{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/22334"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/22334","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Knowledge-based learning: Integration of deductive and inductive learning for knowledge base completion","abstract":"To learn effectively, a system needs to use all the knowledge that is available. Explanation-based learning and similarity-based learning operate over a domain theory and a set of examples, respectively, but neither approach makes extensive use of both forms of knowledge. Many problems in engineering and other areas can provide a learning system with an incomplete domain theory and a limited set of examples. Knowledge-based learning uses knowledge in both forms to learn knowledge missing from the domain theory.","abstract_html":"To learn effectively, a system needs to use all the knowledge that is available. Explanation-based learning and similarity-based learning operate over a domain theory and a set of examples, respectively, but neither approach makes extensive use of both forms of knowledge. Many problems in engineering and other areas can provide a learning system with an incomplete domain theory and a limited set of examples. Knowledge-based learning uses knowledge in both forms to learn knowledge missing from the domain theory.","abstract_has_math":false,"creators":["Whitehall, Bradley Lane"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Lu, Stephen C-Y"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-07T13:36:34Z","date_published":"2011-05-07T13:36:34Z","updated_at":"2026-07-22T22:25:19Z","subjects":["Artificial Intelligence","Computer Science"],"languages":["eng"],"rights":["Copyright 1990 Whitehall, Bradley Lane"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9114461","(UMI)AAI9114461"],"render_values":[{"text":"AAI9114461","href":null,"code":true},{"text":"(UMI)AAI9114461","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/22334","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Lu, Stephen C-Y"]},{"key":"dc:creator","label":"Author","values":["Whitehall, Bradley Lane"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-05-07T13:36:34Z","10000-01-01","1990"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer 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":["Artificial Intelligence","Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 1990 Whitehall, Bradley Lane"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["AAI9114461","(UMI)AAI9114461","http://hdl.handle.net/2142/22334"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["To learn effectively, a system needs to use all the knowledge that is available. Explanation-based learning and similarity-based learning operate over a domain theory and a set of examples, respectively, but neither approach makes extensive use of both forms of knowledge. Many problems in engineering and other areas can provide a learning system with an incomplete domain theory and a limited set of examples. Knowledge-based learning uses knowledge in both forms to learn knowledge missing from the domain theory.","The knowledge-based learning approach is illustrated with two systems, KBL0 and KBL1. These systems have been designed and implemented to work with domains requiring a representation of real numbers and mathematical formulas, such as engineering.","This research has shown, not only that it is possible to use a domain theory to guide induction using examples, but that when there are few examples available compared to the size of the problem space, the resulting rules are more accurate and stable than those from pure empirical techniques. In addition, knowledge-based learning algorithms free the user from selecting relevant examples and attributes for learning by using an incomplete domain theory to determine where knowledge needs to be added. A problem unsolved by the current domain knowledge helps to determine where new knowledge needs to be incorporated into the domain theory and what the context is for the learning. The context is used to select relevant examples from an example base and to reduce the number of attributes used during induction. With the control structure provided by knowledge-based systems, inductive learning can be used to extend an existing knowledge base.","Made available in DSpace on 2011-05-07T13:36:34Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9114461.pdf: 6461839 bytes, checksum: 74f2f8f02751b63a344798a216284232 (MD5) Previous issue date: 1990","Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:56:55Z Item is restricted indefinitely.","Restriction data tranferred 2014-07-01T11:26:39-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission","ETDs are only available to UIUC Users without author permission","U of I Only"]},{"key":"dc:title","label":"Title","values":["Knowledge-based learning: Integration of deductive and inductive learning for knowledge base completion"]}]}],"canonical_facts":{"dc:contributor":["Lu, Stephen C-Y"],"dc:creator":["Whitehall, Bradley Lane"],"dc:date":["2011-05-07T13:36:34Z","10000-01-01","1990"],"dc:description":["To learn effectively, a system needs to use all the knowledge that is available. Explanation-based learning and similarity-based learning operate over a domain theory and a set of examples, respectively, but neither approach makes extensive use of both forms of knowledge. Many problems in engineering and other areas can provide a learning system with an incomplete domain theory and a limited set of examples. Knowledge-based learning uses knowledge in both forms to learn knowledge missing from the domain theory.","The knowledge-based learning approach is illustrated with two systems, KBL0 and KBL1. These systems have been designed and implemented to work with domains requiring a representation of real numbers and mathematical formulas, such as engineering.","This research has shown, not only that it is possible to use a domain theory to guide induction using examples, but that when there are few examples available compared to the size of the problem space, the resulting rules are more accurate and stable than those from pure empirical techniques. In addition, knowledge-based learning algorithms free the user from selecting relevant examples and attributes for learning by using an incomplete domain theory to determine where knowledge needs to be added. A problem unsolved by the current domain knowledge helps to determine where new knowledge needs to be incorporated into the domain theory and what the context is for the learning. The context is used to select relevant examples from an example base and to reduce the number of attributes used during induction. With the control structure provided by knowledge-based systems, inductive learning can be used to extend an existing knowledge base.","Made available in DSpace on 2011-05-07T13:36:34Z (GMT). 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