Work

Interactive Causal Discovery in Knowledge Graphs

Year: 2019

Type: conference-paper

Abstract: Being able to provide explanations about a domain is a hard task that requires from a probabilistic reasoning's viewpoint a causal knowledge about the domain variables, allowing one to predict how they can influence each others. However, causal discovery from data alone remains a challenging question. In this article, we introduce a way to tackle this question by presenting an interactive method to build a probabilistic relational model from any given relevant domain represented by a knowledge graph. Combining both ontological and expert knowledge, we define a set of constraints translated into a so-called relational schema. Such a relational schema can then be used to learn a probabilistic relational model, which allows causal discovery.

Source: HAL

Authors: Munch, Mélanie, Dibie-Barthelemy, Juliette, Wuillemin, Pierre-Henri, Manfredotti, Cristina

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2982550454

FWCI: 0.245

Cites: 17

Cited by: 5

Related to: 10