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        <title>Differentiating Concepts and Instances for Knowledge Graph Embedding</title>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
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    <abstract>Concepts, which represent a group of different instances sharing common properties, are essential information in knowledge representation. Most conventional knowledge embedding methods encode both entities (concepts and instances) and relations as vectors in a low dimensional semantic space equally, ignoring the difference between concepts and instances. In this paper, we propose a novel knowledge graph embedding model named TransC by differentiating concepts and instances. Specifically, TransC encodes each concept in knowledge graph as a sphere and each instance as a vector in the same semantic space. We use the relative positions to model the relations between concepts and instances (i.e.,instanceOf), and the relations between concepts and sub-concepts (i.e., subClassOf). We evaluate our model on both link prediction and triple classification tasks on the dataset based on YAGO. Experimental results show that TransC outperforms state-of-the-art methods, and captures the semantic transitivity for instanceOf and subClassOf relation. Our codes and datasets can be obtained from https://github.com/davidlvxin/TransC.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1222</identifier>
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            <start>1971</start>
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%0 Conference Proceedings
%T Differentiating Concepts and Instances for Knowledge Graph Embedding
%A Lv, Xin
%A Hou, Lei
%A Li, Juanzi
%A Liu, Zhiyuan
%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 lv-etal-2018-differentiating
%X Concepts, which represent a group of different instances sharing common properties, are essential information in knowledge representation. Most conventional knowledge embedding methods encode both entities (concepts and instances) and relations as vectors in a low dimensional semantic space equally, ignoring the difference between concepts and instances. In this paper, we propose a novel knowledge graph embedding model named TransC by differentiating concepts and instances. Specifically, TransC encodes each concept in knowledge graph as a sphere and each instance as a vector in the same semantic space. We use the relative positions to model the relations between concepts and instances (i.e.,instanceOf), and the relations between concepts and sub-concepts (i.e., subClassOf). We evaluate our model on both link prediction and triple classification tasks on the dataset based on YAGO. Experimental results show that TransC outperforms state-of-the-art methods, and captures the semantic transitivity for instanceOf and subClassOf relation. Our codes and datasets can be obtained from https://github.com/davidlvxin/TransC.
%R 10.18653/v1/D18-1222
%U https://aclanthology.org/D18-1222
%U https://doi.org/10.18653/v1/D18-1222
%P 1971-1979

Markdown (Informal)

[Differentiating Concepts and Instances for Knowledge Graph Embedding](https://aclanthology.org/D18-1222) (Lv et al., EMNLP 2018)

• Differentiating Concepts and Instances for Knowledge Graph Embedding (Lv et al., EMNLP 2018)

ACL

• Xin Lv, Lei Hou, Juanzi Li, and Zhiyuan Liu. 2018. Differentiating Concepts and Instances for Knowledge Graph Embedding. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1971–1979, Brussels, Belgium. Association for Computational Linguistics.