KGPrune: a Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruning - Inria - Institut national de recherche en sciences et technologies du numérique
Poster Année : 2024

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

Résumé

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.
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Dates et versions

hal-04678284 , version 1 (27-08-2024)

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  • HAL Id : hal-04678284 , version 1

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Pierre Monnin, Cherif-Hassan Nousradine, Lucas Jarnac, Laurel Zuckerman, Miguel Couceiro. KGPrune: a Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruning. ECAI 2024 - 27th European Conference on Artificial Intelligence, Oct 2024, Santiago de Compostela, Spain. ECAI 2024 - 27th European Conference on Artificial Intelligence, October 19-24, 2024, Santiago de Compostela, Spain. ⟨hal-04678284⟩
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