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Knowledge Acquisition, Modeling and Management [electronic resource] :11th European Workshop, EKAW’99 Dagstuhl Castle, Germany, May 26–29, 1999 Proceedings /

Contributor(s): Fensel, Dieter [editor.] | Studer, Rudi [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 1621Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 1999.Description: XII, 412 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540487753.Subject(s): Computer science | Artificial intelligence | Computer Science | Artificial Intelligence (incl. Robotics)Online resources: Click here to access online
Contents:
Invited Papers -- Reengineering and Knowledge Management -- Knowledge Navigation in Networked Digital Libraries -- Long Papers -- Towards Brokering Problem-Solving Knowledge on the Internet -- TERMINAE: A Linguistics-Based Tool for the Building of a Domain Ontology -- Applications of Knowledge Acquisition in Experimental Software Engineering -- Acquiring and Structuring Web Content with Knowledge Level Models -- A Knowledge-Based News Server Supporting Ontology-Driven Story Enrichment and Knowledge Retrieval -- Modeling Information Sources for Information Integration -- Ontological Reengineering for Reuse -- Formally Verifying Dynamic Properties of Knowledge Based Systems -- Integration of Behavioural Requirements Specification within Knowledge Engineering -- Towards an Ontology for Substances and Related Actions -- Use of Formal Ontologies to Support Error Checking in Specifications -- The Ontologies of Semantic and Transfer Links -- Distributed Problem Solving Environment Dedicated to DNA Sequence Annotation -- Knowledge Acquisition from Multiple Experts Based on Semantics of Concepts -- Acquiring Expert Knowledge for the Design of Conceptual Information Systems -- A Constraint-Based Approach to the Description of Competence -- Short Papers -- Holism and Incremental Knowledge Acquisition -- Indexing Problem Solving Methods for Reuse -- Software Methodologies at Risk -- Knowledge acquisition of predicate argument structures from technical texts using Machine Learning: the system Asium -- An Interoperative Environment for Developing Expert Systems -- On the Use of Meaningful Names in Knowledge-Based Systems -- FMR: An Incremental Knowledge Acquisition System for Fuzzy Domains -- Applying SeSKA to Sisyphus III -- Describing Similar Control Flows for Families of Problem-Solving Methods -- Meta Knowledge for Extending Diagnostic Consultation to Critiquing Systems -- Exploitation of XML for Corporate Knowledge Management -- An Oligo-Agents System with Shared Responsibilities for Knowledge Management -- Veri-KoMoD: Verification of Knowledge Models in the Mechanical Design Field -- A Flexible Framework for Uncertain Expertise -- Elicitation of Operational Track Grids.
In: Springer eBooksSummary: Past, Present, and Future of Knowledge Acquisition This book contains the proceedings of the 11th European Workshop on Kno- edge Acquisition, Modeling, and Management (EKAW ’99), held at Dagstuhl Castle (Germany) in May of 1999. This continuity and the high number of s- missions re?ect the mature status of the knowledge acquisition community. Knowledge Acquisition started as an attempt to solve the main bottleneck in developing expert systems (now called knowledge-based systems): Acquiring knowledgefromahumanexpert. Variousmethodsandtoolshavebeendeveloped to improve this process. These approaches signi?cantly reduced the cost of - veloping knowledge-based systems. However, these systems often only partially ful?lled the taskthey weredevelopedfor andmaintenanceremainedanunsolved problem. This required a paradigm shift that views the development process of knowledge-based systems as a modeling activity. Instead of simply transf- ring human knowledge into machine-readable code, building a knowledge-based system is now viewed as a modeling activity. A so-called knowledge model is constructed in interaction with users and experts. This model need not nec- sarily re?ect the already available human expertise. Instead it should provide a knowledgelevelcharacterizationof the knowledgethat is requiredby the system to solve the application task. Economy and quality in system development and maintainability are achieved by reusable problem-solving methods and onto- gies. The former describe the reasoning process of the knowledge-based system (i. e. , the algorithms it uses) and the latter describe the knowledge structures it uses (i. e. , the data structures). Both abstract from speci?c application and domain speci?c circumstances to enable knowledge reuse.
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Invited Papers -- Reengineering and Knowledge Management -- Knowledge Navigation in Networked Digital Libraries -- Long Papers -- Towards Brokering Problem-Solving Knowledge on the Internet -- TERMINAE: A Linguistics-Based Tool for the Building of a Domain Ontology -- Applications of Knowledge Acquisition in Experimental Software Engineering -- Acquiring and Structuring Web Content with Knowledge Level Models -- A Knowledge-Based News Server Supporting Ontology-Driven Story Enrichment and Knowledge Retrieval -- Modeling Information Sources for Information Integration -- Ontological Reengineering for Reuse -- Formally Verifying Dynamic Properties of Knowledge Based Systems -- Integration of Behavioural Requirements Specification within Knowledge Engineering -- Towards an Ontology for Substances and Related Actions -- Use of Formal Ontologies to Support Error Checking in Specifications -- The Ontologies of Semantic and Transfer Links -- Distributed Problem Solving Environment Dedicated to DNA Sequence Annotation -- Knowledge Acquisition from Multiple Experts Based on Semantics of Concepts -- Acquiring Expert Knowledge for the Design of Conceptual Information Systems -- A Constraint-Based Approach to the Description of Competence -- Short Papers -- Holism and Incremental Knowledge Acquisition -- Indexing Problem Solving Methods for Reuse -- Software Methodologies at Risk -- Knowledge acquisition of predicate argument structures from technical texts using Machine Learning: the system Asium -- An Interoperative Environment for Developing Expert Systems -- On the Use of Meaningful Names in Knowledge-Based Systems -- FMR: An Incremental Knowledge Acquisition System for Fuzzy Domains -- Applying SeSKA to Sisyphus III -- Describing Similar Control Flows for Families of Problem-Solving Methods -- Meta Knowledge for Extending Diagnostic Consultation to Critiquing Systems -- Exploitation of XML for Corporate Knowledge Management -- An Oligo-Agents System with Shared Responsibilities for Knowledge Management -- Veri-KoMoD: Verification of Knowledge Models in the Mechanical Design Field -- A Flexible Framework for Uncertain Expertise -- Elicitation of Operational Track Grids.

Past, Present, and Future of Knowledge Acquisition This book contains the proceedings of the 11th European Workshop on Kno- edge Acquisition, Modeling, and Management (EKAW ’99), held at Dagstuhl Castle (Germany) in May of 1999. This continuity and the high number of s- missions re?ect the mature status of the knowledge acquisition community. Knowledge Acquisition started as an attempt to solve the main bottleneck in developing expert systems (now called knowledge-based systems): Acquiring knowledgefromahumanexpert. Variousmethodsandtoolshavebeendeveloped to improve this process. These approaches signi?cantly reduced the cost of - veloping knowledge-based systems. However, these systems often only partially ful?lled the taskthey weredevelopedfor andmaintenanceremainedanunsolved problem. This required a paradigm shift that views the development process of knowledge-based systems as a modeling activity. Instead of simply transf- ring human knowledge into machine-readable code, building a knowledge-based system is now viewed as a modeling activity. A so-called knowledge model is constructed in interaction with users and experts. This model need not nec- sarily re?ect the already available human expertise. Instead it should provide a knowledgelevelcharacterizationof the knowledgethat is requiredby the system to solve the application task. Economy and quality in system development and maintainability are achieved by reusable problem-solving methods and onto- gies. The former describe the reasoning process of the knowledge-based system (i. e. , the algorithms it uses) and the latter describe the knowledge structures it uses (i. e. , the data structures). Both abstract from speci?c application and domain speci?c circumstances to enable knowledge reuse.

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