Differential Causal Rules Mining in Knowledge Graphs
| Autor: | Rallou Thomopoulos, Nathalie Pernelle, Fatiha Saïs, Lucas Simonne |
|---|---|
| Přispěvatelé: | Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Données et Connaissances Massives et Hétérogènes (LaHDAK), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Science des Données (SDD), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique de Paris-Nord (LIPN), Centre National de la Recherche Scientifique (CNRS)-Université Sorbonne Paris Nord, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) |
| Rok vydání: | 2021 |
| Předmět: |
Class (computer programming)
business.industry Computer science Explainability Machine learning computer.software_genre [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Domain (software engineering) Development (topology) Knowledge graph Causal rules Knowledge graphs Key (cryptography) Artificial intelligence Differential (infinitesimal) business computer Semantic matching |
| Zdroj: | K-CAP 11th Knowledge Capture Conference 11th Knowledge Capture Conference, Dec 2021, New York (USA), United States. ⟨10.1145/3460210.3493584⟩ |
| DOI: | 10.1145/3460210.3493584 |
| Popis: | In recent years, keen interest towards Knowledge Graphs has increased in both academia and the industry which has led to the creation of various datasets and the development of different research topics. In this paper, we present an approach that discovers differential causal rules in Knowledge Graphs. Such rules express that for two different class instances, a different treatment leads to different outcomes. Discovering causal rules is often the key of experiments, independently of their domain. The proposed approach is based on semantic matching relying on community detection and strata that can be defined as complex sub-classes. An experimental evaluation on two datasets shows that such mined rules can help gain insights into various domains. |
| Databáze: | OpenAIRE |
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