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Concept Commons Enhanced Knowledge Graph Representation

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Knowledge Science, Engineering and Management (KSEM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13368))

Abstract

Knowledge graphs (KGs) are regarded as important resources for a variety of artificial intelligence (AI) and auxiliary decision tasks but suffer from incompleteness. To address this challenge, a number of knowledge graph representation (KGR) and knowledge graph completion (KGC) methods have been developed using graph embeddings manners. Most existing methods focus on the structured information of native triples in encyclopaedia KG and maximize the likelihood of them. However, they neglect semantic commons contained in lexical KG. Recent researches aims at investigating the general framework for enhancing KGR or KGC methods with semantic signals (e.g., entity types, entity descriptions etc.,). However, their work almost modeled the semantic resources by an implicit way, leading their results lacked interpretability. To overcome this drawback, we propose a novel Concept Commons enhanced Knowledge Graph Representation model (named as C\(^2\)KGR), that integrates the structured information in encyclopaedia KG and the entity’s concepts in lexical KG, via both implicit manner and explicit manner. Experimental results demonstrate the efficiency and interpretability of the proposed model on the real-world datasets.

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Notes

  1. 1.

    Probase naturally provides two kinds of relation types: (i) occurrence co-occurrence frequencies among entities (or concepts); (ii) IsA probabilities (“belong” relation) between entity and concept.

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Acknowledgements

We thank anonymous reviewers for valuable comments. This work is funded by: (i) the National Natural Science Foundation of China (No. U19B2026, No. 62106243).

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Authors and Affiliations

  1. National Engineering Laboratory for Risk Perception and Prevention (RPP), China Academy of Electronics and Information Technology of CETC, Beijing, 100041, China

    Yashen Wang, Xiaoye Ouyang, Xiaoling Zhu & Huanhuan Zhang

  2. Information Science Academy of CETC, Beijing, 100041, China

    Yashen Wang

Authors
  1. Yashen Wang
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  2. Xiaoye Ouyang
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  3. Xiaoling Zhu
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  4. Huanhuan Zhang
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Corresponding author

Correspondence to Yashen Wang .

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Editors and Affiliations

  1. Télécom Paris, Paris, France

    Gerard Memmi

  2. Purdue University, West Lafayette, IN, USA

    Baijian Yang

  3. Shanghai Jiao Tong University, Shanghai, Shanghai, China

    Linghe Kong

  4. Nanyang Technological University, Singapore, Singapore

    Tianwei Zhang

  5. Texas A&M University – Commerce, Commerce, TX, USA

    Meikang Qiu

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Wang, Y., Ouyang, X., Zhu, X., Zhang, H. (2022). Concept Commons Enhanced Knowledge Graph Representation. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13368. Springer, Cham. https://doi.org/10.1007/978-3-031-10983-6_32

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  • DOI: https://doi.org/10.1007/978-3-031-10983-6_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10982-9

  • Online ISBN: 978-3-031-10983-6

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