite as

Knowledge Graph Entity Type Prediction with Relational Aggregation Graph Attention Network

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13261)

Abstract

Most of the knowledge graph completion methods focus on inferring missing entities or relations between entities in the knowledge graphs. However, many knowledge graphs are missing entity types. The goal of entity type prediction in the knowledge graph is to infer the missing entity types that belong to entities in the knowledge graph, that is, (entity, entity type=?). At present, most knowledge graph entity type prediction models tend to model entities and entity types, which will cause the relations between entities to not be effectively used, and the relations often contain rich semantic information. To utilize the information contained in the relation when performing entity type prediction, we propose a method for entity type prediction based on relational aggregation graph attention network (RACE2T), which consists of an encoder relational aggregation graph attention network (FRGAT) and a decoder (CE2T). The encoder FRGAT uses the scoring function of the knowledge graph completion method to calculate the attention coefficient between entities. This attention coefficient will be used to aggregate the information of relations and entities in the neighborhood of the entity to utilize the information of the relations. The decoder CE2T is designed based on convolutional neural network, which models the entity embeddings output by FRGAT and entity type embeddings, and performs entity type prediction. The experimental results demonstrate that the method proposed in this paper outperforms existing methods. The source code and dataset for RACE2T can be downloaded from: https://github.com/GentlebreezeZ/RACE2T.

Keywords

  • Knowledge graph
  • Entity type
  • Relational aggregation
  • Attention
  • Scoring function
  • Convolutional neural network

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Fig. 1.
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Fig. 3.
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Notes

  1. 1.

    https://github.com/daiquocnguyen/ConvKB.

  2. 2.

    https://github.com/daiquocnguyen/CapsE.

  3. 3.

    https://github.ncsu.edu/cmoon2/kg.

  4. 4.

    https://github.com/Adam1679/ConnectE.

  5. 5.

    For details of entity type triples, see Ref [31].

  6. 6.

    https://github.com/GentlebreezeZ/CE2T.

  7. 7.

    https://github.com/GentlebreezeZ/RACE2T.

  8. 8.

    ConnectE (E2T) represents that entity type triples are not used.

  9. 9.

    ConnectE (E2T+TRT) represents that entity type triples are used.

  10. 10.

    https://github.com/Diego999/pyGAT.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61976032).

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Authors and Affiliations

  1. Faculty of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning, China

    Changlong Zou, Jingmin An & Guanyu Li

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  1. Changlong Zou
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  2. Jingmin An
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Corresponding author

Correspondence to Guanyu Li .

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Editors and Affiliations

  1. University of Amsterdam, Amsterdam, Noord-Holland, The Netherlands

    Paul Groth

  2. Universidad Simón Bolívar, Leibniz Information Centre for Science and Technology, Hannover, Niedersachsen, Germany

    Maria-Esther Vidal

  3. Institut Polytechnique de Paris "DIG", Télécom ParisTech, Palaiseau, France

    Fabian Suchanek

  4. University of Southern California, Marina del Rey, CA, USA

    Pedro Szekley

  5. IBM Research - Thomas J. Watson Research, Yorktown Heights, NY, USA

    Pavan Kapanipathi

  6. LaSIGE, Fac de Ciencias,Edif C6, Pis0 3, Universidade de Lisboa, Lisbon, Portugal

    Dr. Catia Pesquita

  7. University of Nantes, Nantes, France

    Hala Skaf-Molli

  8. Aalto University, Espoo, Finland

    Minna Tamper

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Zou, C., An, J., Li, G. (2022). Knowledge Graph Entity Type Prediction with Relational Aggregation Graph Attention Network. In: , et al. The Semantic Web. ESWC 2022. Lecture Notes in Computer Science, vol 13261. Springer, Cham. https://doi.org/10.1007/978-3-031-06981-9_3

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