Papers › Analyzing Knowledge Graph Embedding Methods from a Multi-Embedding Interaction Perspective

Analyzing Knowledge Graph Embedding Methods from a Multi-Embedding Interaction Perspective

27 Mar 2019arXiv:1903.11406archive 2025-07-28

Hung Nghiep Tran, Atsuhiro Takasu

Knowledge graph is a popular format for representing knowledge, with many applications to semantic search engines, question-answering systems, and recommender systems. Real-world knowledge graphs are usually incomplete, so knowledge graph embedding methods, such as Canonical decomposition/Parallel factorization (CP), DistMult, and ComplEx, have been proposed to address this issue. These methods represent entities and relations as embedding vectors in semantic space and predict the links between them. The embedding vectors themselves contain rich semantic information and can be used in other applications such as data analysis. However, mechanisms in these models and the embedding vectors themselves vary greatly, making it difficult to understand and compare them. Given this lack of understanding, we risk using them ineffectively or incorrectly, particularly for complicated models, such as CP, with two role-based embedding vectors, or the state-of-the-art ComplEx model, with complex-valued embedding vectors. In this paper, we propose a multi-embedding interaction mechanism as a new approach to uniting and generalizing these models. We derive them theoretically via this mechanism and provide empirical analyses and comparisons between them. We also propose a new multi-embedding model based on quaternion algebra and show that it achieves promising results using popular benchmarks. Source code is available on GitHub at https://github.com/tranhungnghiep/AnalyzeKGE.

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Graph Embedding Knowledge Graph Embedding Knowledge Graphs Link Prediction Question Answering Recommendation Systems World Knowledge

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Link Prediction	WN18	Quaternion	Hits@1	0.931	#16 of 37	Archive leaderboard	report
Link Prediction	WN18	Quaternion	Hits@10	0.956	#16 of 37	Archive leaderboard	report
Link Prediction	WN18	Quaternion	Hits@3	0.950	#16 of 37	Archive leaderboard	report
Link Prediction	WN18	Quaternion	MRR	0.941	#16 of 37	Archive leaderboard	report

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