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

TL;DR

PRASEMap is an unsupervised system that combines probabilistic reasoning and semantic embeddings to improve knowledge graph alignment, supporting user interaction and feedback for enhanced accuracy.

Contribution

It introduces a novel combination of reasoning and embedding techniques in an unsupervised framework with interactive features for knowledge graph alignment.

Findings

Supports various embedding approaches

Enables user feedback integration

Demonstrates improved alignment accuracy

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. The demo is available at https://prasemap.qizhy.com.