Integrated Analysis for Knowledge Graph Embedding and Text Embedding 


Vol. 15,  No. 1, pp. 28-36, Jan.  2026
10.3745/TKIPS.2026.15.1.28


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  Abstract

Knowledge graphs have been utilized in a broad range of areas such as recommendation systems and question-answering systems. A knowledge graph can contain not only graph structures that represent the connections between entities but also the detailed explanations for each entity. Graph structures and explanations can be easily applied to various areas using knowledge graph embedding and text embedding, respectively. Although a knowledge graph can contain multi-modal data such as graph structures and texts, there has been little research on the integrated analysis of embeddings generated from each modality. This paper compares and analyzes knowledge graph embedding and text embedding on the YAGO3-10 data. Principal component analysis and correlation analysis are first performed on knowledge graph embedding and text embedding data. Then, the relationships with degrees, which are one of the important characteristics in a graph, are analyzed. Finally, this paper constructs linear regression models between knowledge graph embedding and text embedding.

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  Cite this article

[IEEE Style]

C. Lee, "Integrated Analysis for Knowledge Graph Embedding and Text Embedding," The Transactions of the Korea Information Processing Society, vol. 15, no. 1, pp. 28-36, 2026. DOI: 10.3745/TKIPS.2026.15.1.28.

[ACM Style]

Chun-Hee Lee. 2026. Integrated Analysis for Knowledge Graph Embedding and Text Embedding. The Transactions of the Korea Information Processing Society, 15, 1, (2026), 28-36. DOI: 10.3745/TKIPS.2026.15.1.28.