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        <title>A Semantic Filter Based on Relations for Knowledge Graph Completion</title>
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        <namePart type="given">Zongwei</namePart>
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        <namePart type="given">Junan</namePart>
        <namePart type="family">Yang</namePart>
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    <name type="personal">
        <namePart type="given">Hui</namePart>
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    <name type="personal">
        <namePart type="given">Keju</namePart>
        <namePart type="family">Huang</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>Knowledge graph embedding, representing entities and relations in the knowledge graphs with high-dimensional vectors, has made significant progress in link prediction. More researchers have explored the representational capabilities of models in recent years. That is, they investigate better representational models to fit symmetry/antisymmetry and combination relationships. The current embedding models are more inclined to utilize the identical vector for the same entity in various triples to measure the matching performance. The observation that measuring the rationality of specific triples means comparing the matching degree of the specific attributes associated with the relations is well-known. Inspired by this fact, this paper designs Semantic Filter Based on Relations(SFBR) to extract the required attributes of the entities. Then the rationality of triples is compared under these extracted attributes through the traditional embedding models. The semantic filter module can be added to most geometric and tensor decomposition models with minimal additional memory. experiments on the benchmark datasets show that the semantic filter based on relations can suppress the impact of other attribute dimensions and improve link prediction performance. The tensor decomposition models with SFBR have achieved state-of-the-art.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.625</identifier>
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        <date>2021-11</date>
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%0 Conference Proceedings
%T A Semantic Filter Based on Relations for Knowledge Graph Completion
%A Liang, Zongwei
%A Yang, Junan
%A Liu, Hui
%A Huang, Keju
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F liang-etal-2021-semantic
%X Knowledge graph embedding, representing entities and relations in the knowledge graphs with high-dimensional vectors, has made significant progress in link prediction. More researchers have explored the representational capabilities of models in recent years. That is, they investigate better representational models to fit symmetry/antisymmetry and combination relationships. The current embedding models are more inclined to utilize the identical vector for the same entity in various triples to measure the matching performance. The observation that measuring the rationality of specific triples means comparing the matching degree of the specific attributes associated with the relations is well-known. Inspired by this fact, this paper designs Semantic Filter Based on Relations(SFBR) to extract the required attributes of the entities. Then the rationality of triples is compared under these extracted attributes through the traditional embedding models. The semantic filter module can be added to most geometric and tensor decomposition models with minimal additional memory. experiments on the benchmark datasets show that the semantic filter based on relations can suppress the impact of other attribute dimensions and improve link prediction performance. The tensor decomposition models with SFBR have achieved state-of-the-art.
%R 10.18653/v1/2021.emnlp-main.625
%U https://aclanthology.org/2021.emnlp-main.625
%U https://doi.org/10.18653/v1/2021.emnlp-main.625
%P 7920-7929

Markdown (Informal)

[A Semantic Filter Based on Relations for Knowledge Graph Completion](https://aclanthology.org/2021.emnlp-main.625) (Liang et al., EMNLP 2021)

• A Semantic Filter Based on Relations for Knowledge Graph Completion (Liang et al., EMNLP 2021)

ACL

• Zongwei Liang, Junan Yang, Hui Liu, and Keju Huang. 2021. A Semantic Filter Based on Relations for Knowledge Graph Completion. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7920–7929, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.