Abstract
In high connectivity knowledge graph, distance based knowledge graph embedding methods show promising performance on link prediction task, and are capable of encoding complex relations and key relation patterns. However, the existing methods fail to achieve excellent results in knowledge graph with poor context structure information. To mitigate this problem, we propose Bi2E, a bidirectional model based on subject-object feature spaces. To enhance the efficiency of data utilization and perceive more potential semantic links, we utilize the bidirectionality of relation to model from both forward and reverse directions. And Bi2E represents triples in the subject-object feature spaces, which enables it to capture richer feature information from rare data. In addition, Bi2E employs adaptive margin \(\gamma \) which makes embedded representation more flexible by only using a small amount of feature information. Experiments on link prediction benchmarks demonstrate the proposed key capabilities of Bi2E. Moreover, we set a new state-of-the-art on two low connectivity knowledge graph benchmarks.
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Wang, Z., Li, X., Guo, Z. (2022). Bi2E: Bidirectional Knowledge Graph Embeddings Based on Subject-Object Feature Spaces. In: Sellami, M., Ceravolo, P., Reijers, H.A., Gaaloul, W., Panetto, H. (eds) Cooperative Information Systems. CoopIS 2022. Lecture Notes in Computer Science, vol 13591. Springer, Cham. https://doi.org/10.1007/978-3-031-17834-4_1
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