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        <title>Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs</title>
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        <namePart type="given">Wenpeng</namePart>
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    <name type="personal">
        <namePart type="given">Yadollah</namePart>
        <namePart type="family">Yaghoobzadeh</namePart>
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        <namePart type="given">Hinrich</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
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    <abstract>Large scale knowledge graphs (KGs) such as Freebase are generally incomplete. Reasoning over multi-hop (mh) KG paths is thus an important capability that is needed for question answering or other NLP tasks that require knowledge about the world. mh-KG reasoning includes diverse scenarios, e.g., given a head entity and a relation path, predict the tail entity; or given two entities connected by some relation paths, predict the unknown relation between them. We present ROPs, recurrent one-hop predictors, that predict entities at each step of mh-KB paths by using recurrent neural networks and vector representations of entities and relations, with two benefits: (i) modeling mh-paths of arbitrary lengths while updating the entity and relation representations by the training signal at each step; (ii) handling different types of mh-KG reasoning in a unified framework. Our models show state-of-the-art for two important multi-hop KG reasoning tasks: Knowledge Base Completion and Path Query Answering.</abstract>
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        <url>https://aclanthology.org/C18-1200</url>
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        <date>2018-08</date>
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            <start>2369</start>
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%0 Conference Proceedings
%T Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs
%A Yin, Wenpeng
%A Yaghoobzadeh, Yadollah
%A Schütze, Hinrich
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F yin-etal-2018-recurrent
%X Large scale knowledge graphs (KGs) such as Freebase are generally incomplete. Reasoning over multi-hop (mh) KG paths is thus an important capability that is needed for question answering or other NLP tasks that require knowledge about the world. mh-KG reasoning includes diverse scenarios, e.g., given a head entity and a relation path, predict the tail entity; or given two entities connected by some relation paths, predict the unknown relation between them. We present ROPs, recurrent one-hop predictors, that predict entities at each step of mh-KB paths by using recurrent neural networks and vector representations of entities and relations, with two benefits: (i) modeling mh-paths of arbitrary lengths while updating the entity and relation representations by the training signal at each step; (ii) handling different types of mh-KG reasoning in a unified framework. Our models show state-of-the-art for two important multi-hop KG reasoning tasks: Knowledge Base Completion and Path Query Answering.
%U https://aclanthology.org/C18-1200
%P 2369-2378

Markdown (Informal)

[Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs](https://aclanthology.org/C18-1200) (Yin et al., COLING 2018)

• Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs (Yin et al., COLING 2018)

ACL

• Wenpeng Yin, Yadollah Yaghoobzadeh, and Hinrich Schütze. 2018. Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2369–2378, Santa Fe, New Mexico, USA. Association for Computational Linguistics.