  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.
