{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/69498"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/69498","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"LOOK: Implementation of an Expert System in Information Retrieval for Database Selection","abstract":"111 p.","abstract_html":"111 p.","abstract_has_math":false,"creators":["Thornburg, Gail Ellen"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Library Science","degree_department":null,"school":null,"contributors":["Smith, Linda C.","Michalski, R.S."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-12-15T19:18:28Z","date_published":"2014-12-15T19:18:28Z","updated_at":"2026-07-22T22:26:01Z","subjects":["Information Science","Artificial Intelligence"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(UMI)AAI8803222"],"render_values":[{"text":"(UMI)AAI8803222","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/69498","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Smith, Linda C.","Michalski, R.S."]},{"key":"dc:creator","label":"Author","values":["Thornburg, Gail Ellen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-12-15T19:18:28Z","10000-01-01","1987"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Library Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Information Science","Artificial Intelligence"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/69498","(UMI)AAI8803222"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["111 p.","This project was designed to construct an advisory or expert system in one area of online information retrieval, specifically, choice of online database. Problems of this domain include proliferation of potentially expensive databases, and the difficulty of predicting the specific database(s) most likely to yield optimum results in the climate of extreme "information compartmentalization." This implementation made use of an integrated set of software tools developed in the Artificial Intelligence Lab of the Department of Computer Science at the University of Illinois at Urbana-Champaign. The system was designed to reflect as closely as possible the decision-making expertise of academic online searchers in life sciences.","The implemented system, LOOK (non-acronymic), represented general features of 18 online databases, and its advice succeeded in satisfying the experts involved in its development. The system used a rule-based representation, and advisory sessions were guided by an inference algorithm featuring three phases of evaluation. Issues discussed here include the numbers of variables and values required to represent the domain with any adequacy, the levels of abstraction apparent in these variables, and the difficulty of separating domain from world knowledge in constructing an apt representation.","Made available in DSpace on 2014-12-15T19:18:28Z (GMT). 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Problems of this domain include proliferation of potentially expensive databases, and the difficulty of predicting the specific database(s) most likely to yield optimum results in the climate of extreme "information compartmentalization." This implementation made use of an integrated set of software tools developed in the Artificial Intelligence Lab of the Department of Computer Science at the University of Illinois at Urbana-Champaign. The system was designed to reflect as closely as possible the decision-making expertise of academic online searchers in life sciences.","The implemented system, LOOK (non-acronymic), represented general features of 18 online databases, and its advice succeeded in satisfying the experts involved in its development. The system used a rule-based representation, and advisory sessions were guided by an inference algorithm featuring three phases of evaluation. 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