Abstract
Knowledge graph embedding aims to represent entities and relations in a knowledge graph as low-dimensional real-value vectors. Most existing studies exploit only structural information to learn these vectors. This paper studies how logical information expressed as RBox axioms in OWL 2 is used for embedding. The involvement of RBox axioms could prevent existing methods from learning predictive vectors. For example, the symmetric, reflexive or transitive relations can be declared by RBox axioms, but popular translation-based methods are unable to learn distinguishable vectors for multiple these relations in the ideal case. To overcome these limitations introduced by the involvement of RBox axioms, this paper proposes to enhance existing translation-based methods by logical pre-completion and bi-directional projection of entities. Experimental results demonstrate that these enhancements improve the predictive performance in link prediction and triple classification.
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Notes
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The head entity, relation and tail entity are respectively called the subject, predicate and object in OWL/RDF terminology. We use terminology in the field of knowledge graph embedding rather than OWL/RDF terminology throughout the paper.
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The test datasets and their attached RBoxes as well as the RapidMiner platform with test processes are available at http://www.dataminingcenter.net/JIST17/.
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Acknowledgements
This work was partly supported by National Natural Science Foundation of China under grants 61375056 and 61573386, Natural Science Foundation of Guangdong Province under grant 2016A030313292, Guangdong Province Science and Technology Plan projects under grant 2016B030305007, Sun Yat-sen University Cultivation Project (16lgpy40) and the Undergraduate Innovative Experiment Project in Guangdong University of Foreign Studies (201711846022).
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Du, J., Qi, K., Wan, H., Peng, B., Lu, S., Shen, Y. (2017). Enhancing Knowledge Graph Embedding from a Logical Perspective. In: Wang, Z., Turhan, AY., Wang, K., Zhang, X. (eds) Semantic Technology. JIST 2017. Lecture Notes in Computer Science(), vol 10675. Springer, Cham. https://doi.org/10.1007/978-3-319-70682-5_15
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