PRASEMap: A Probabilistic Reasoning and Semantic Embedding based Knowledge Graph Alignment System
This work addresses knowledge graph alignment for researchers and practitioners, but it is incremental as it combines existing techniques without major breakthroughs.
The authors tackled the problem of aligning knowledge graphs by developing PRASEMap, an unsupervised system that combines probabilistic reasoning and semantic embedding techniques, resulting in a user-friendly web application that supports iterative mapping and human feedback.
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. The demo is available at https://prasemap.qizhy.com.