Very glad to announce KGPrune, a Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruning, accepted as a demo paper at #ECAI 2024! Looking forward to feedback and great use cases from the community!

📺 https://youtu.be/mt5gF4ZmhGY
🌐 https://kgprune.loria.fr/
📎 https://inria.hal.science/hal-04678284v1

#knowledgeGraph #artificialIntelligence #semanticWeb #linkedOpenData #knowledgeGraphConstruction #Wikidata

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KGPrune aims at extracting subgraphs of Wikidata related to input entities of interest to you. Our approach starts from such seed entities, and keeps or prunes their neighboring entities. This process allows you to extract subgraphs of Wikidata related to your domains of interest. Such subgraphs can then be used, e.g., as a high-quality nucleus to bootstrap the construction of a new knowledge graph, or as a supporting structure for knowledge extraction or mining approaches.