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Dihedron Algebraic Embeddings for Spatio-Temporal Knowledge Graph Completion

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The Semantic Web(ESWC 2022)

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

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Abstract

Many knowledge graphs (KG) contain spatial and temporal information. Most KG embedding models follow triple-based representation and often neglect the simultaneous consideration of the spatial and temporal aspects. Encoding such higher dimensional knowledge necessitates the consideration of true algebraic and geometric aspects. Hypercomplex algebra provides the foundation of a well defined mathematical system among which the Dihedron algebra with its rich framework is suitable to handle multidimensional knowledge. In this paper, we propose an embedding model that uses Dihedron algebra for learning such spatial and temporal aspects. The evaluation results show that our model performs significantly better than other adapted models.

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Acknowledgement

We acknowledge the support of the following projects: SPEAKER (BMWi FKZ 01MK20011A), JOSEPH (Fraunhofer Zukunftsstiftung), the EU projects Cleopatra (GA 812997), PLATOON(GA 872592), TAILOR(EU GA 952215), CALLISTO(101004152), the BMBF projects MLwin(01IS18050) and the BMBF excellence clusters ML2R (BmBF FKZ 01 15 18038 A/B/C) and ScaDS.AI (IS18026A-F).

Author information

Authors and Affiliations

  1. University of Bonn, Bonn, Germany

    Mojtaba Nayyeri, Md Tansen Khan & Jens Lehmann

  2. Institute for Applied Informatics (InfAI), Dresden, Germany

    Sahar Vahdati, Mirza Mohtashim Alam & Lisa Wenige

  3. Institute for Telecommunications (INT), TH Köln, Cologne, Germany

    Andreas Behrend

  4. Fraunhofer IAIS, Dresden, Germany

    Jens Lehmann

  5. University of Stuttgart, Stuttgart, Germany

    Mojtaba Nayyeri

Authors
  1. Mojtaba Nayyeri

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  2. Sahar Vahdati

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  3. Md Tansen Khan

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  4. Mirza Mohtashim Alam

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  5. Lisa Wenige

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  6. Andreas Behrend

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  7. Jens Lehmann

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Corresponding author

Correspondence toMd Tansen Khan.

Editor information

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

    Catia Pesquita

  7. University of Nantes, Nantes, France

    Hala Skaf-Molli

  8. Aalto University, Espoo, Finland

    Minna Tamper

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Nayyeri, M.et al. (2022). Dihedron Algebraic Embeddings for Spatio-Temporal Knowledge Graph Completion. In: Groth, P.,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_15

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