{"type":"ScholarlyArticle","title":"Inducing Probabilistic Relational Rules from Probabilistic Examples","authors":[{"name":"Luc De Raedt","slug":"luc-de-raedt","given":"Luc","family":"de Raedt","affiliation":""},{"name":"Anton Dries","slug":"anton-dries","given":"Anton","family":"Dries","affiliation":""},{"name":"Ingo Thon","slug":"ingo-thon","given":"Ingo","family":"Thon","affiliation":""},{"name":"Guy Van den Broeck","slug":"guy-van-den-broeck","given":"Guy","family":"Van den Broeck","affiliation":""},{"name":"Mathias Verbeke","slug":"mathias-verbeke","given":"Mathias","family":"Verbeke","affiliation":""}],"abstract":"We study the problem of inducing logic programs in a probabilistic setting, in which both the example descriptions and their classification can be probabilistic. The setting is incorporated in the probabilistic rule learner ProbFOIL+, which combines principles of the rule learner FOIL with ProbLog, a probabilistic Prolog. We illustrate the approach by applying it to the knowledge base of NELL, the Never-Ending Language Learner.","doi":null,"url":"https://ijcai.org/proceedings/2015/261","pdf":"https://ijcai.org/proceedings/2015/0261.pdf","markdown":"https://ijcai.org/proceedings/2015/261.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/261","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Relational Learning","track":"Main Track","number":261,"pages":{"first":1835,"last":1842},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}