A logic data model for the machine representation of knowledge
DLOG is a logic-based data model developed to show how logic-programming can combine contributions of Data Base Management (DBM) and Artificial Intelligence (AI). The DLOG specification includes a language syntax, a proof (or query evaluation) procedure, a description of the language's semantic...
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ndltd-UBC-oai-circle.library.ubc.ca-2429-257992018-01-05T17:43:19Z A logic data model for the machine representation of knowledge Goebel, Randy Logic -- Data processing Prolog (Computer program language) DLOG is a logic-based data model developed to show how logic-programming can combine contributions of Data Base Management (DBM) and Artificial Intelligence (AI). The DLOG specification includes a language syntax, a proof (or query evaluation) procedure, a description of the language's semantics, and a specification of the relationships between assertions, queries, and application databases. DLOG's data description language is the Horn clause subset of first order logic [Kowalski79, Kowalski81], augmented with descriptive terms and non-Horn integrity constraints. The descriptive terms are motivated by AI representation language ideas, specifically, the descriptive terms of the KRL language [Bobrow77]. A similar facility based on logical descriptions is provided in DLOG. DLOG permits the use of definite and indefinite descriptions of individuals and sets in queries and assertions. The meaning of DLOG's extended language is specified as Horn clauses that describe the relation between the basic language and the extensions. The experimental implementation is a Prolog program derived from that specification. The DLOG implementation relies on an extension to the standard Prolog proof procedure. This includes a "unification" procedure that matches embedded terms by recursively invoking the DLOG proof procedure (cf. LOGLISP [Robinson82]). The experimental system includes Prolog implementations of traditional database facilities (e.g., transactions, integrity constraints, data dictionaries, data manipulation language facilities), and an idea for using logic as the basis for heuristic interpretation of queries. This heuristic uses a notion of partial, match or sub-proof to produce assumptions under which plausible query answers can be derived. The experimental DLOG knowledge base management system is exercised by describing an undergraduate degree program. The example application is a description of the Bachelor of Computer Science degree requirements at The University of British Columbia. This application demonstrates how DLOG's descriptive terms provide a concise description of degree program knowledge, and how that knowledge is used to specify student programs and select program options. Science, Faculty of Computer Science, Department of Graduate 2010-06-16T20:47:41Z 2010-06-16T20:47:41Z 1985 Text Thesis/Dissertation http://hdl.handle.net/2429/25799 eng For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use. University of British Columbia |
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English |
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Logic -- Data processing Prolog (Computer program language) |
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Logic -- Data processing Prolog (Computer program language) Goebel, Randy A logic data model for the machine representation of knowledge |
description |
DLOG is a logic-based data model developed to show how logic-programming can combine contributions of Data Base Management (DBM) and Artificial Intelligence (AI). The DLOG specification includes a language syntax, a proof (or query evaluation) procedure, a description of the language's semantics,
and a specification of the relationships between assertions, queries, and application databases. DLOG's data description language is the Horn clause subset of first order logic [Kowalski79, Kowalski81], augmented with descriptive terms and non-Horn integrity constraints. The descriptive terms are motivated by AI representation language ideas, specifically, the descriptive terms of the KRL language [Bobrow77]. A similar facility based on logical descriptions is provided in DLOG. DLOG permits the use of definite and indefinite descriptions of individuals and sets in queries and assertions. The meaning of DLOG's extended language is specified as Horn clauses that describe the relation between the basic language and the extensions. The experimental implementation is a Prolog program derived from that specification. The DLOG implementation relies on an extension to the standard Prolog proof procedure. This includes a "unification" procedure that matches embedded terms by recursively invoking the DLOG proof procedure (cf. LOGLISP [Robinson82]). The experimental system includes Prolog implementations of traditional database facilities (e.g., transactions, integrity constraints, data dictionaries, data manipulation language facilities), and an idea for using logic as the basis for heuristic interpretation of queries. This heuristic uses a notion of partial, match or sub-proof to produce assumptions under which plausible query answers can be derived. The experimental DLOG knowledge base management system is exercised by describing an undergraduate degree program. The example application is a description of the Bachelor of Computer Science degree requirements at The University of British Columbia. This application demonstrates how DLOG's descriptive terms provide a concise description of degree program knowledge, and how that knowledge is used to specify student programs and select program options. === Science, Faculty of === Computer Science, Department of === Graduate |
author |
Goebel, Randy |
author_facet |
Goebel, Randy |
author_sort |
Goebel, Randy |
title |
A logic data model for the machine representation of knowledge |
title_short |
A logic data model for the machine representation of knowledge |
title_full |
A logic data model for the machine representation of knowledge |
title_fullStr |
A logic data model for the machine representation of knowledge |
title_full_unstemmed |
A logic data model for the machine representation of knowledge |
title_sort |
logic data model for the machine representation of knowledge |
publisher |
University of British Columbia |
publishDate |
2010 |
url |
http://hdl.handle.net/2429/25799 |
work_keys_str_mv |
AT goebelrandy alogicdatamodelforthemachinerepresentationofknowledge AT goebelrandy logicdatamodelforthemachinerepresentationofknowledge |
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1718592905094889472 |