Optimized embedding and open domain representation learning in  knowledge graphs

Funing Yang; Bohui Du; Daiwei Tan; Xingliang Zhang; Bing Jia; Jing Liu

doi:10.1117/12.3048067

3 October 2024 Optimized embedding and open domain representation learning in knowledge graphs

Funing Yang, Bohui Du, Daiwei Tan, Xingliang Zhang, Bing Jia, Jing Liu

Author Affiliations +

Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 1327213 (2024) https://doi.org/10.1117/12.3048067
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

The Knowledge Graph (KG) is a model that represents structured knowledge by capturing entities and their relationships in the real world. It is widely used in search engines, recommendation systems, and natural language processing. Knowledge Representation Learning (KRL) transforms semantic information from knowledge graphs into continuous vector space representations, thereby improving knowledge acquisition and reasoning capabilities. However, current KRL faces issues such as underutilization of entity attribute information and difficulty in handling zero-shot scenarios. This paper proposes the Integrated Embedding Model (IEM) to address these issues. IEM employs a BERT-based attribute encoder and attention mechanism to weigh different attribute types, creating reliable attribute information embedding. During training, IEM merges structure and attribute representations, demonstrating excellent performance in knowledge graph completion tasks. Additionally, this paper introduces the Open Domain Representation Learning Model (ODRLM) for entity and link prediction in open domain knowledge graphs. ODRLM enhances the representation of zero-shot entities and relationships through three stages of optimization. Experimental results show that this model significantly improves both entity and relationship prediction accuracy, effectively addressing challenges in knowledge graph completion, especially in Zero-shot scenarios.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Funing Yang, Bohui Du, Daiwei Tan, Xingliang Zhang, Bing Jia, and Jing Liu "Optimized embedding and open domain representation learning in knowledge graphs", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 1327213 (3 October 2024); https://doi.org/10.1117/12.3048067

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