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PRASEMap: A Probabilistic Reasoning and Semantic Embedding based Knowledge Graph Alignment System

2021-06-16 14:06:09
Zhiyuan Qi, Ziheng Zhang, Jiaoyan Chen, Xi Chen, Yefeng Zheng
arXiv_AI
               
Abstract

Knowledge Graph (KG) alignment aims at finding equivalent entities and relations (i.e., mappings) between two KGs. The existing approaches utilize either reasoning-based or semantic embedding-based techniques, but few studies explore their combination. In this demonstration, we present PRASEMap, an unsupervised KG alignment system that iteratively computes the Mappings with both Probabilistic Reasoning (PR) And Semantic Embedding (SE) techniques. PRASEMap can support various embedding-based KG alignment approaches as the SE module, and enables easy human computer interaction that additionally provides an option for users to feed the mapping annotations back to the system for better results. The demonstration showcases these features via a stand-alone Web application with user friendly interfaces.

Abstract (translated)

URL

https://arxiv.org/abs/2106.08801

PDF

https://arxiv.org/pdf/2106.08801.pdf

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