KGPrune: a Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruning

TL;DR

KGPrune is a web tool that efficiently extracts relevant subgraphs from Wikidata using analogical reasoning, aiding specific applications like enterprise knowledge bases and art provenance analysis.

Contribution

It introduces a novel analogical pruning algorithm for subgraph extraction, addressing scalability and relevance issues in large knowledge graphs.

Findings

Effective subgraph extraction from Wikidata demonstrated

Application to enterprise KG bootstrapping shown

Knowledge related to looted artworks successfully extracted

Abstract

Knowledge graphs (KGs) have become ubiquitous publicly available knowledge sources, and are nowadays covering an ever increasing array of domains. However, not all knowledge represented is useful or pertaining when considering a new application or specific task. Also, due to their increasing size, handling large KGs in their entirety entails scalability issues. These two aspects asks for efficient methods to extract subgraphs of interest from existing KGs. To this aim, we introduce KGPrune, a Web Application that, given seed entities of interest and properties to traverse, extracts their neighboring subgraphs from Wikidata. To avoid topical drift, KGPrune relies on a frugal pruning algorithm based on analogical reasoning to only keep relevant neighbors while pruning irrelevant ones. The interest of KGPrune is illustrated by two concrete applications, namely, bootstrapping an enterprise KG and extracting knowledge related to looted artworks.