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    <titleInfo>
        <title>Learning Sequence Encoders for Temporal Knowledge Graph Completion</title>
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    <name type="personal">
        <namePart type="given">Alberto</namePart>
        <namePart type="family">García-Durán</namePart>
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    <name type="personal">
        <namePart type="given">Sebastijan</namePart>
        <namePart type="family">Dumančić</namePart>
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    <name type="personal">
        <namePart type="given">Mathias</namePart>
        <namePart type="family">Niepert</namePart>
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        <dateIssued>2018-oct-nov</dateIssued>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>Research on link prediction in knowledge graphs has mainly focused on static multi-relational data. In this work we consider temporal knowledge graphs where relations between entities may only hold for a time interval or a specific point in time. In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations. To incorporate temporal information, we utilize recurrent neural networks to learn time-aware representations of relation types which can be used in conjunction with existing latent factorization methods. The proposed approach is shown to be robust to common challenges in real-world KGs: the sparsity and heterogeneity of temporal expressions. Experiments show the benefits of our approach on four temporal KGs. The data sets are available under a permissive BSD-3 license.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1516</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Learning Sequence Encoders for Temporal Knowledge Graph Completion
%A García-Durán, Alberto
%A Dumančić, Sebastijan
%A Niepert, Mathias
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F garcia-duran-etal-2018-learning
%X Research on link prediction in knowledge graphs has mainly focused on static multi-relational data. In this work we consider temporal knowledge graphs where relations between entities may only hold for a time interval or a specific point in time. In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations. To incorporate temporal information, we utilize recurrent neural networks to learn time-aware representations of relation types which can be used in conjunction with existing latent factorization methods. The proposed approach is shown to be robust to common challenges in real-world KGs: the sparsity and heterogeneity of temporal expressions. Experiments show the benefits of our approach on four temporal KGs. The data sets are available under a permissive BSD-3 license.
%R 10.18653/v1/D18-1516
%U https://aclanthology.org/D18-1516
%U https://doi.org/10.18653/v1/D18-1516
%P 4816-4821

Markdown (Informal)

[Learning Sequence Encoders for Temporal Knowledge Graph Completion](https://aclanthology.org/D18-1516) (García-Durán et al., EMNLP 2018)

• Learning Sequence Encoders for Temporal Knowledge Graph Completion (García-Durán et al., EMNLP 2018)

ACL

• Alberto García-Durán, Sebastijan Dumančić, and Mathias Niepert. 2018. Learning Sequence Encoders for Temporal Knowledge Graph Completion. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4816–4821, Brussels, Belgium. Association for Computational Linguistics.