Article In: dblp
KGPrune: a web application to extract subgraphs of interest from Wikidata with analogical pruning
ECAI 2024 - 27th European Conference on Artificial Intelligence
— 2024 — IOS Press
Key information
Authors:
Published in
2024
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.
Publication details
Authors in the community:
Miguel Couceiro
ist430030
Publisher
IOS Press
Link to the publisher's version
https://inria.hal.science/hal-04678284v1
Title of the publication container
ECAI 2024 - 27th European Conference on Artificial Intelligence
Volume
abs/2408.14658
Fields of Science and Technology (FOS)
computer-and-information-sciences - Computer and information sciences
Publication language (ISO code)
eng - English
Rights type:
Open access
Creative Commons license
CC-BY-NC - Attribution-NonCommercial