Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.657261
Title: Relational extensions to feature logic : applications to constraint based grammars
Author: Manandhar, S. K.
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1994
Availability of Full Text:
Full text unavailable from EThOS.
Please contact the current institution’s library for further details.
Abstract:
This thesis investigates the logical and computational foundations of unification-based or more appropriately constraint based grammars. The thesis explores extensions to feature logics (which provide the basic knowledge representation services to constraint based grammars) with multi- valued or relational features. These extensions are useful for knowledge representation tasks that cannot be expressed within current feature logics. The approach bridges the gap between concept languages (such as KL-ONE) which are the mainstay of knowledge representation languages in AI and feature logics. Various constraints on relational attributes are considered such as existential membership, universal membership, set descriptions, transitive relations and linear precedence constraints. The specific contributions of this thesis can be summarised as follows: 1. Development of an integrated feature/concept logic 2. Development of a constraint logic for so called partial set descriptions 3. Development of a constraint logic for expressing linear precedence constraints 4. The design of a constraint language CL-ONE that incorporates the central ideas provided by the above study. 5. A methodological study of the application of CL-ONE for constraint based grammars The thesis takes into account current insights in the areas of constraint logic programming, object-oriented languages, computational linguistics and knowledge representation.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.657261  DOI: Not available
Share: