Abstract
Representation learning of knowledge graph aims to transform both the entities and relations into continuous low-dimensional vector space. Though there have been a variety of models for knowledge graph embedding, most existing latent-based models merely explain triples via latent features, while supplementary rich inference patterns hidden in the observed graph features have not been fully employed. For this reason, in this paper we propose the discriminative path-based embedding model (DPTransE) which jointly learns from the latent features and graph features. Our model builds interactions between these two features, and uses the graph features as the crucial prior to offer precise and discriminative embedding. Experimental results demonstrate that our method outperforms other baselines on the task of link prediction and entity classification.
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Notes
- 1.
The original relations are
(1) \(/film/film\_crewmember/films\_crewed./film/film\_crew\_gig/film\),
(2) \(/award/award\_nominee/award\_nominations./award/award\_nomination/nominated\_for\), here we use a wildcard \( * \) to reduce occupation without ambiguous expression.
- 2.
The original relation path is \(/award/award\_winner/awards\_won./award/award\_honor/award\) \( -> \) \(/award/award\_category/nominees./award/award\_nomination/nominated\_for\).
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Acknowledgments
We thank all the anonymous reviewers for their detailed and insightful comments on this paper. This work was supported by Humanity and Social Science Youth Foundation of Ministry of Education of China (No.15YJC870029) and Research Planning Project of National Language Committee (No. YB135-40).
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Zhang, M., Wang, Q., Xu, W., Li, W., Sun, S. (2018). Discriminative Path-Based Knowledge Graph Embedding for Precise Link Prediction. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_21
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