Part-Whole Reasoning in an Object-Centered Framework [electronic resource] / by Patrick Lambrix.
Material type: TextSeries: Lecture Notes in Computer Science ; 1771Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2000Description: XII, 200 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540464402Subject(s): Computer science | Software engineering | Artificial intelligence | Computer Science | Artificial Intelligence (incl. Robotics) | Programming Techniques | Mathematical Logic and Formal Languages | Software EngineeringAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q334-342TJ210.2-211.495Online resources: Click here to access onlineCurrent library | Home library | Call number | Materials specified | URL | Status | Date due | Barcode |
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IMSc Library | IMSc Library | Link to resource | Available | EBK5787 |
Background -- Description Logics -- Composite Objects -- Part-of in Description Logics -- Theory -- A Framework for Part-of Reasoning in Description Logics -- Extending the Framework -- Comparison of the Framework with Other Approaches -- Application Areas -- Domain Modeling in an Agent-Oriented Application -- Document Management -- Learning Composite Concepts -- Application -- Document Search Using Content, Structure and Properties -- Conclusion -- Conclusion.
In this book, the author develops an object-centered framework with specialized support of the part-of relation based on description logics. These logics are a family of object-centered knowledge representation languages tailored for describing knowledge about concepts and is-a hierarchies of these concepts. In addition to the representation and reasoning facilities provided by description logics for is-a, representation and reasoning facilities are introduced for part-of. Finally, the feasibility and the usefulness of the approach is demonstrated by applying the framework to various areas including domain modeling, agent-oriented scenarios, document management and retrieval, and composite concept learning.
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