Semantic Enhancement Strategy for Knowledge Graph Completion
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Abstract
Knowledge graph completion (KGC) is a critical task aimed at enhancing the integrity and completeness of knowledge bases by predicting missing triples. Traditional methods often struggle with complex relations due to their limited semantic modeling capabilities, which restrict their ability to accurately infer missing information in diverse and intricate knowledge structures. To address these limitations, this paper systematically examines advanced semantic enhancement strategies for KGC. These strategies include external knowledge integration, deep representation learning, and logical reasoning reinforcement. By incorporating multi-source data, these methods enrich the semantic context of the knowledge graph, enabling more accurate predictions. Hierarchical semantic encoding further refines the representation of entities and relations, capturing nuanced meanings and dependencies. Additionally, causal reasoning mechanisms are introduced to handle complex relational patterns, ensuring that the inferred triples align with real-world causal structures. These enhanced methods demonstrate significant improvements in completion tasks, particularly in predicting long-tail entities that are often underrepresented in traditional approaches. Experiments on benchmark datasets such as FB15k show substantial performance gains, highlighting the effectiveness of the proposed strategies.
Keywords
Knowledge graph completion; Semantic enhancement; Representation learning; Logical reasoning
References
[1] Cai Q, XU B, Dong X. A knowledge graph completion model using semantically enhanced prompts and structural information[J]. Computer Science, 2024: 1-17.
[2] Zhao F, Wang S. Conceptual model construction of international Chinese digital-intelligent textbooks based on knowledge graphs[J]. Language Teaching and Research, 2025(4): 13-23.
[3] Ji X, Ying T, WANG X, et al. Research on programming knowledge retrieval and intelligent Q&A system based on knowledge graph[J]. Intelligent Computers and Applications, 2025, 15(7): 186-193.
[4] Cao J, Guo J. Three dimensions of applying knowledge graphs in ideological and political education[J]. Ideological and Political Education Research, 2025, 41(3): 130-136.
[5] Zhang L. A knowledge graph completion method based on reinforcement learning and graph attention mechanism[J]. Informatization Research, 2025, 51(3): 60-66.
[6] Liu M, Zhao Y, QIN B. Rethinking knowledge engineering courses in the era of large models[J]. Heilongjiang Education (Higher Education Research & Evaluation), 2025(6): 43-48.
[7] Peng C, Li Z. Construction of oil-gas exploration and development case database based on large language models and knowledge graphs[J]. Computer Programming Skills & Maintenance, 2025(5): 14-16, 47.
[8] Chen X, Li S, ZHOU C, et al. Review on fault knowledge graph technology system[J]. Computer Measurement & Control, 2025, 33(5): 1-12.
How to cite this paper
Semantic Enhancement Strategy for Knowledge Graph Completion
How to cite this paper: Jian Sun, Yizheng Xu, Yansong Li. (2025) Semantic Enhancement Strategy for Knowledge Graph Completion. Future Trends in AI Research, 2(1), 1-5.
DOI: http://dx.doi.org/10.26855/ftair.2025.06.001
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