Abstract
Although concepts and instances in a knowledge graph (KG) are distinguished, TransC embeds concepts, instances, and various relations into the same vector space, which leads to the following problems: (1) The same instance in different triples that model different relations between instances is represented as the same vector, resulting in improper representation of different properties possessed by this instance; (2) Multiple instances not belonging to one concept may be located in the sphere representing this concept, resulting in an inaccurate modeling of the instanceOf relations between these instances and the concept. Based on TransC, this paper proposes a fine-grained KG embedding model called TransFG. TransFG embeds concepts, instances, and relations into different vector spaces and projects the instance vectors from the instance space to the concept space and the relation spaces through dynamic mapping matrices. This causes the projected vectors of the same instance in different triples to have different representations and the projected vectors of multiple instances belonging to the same concept to be spatially close to each other; otherwise they are far away. Experiments on the YAGO39K and M-YAGO39K datasets show that on the triple classification task, TransFG outperforms TransC and other typical KG embedding models in terms of accuracy, precision, recall and F1-score in most cases, and on the link prediction task, TransFG outperforms these compared models in terms of MRR and Hits@N in most cases.
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Yu, Y., Xu, Z., Lv, Y., Li, J. (2019). TransFG: A Fine-Grained Model for Knowledge Graph Embedding. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_45
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