KGPrune: a Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruning
This addresses scalability and relevance issues for users handling large knowledge graphs in specific applications, though it is incremental as it builds on existing pruning techniques.
The authors tackled the problem of extracting relevant subgraphs from large knowledge graphs like Wikidata by introducing KGPrune, a web application that uses analogical pruning to remove irrelevant neighbors, resulting in efficient extraction for tasks such as bootstrapping enterprise knowledge graphs and analyzing looted artworks.
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.