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W-KG2Vec: a weighted text-enhanced meta-path-based knowledge graph embedding for similarity search

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Abstract

Recently, similar entity searching over knowledge graph (KG) has gained much attentions by researchers. However, in rich-semantic KGs with multi-typed entities and relations, also known as heterogeneous information network, relevant entity search is considered as a challenging task due to the ambiguity as well as complexity of user’s queries in realistic applications, such as QA chatbot and KG-based information retrieval. In this paper, we propose a novel approach, called W-KG2Vec which enables to automatically learn the semantic representations of entities in KG by applying the meta-path. The proposed W-KG2Vec is a meta-path-specific model which supports to evaluate both semantic relations as well as the text-based similarity between entities. The combination of text- and structure-based embedding mechanism of W-KG2Vec is promising to achieve better representations of entities in given KGs for handling complex user’s queries. To effectively learn the sequential textual representations of entities’ descriptions, we propose a combination of BERT pre-trained model with LTSM encoder, called BERT-Text2Vec. Then, the text-based similarity between entities is used to leverage our weighted meta-path-based random walk mechanism in W-KG2Vec model. Extensive experiences on real-world KGs (YAGO and Freebase) demonstrate the effectiveness of our proposed model against recent state-of-the-art KG embedding baselines.

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Acknowledgements

This research is funded by Vietnam National University HoChiMinh City (VNU-HCM) under grant number DS2020-26-01.

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  1. University of Information Technology (UIT), VNU-HCM, Ho Chi Minh City, Vietnam

    Phuc Do & Phu Pham

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  1. Phuc Do
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Correspondence to Phuc Do.

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Do, P., Pham, P. W-KG2Vec: a weighted text-enhanced meta-path-based knowledge graph embedding for similarity search. Neural Comput & Applic 33, 16533–16555 (2021). https://doi.org/10.1007/s00521-021-06252-8

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  • DOI: https://doi.org/10.1007/s00521-021-06252-8

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