{"type":"ScholarlyArticle","title":"Learning Intermediate Concepts in Constructing a Hierarchical Knowledge Base","authors":[{"name":"LiMin Fu","slug":"limin-fu","given":"LiMin","family":"Fu","affiliation":""},{"name":"Bruce G. Buchanan","slug":"bruce-g-buchanan","given":"Bruce G.","family":"Buchanan","affiliation":""}],"abstract":"In expert systems. hierarchical reasoning can provide better accuracy a nd understandability. Here, we develop a method of learning hierarchical knowledge from a case library, in which each tr ain ing instance is described by low level features and high level concepts (e.g., manifestations and diseases) but not by intennediate concep ts (e.g., disease states). · Learning intennediate knowledge involves exploiting the o ld partial intennediate knowledge or creating new intennediate concepts by observing the relationship between the low level features and high level concepts. Experiments in the domain of diagnosing causes of jaundice validate the method.","doi":null,"url":"https://ijcai.org/proceedings/1985/128","pdf":"https://ijcai.org/proceedings/1985/0128.pdf","markdown":"https://ijcai.org/proceedings/1985/128.md","bibtex":"https://ijcai.org/proceedings/1985/bibtex/128","volume":{"slug":"1985","title":"Proceedings of the Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1985","url":"https://ijcai.org/proceedings/1985/","year":1985,"location":"Los Angeles, California, USA","dates":"","editors":"Aravind Joshi","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Learning and Acquisition","track":"Learning and Acquisition","number":128,"pages":{"first":659,"last":665},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}