<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="jain-etal-2018-type">
    <titleInfo>
        <title>Type-Sensitive Knowledge Base Inference Without Explicit Type Supervision</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Prachi</namePart>
        <namePart type="family">Jain</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Pankaj</namePart>
        <namePart type="family">Kumar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name>
        <namePart>Mausam</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Soumen</namePart>
        <namePart type="family">Chakrabarti</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Melbourne, Australia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>State-of-the-art knowledge base completion (KBC) models predict a score for every known or unknown fact via a latent factorization over entity and relation embeddings. We observe that when they fail, they often make entity predictions that are incompatible with the type required by the relation. In response, we enhance each base factorization with two type-compatibility terms between entity-relation pairs, and combine the signals in a novel manner. Without explicit supervision from a type catalog, our proposed modification obtains up to 7% MRR gains over base models, and new state-of-the-art results on several datasets. Further analysis reveals that our models better represent the latent types of entities and their embeddings also predict supervised types better than the embeddings fitted by baseline models.</abstract>
    <identifier type="citekey">jain-etal-2018-type</identifier>
    <identifier type="doi">10.18653/v1/P18-2013</identifier>
    <location>
        <url>https://aclanthology.org/P18-2013</url>
    </location>
    <part>
        <date>2018-07</date>
        <extent unit="page">
            <start>75</start>
            <end>80</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Type-Sensitive Knowledge Base Inference Without Explicit Type Supervision
%A Jain, Prachi
%A Kumar, Pankaj
%A Chakrabarti, Soumen
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%A Mausam
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F jain-etal-2018-type
%X State-of-the-art knowledge base completion (KBC) models predict a score for every known or unknown fact via a latent factorization over entity and relation embeddings. We observe that when they fail, they often make entity predictions that are incompatible with the type required by the relation. In response, we enhance each base factorization with two type-compatibility terms between entity-relation pairs, and combine the signals in a novel manner. Without explicit supervision from a type catalog, our proposed modification obtains up to 7% MRR gains over base models, and new state-of-the-art results on several datasets. Further analysis reveals that our models better represent the latent types of entities and their embeddings also predict supervised types better than the embeddings fitted by baseline models.
%R 10.18653/v1/P18-2013
%U https://aclanthology.org/P18-2013
%U https://doi.org/10.18653/v1/P18-2013
%P 75-80

Markdown (Informal)

[Type-Sensitive Knowledge Base Inference Without Explicit Type Supervision](https://aclanthology.org/P18-2013) (Jain et al., ACL 2018)

• Type-Sensitive Knowledge Base Inference Without Explicit Type Supervision (Jain et al., ACL 2018)

ACL

• Prachi Jain, Pankaj Kumar, Mausam, and Soumen Chakrabarti. 2018. Type-Sensitive Knowledge Base Inference Without Explicit Type Supervision. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 75–80, Melbourne, Australia. Association for Computational Linguistics.