Summary of Kgprune: a Web Application to Extract Subgraphs Of Interest From Wikidata with Analogical Pruning, by Pierre Monnin et al.
KGPrune: a Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruning
by Pierre Monnin, Cherif-Hassan Nousradine, Lucas Jarnac, Laurel Zuckerman, Miguel Couceiro
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Databases (cs.DB); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Web Application, KGPrune, is introduced for efficiently extracting subgraphs of interest from large knowledge graphs (KGs). Given seed entities and properties, KGPrune extracts neighboring subgraphs from Wikidata while avoiding topical drift through a frugal pruning algorithm based on analogical reasoning. The application is demonstrated through two case studies: bootstrapping an enterprise KG and extracting knowledge related to looted artworks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a huge library with millions of books, but most of them are about things that don’t interest you. You want to find the relevant information quickly without having to read all the books. This is similar to what happens when we try to use large databases of knowledge, like Wikidata, to learn something new. We need a way to efficiently extract the useful information from these big databases. A team has developed a tool called KGPrune that does just this. It helps us find the most important pieces of information related to a specific topic or question. The tool is useful for many real-life applications, such as building a database for an organization or finding information about stolen artworks. |
Keywords
» Artificial intelligence » Bootstrapping » Pruning
