Completeness-aware Rule Learning from Knowledge Graphs
| Autor: | Daria Stepanova, Simon Razniewski, Gerhard Weikum, Paramita Mirza, Thomas Pellissier Tanon |
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| Přispěvatelé: | Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Max-Planck-Institut für Informatik (MPII), Max-Planck-Gesellschaft |
| Rok vydání: | 2018 |
| Předmět: |
Association rule learning
Computer science media_common.quotation_subject Rule mining 02 engineering and technology computer.software_genre 01 natural sciences [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 020204 information systems Completeness (order theory) 0202 electrical engineering electronic engineering information engineering Question answering [INFO]Computer Science [cs] Quality (business) media_common [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB] Information retrieval business.industry 010401 analytical chemistry 0104 chemical sciences Knowledge graph 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing |
| Zdroj: | IJCAI The Semantic Web--ISWC 2017 Lecture Notes in Computer Science Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18} Twenty-Seventh International Joint Conference on Artificial Intelligence, Jul 2018, Stockholm, Sweden. ⟨10.24963/ijcai.2018/749⟩ Lecture Notes in Computer Science ISBN: 9783319682877 ISWC (1) |
| Popis: | International audience; Knowledge graphs (KGs) are huge collections of primarily encyclopedic facts that are widely used in entity recognition, structured search, question answering , and other tasks. Rule mining is commonly applied to discover patterns in KGs. However, unlike in traditional association rule mining, KGs provide a setting with a high degree of incompleteness, which may result in the wrong estimation of the quality of mined rules, leading to erroneous beliefs such as all artists have won an award. In this paper we propose to use (in-)completeness meta-information to better assess the quality of rules learned from incomplete KGs. We introduce completeness-aware scoring functions for relational association rules. Experimental evaluation both on real and synthetic datasets shows that the proposed rule ranking approaches have remarkably higher accuracy than the state-of-the-art methods in uncovering missing facts. |
| Databáze: | OpenAIRE |
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