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Knowledge Graphs: Research Directions

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Reasoning Web. Declarative Artificial Intelligence (Reasoning Web 2020)

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Abstract

In these lecture notes, we provide an overview of some of the high-level research directions and open questions relating to knowledge graphs. We discuss six high-level concepts relating to knowledge graphs: data models, queries, ontologies, rules, embeddings and graph neural networks. While traditionally these concepts have been explored by different communities in the context of graphs, more recent works have begun to look at how they relate to one another, and how they can be unified. In fact, at a more foundational level, we can find some surprising relations between the different concepts. The research questions we explore mostly involve combinations of these concepts.

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Notes

  1. 1.

    We intend to refer to the data model known as graph databases [9], not graph database systems [2].

  2. 2.

    An abridged version of [35] is currently under review for ACM CSUR.

  3. 3.

    We implicitly refer to two-way regular path queries as inverse expressions are quite widely used in practice [15].

  4. 4.

    Cyclical graph patterns that entail concept assertions can be captured, in a slightly roundabout way, in DLs with Self Restrictions and Complex Role Inclusions.

  5. 5.

    In practice, knowledge graph embeddings can take complex-valued vectors, or real-valued matrices, or have entity and relation embeddings of different dimensions [67], and so forth, but such details are not exigent for our purposes.

  6. 6.

    We can still define a to be the largest dimension needed, padding other vectors.

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Acknowledgements

This work was supported by Fondecyt Grant No. 1181896 and by the Millennium Institute for Foundational Research on Data (IMFD). I would like to thank my co-authors on the extended tutorial for the various discussions and contributions that helped to inform these lecture notes. I also wish to thank the anonymous reviewers’ for their helpful comments.

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    Aidan Hogan

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    Marco Manna

  2. School of Informatics, University of Edinburgh, Edinburgh, UK

    Andreas Pieris

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Hogan, A. (2020). Knowledge Graphs: Research Directions. In: Manna, M., Pieris, A. (eds) Reasoning Web. Declarative Artificial Intelligence. Reasoning Web 2020. Lecture Notes in Computer Science(), vol 12258. Springer, Cham. https://doi.org/10.1007/978-3-030-60067-9_8

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