{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T14:39:54Z","timestamp":1777646394399,"version":"3.51.4"},"reference-count":0,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2000,1,1]],"date-time":"2000-01-01T00:00:00Z","timestamp":946684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Fundamenta Informaticae"],"published-print":{"date-parts":[[2000,3]]},"abstract":"<jats:p>Knowledge scouts are software agents that autonomously synthesize user-oriented knowledge (target knowledge) from information present in local or distributed databases. A knowledge generation metalanguage, KGL, is used to creating scripts defining such knowledge scouts. Knowledge scouts operate in an inductive database, by which we mean a database system in which conventional data and knowledge management operators are integrated with a wide range of data mining and inductive inference operators. Discovered knowledge is represented in two forms: (1) attributional rules, which are rules in attributional calculus\u2014a logic-based language between prepositional and predicate calculus, and (2) association graphs, which graphically and abstractly represent relations expressed by the rules. These graphs can depict multi-argument relationships among different concepts, with a visual indication of the relative strength of each dependency. Presented ideas are illustrated by two simple knowledge scouts, one that seeks relations among lifestyles, environmental conditions, symptoms and diseases in a large medical database, and another that searches for patterns of children's behavior in the National Youth Survey database. The preliminary results indicate a high potential utility of this methodology for deriving knowledge from databases.<\/jats:p>","DOI":"10.3233\/fi-2000-41404","type":"journal-article","created":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T22:05:55Z","timestamp":1575324355000},"page":"433-447","source":"Crossref","is-referenced-by-count":6,"title":["Building Knowledge Scouts Using KGL Metalanguage"],"prefix":"10.1177","volume":"41","author":[{"given":"Ryszard S.","family":"Michalski","sequence":"first","affiliation":[{"name":"Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA 22030-4444, USA. michalski@gmu.edu"}]},{"given":"Kenneth A.","family":"Kaufman","sequence":"additional","affiliation":[{"name":"Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA 22030-4444, USA. michalski@gmu.edu"}]}],"member":"179","published-online":{"date-parts":[[2000,1,1]]},"container-title":["Fundamenta Informaticae"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/FI-2000-41404","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/FI-2000-41404","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T06:35:06Z","timestamp":1777444506000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/FI-2000-41404"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2000,1,1]]},"references-count":0,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2000,3]]}},"alternative-id":["10.3233\/FI-2000-41404"],"URL":"https:\/\/doi.org\/10.3233\/fi-2000-41404","relation":{},"ISSN":["0169-2968","1875-8681"],"issn-type":[{"value":"0169-2968","type":"print"},{"value":"1875-8681","type":"electronic"}],"subject":[],"published":{"date-parts":[[2000,1,1]]}}}