{"title":"A multi-functional knowledge management system","authors":"D. Skuce","doi":"10.1006/KNAC.1993.1011","DOIUrl":"https://doi.org/10.1006/KNAC.1993.1011","url":null,"abstract":"Abstract We describe a general purpose knowledge management system, discussing its general goals and features, as well as its use in several very different applications. By \"multi-functional\", we mean having a wide variety of knowledge management functions such as debugging, formatting, and retrieval, and a wide variety of possible applications. The system, called CODE, functions primarily as a \"knowledge engineer's rapid prototyper\", or as a \"spreadsheet for ideas\"; one can experiment rapidly with relationships between concepts and obtain quick feedback on the desirability of changes and additions to a knowledge base. CODE's highly graphic interface permits experimentation with descriptions or definitions of concepts, which are arranged in an inheritance network using a very flexible inheritance mechanism. Several associated subsystems, such as a first order logic system and a simple natural language system, allow various types of syntactic and semantic checks to be performed if desired. We illustrate CODE's flexibility by describing three typical applications: in software engineering, terminology, and ontological design for knowledge-based systems.","PeriodicalId":100857,"journal":{"name":"Knowledge Acquisition","volume":"46 1","pages":"305-346"},"PeriodicalIF":0.0,"publicationDate":"1993-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80442134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Providing descriptive power to guided self-elicitation","authors":"G. G. Shephard","doi":"10.1006/KNAC.1993.1012","DOIUrl":"https://doi.org/10.1006/KNAC.1993.1012","url":null,"abstract":"Abstract Recent exploratory research developed and tested a guided self-elicitation (GSE) methodology. With GSE, an expert is enabled to capture his/her own performed expertise as production rule-instances. GSE is based on published cognitive research, using a production system view of conscious cognitive information processing and certain demonstrated human abilities: for identifying and categorizing perception, rehearsing and reconstituting prior thought processes and verbal reporting of concurrent cognitive information processing. Experimentally self-elicited decision analyst expertise (leading subjective probability assessment interviews) demonstrates that performed expertise can consist of complex rule-processed knowledge forms. An object model for representing complex knowledge forms is proposed and discussed.","PeriodicalId":100857,"journal":{"name":"Knowledge Acquisition","volume":"21 1","pages":"347-366"},"PeriodicalIF":0.0,"publicationDate":"1993-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85472903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Models: toward integrated knowledge modeling environments","authors":"M. Barbuceanu","doi":"10.1006/KNAC.1993.1010","DOIUrl":"https://doi.org/10.1006/KNAC.1993.1010","url":null,"abstract":"Abstract Building knowledge-based problem solvers requires an intellectually challenging modeling stage whose dominance over other activities is now widely recognized. In spite of this, current languages and environments leave the modeling activity on the shoulders of the human, concentrating on the routine programming aspect. Next generation languages and tools will have to explicitly support modeling in the first place. This paper presents a proposal for such a next generation knowledge modeling environment and discusses some steps we have made in this direction. Unlike existing programming environments, knowledge modeling environments focus on manipulating explicit, declarative specifications of problem-solving models which must be acquired, organized, modified, explained, validated, simulated and, eventually, translated into performance computer languages. Programming is only one of the activities supported in such an environment. This paper also discusses the knowledge modeling language we have developed as the foundation of the modeling environment. This language extends term classification technology with refinement, constraints, patterns and events, actions and methods, in order to support the description of both domain and control specifications required by problem-solving models. To substantiate the claims about the adequacy of the language, the paper presents two important modeling applications. The first is developing a full KADS language on top of it and the second is modeling a well known generic problem solving method, \"propose-and-revise\".","PeriodicalId":100857,"journal":{"name":"Knowledge Acquisition","volume":"131 1","pages":"245-304"},"PeriodicalIF":0.0,"publicationDate":"1993-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75820708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Attribute focusing: machine-assisted knowledge discovery applied to software production process control","authors":"I. Bhandari","doi":"10.1006/KNAC.1994.1014","DOIUrl":"https://doi.org/10.1006/KNAC.1994.1014","url":null,"abstract":"Abstract How can people who are not trained in data analysis discover knowledge from a database of attribute-valued data? I address this question by presenting a man-machine approach to knowledge discovery called Attribute Focusing and its application to software production process control. Attribute Focusing utilizes an automatic filter to focus attention on that small part of a large amount of data which is interesting. A person studies that part in a manner which leads him to discover knowledge about the physical situation to which the data pertain. Specifically, the paper describes: 1. A model of interestingness of data based on the magnitude of data values, the association of data values and basic knowledge of the limits of human processing. 2. The use of that model of interestingness by people to discover knowledge. 3. The application of the Attribute Focusing approach to diagnose and correct the software production process. Based on the results that have been observed, the paper concludes that man-machine approaches to knowledge discovery should be emphasized much more than has been in the past, and that Attribute Focusing is a powerful, practical approach to such discovery.","PeriodicalId":100857,"journal":{"name":"Knowledge Acquisition","volume":"20 1","pages":"271-294"},"PeriodicalIF":0.0,"publicationDate":"1993-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83632662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Knowledge acquisition in the small: building knowledge-acquisition tools from pieces","authors":"Jay T. Runkel, William P. Birmingham","doi":"10.1006/knac.1993.1009","DOIUrl":"https://doi.org/10.1006/knac.1993.1009","url":null,"abstract":"<div><p>The knowledge-systems community is interested in easing the knowledge-system development process. One approach, the <em>mechanisms</em> approach, views knowledge systems as a set of tasks, each of which can be realized by a computation mechanism. To be effective, knowledge-acquisition (KA) tools must be automatically configured once a set of mechanisms has been selected. We present a method for automatically generating a model-based KA tool for a given set of mechanisms. The method advocates combining <em>KA mechanisms</em>, which acquire knowledge in the small, and a set of strategies that provide a global view of the KA activity. We show that these global strategies are necessary for the KA tool to efficiently interact with a domain expert.</p></div>","PeriodicalId":100857,"journal":{"name":"Knowledge Acquisition","volume":"5 2","pages":"Pages 221-243"},"PeriodicalIF":0.0,"publicationDate":"1993-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/knac.1993.1009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72110780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A translation approach to portable ontology specifications","authors":"Thomas R. Gruber","doi":"10.1006/knac.1993.1008","DOIUrl":"https://doi.org/10.1006/knac.1993.1008","url":null,"abstract":"<div><p>To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse—definitions of classes, relations, functions, and other objects—is called an ontology. This paper describes a mechanism for defining ontologies that are portable over representation systems. Definitions written in a standard format for predicate calculus are translated by a system called Ontolingua into specialized representations, including frame-based systems as well as relational languages. This allows researchers to share and reuse ontologies, while retaining the computational benefits of specialized implementations.</p><p>We discuss how the translation approach to portability addresses several technical problems. One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content. Another is how to translate from a very expressive language into restricted languages, remaining system-independent while preserving the computational efficiency of implemented systems. We describe how these problems are addressed by basing Ontolingua itself on an ontology of domain-independent, representational idioms.</p></div>","PeriodicalId":100857,"journal":{"name":"Knowledge Acquisition","volume":"5 2","pages":"Pages 199-220"},"PeriodicalIF":0.0,"publicationDate":"1993-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/knac.1993.1008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72070953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Integrating conceptual and operational modeling: a case study","authors":"Marc Linster","doi":"10.1006/knac.1993.1006","DOIUrl":"https://doi.org/10.1006/knac.1993.1006","url":null,"abstract":"<div><p>We argue that it is important for the development of large knowledge models to integrate conceptual and operational modeling. We show that conceptual models can be operationalized by continuous refinement, without the need for a separate manual and structure-transforming implementation phase. Moreover, we show that such a continuity can be the basis for a fruitful integration of both kinds of modeling in a spiral development cycle. This allows us to integrate the best of both worlds: (1) the sloppiness required by conceptual modeling in order to develop structures unhampered by the constraints of an operational language; and (2) the feedback that an operational language provides for the ongoing model development process by allowing for testing, validating, and analysing the formalized structures of the model. To support our claims, we show how a large conceptual model of cancer-chemotherapy administration benefits from this integrating view on modeling.</p></div>","PeriodicalId":100857,"journal":{"name":"Knowledge Acquisition","volume":"5 2","pages":"Pages 143-171"},"PeriodicalIF":0.0,"publicationDate":"1993-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1006/knac.1993.1006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72110779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Active Glossary: taking integration seriously","authors":"G. Klinker, David Marques, J. McDermott","doi":"10.1006/KNAC.1993.1007","DOIUrl":"https://doi.org/10.1006/KNAC.1993.1007","url":null,"abstract":"Abstract Developing automated support for any workplace involves analysing a workplace, designing a problem-solving approach and knowledge base, populating that knowledge base with information required by the problem-solving approach, and introducing the new support into the workplace. Each of these development phases produces different components of the solution for supporting a workplace. Existing knowledge-acquisition tools support only a subset of the development phases, and the solution components they generate are not integrated: it is left to the developer to create and maintain a mapping between the different solution components resulting from the different development phases. A current trend in knowledge acquisition is to move towards coherent knowledge-engineering environments supporting the entire solution-development cycle. This emphasizes the need for tools that assist developers with integrating the different solution components produced by the knowledge-engineering environment into a coherent system. This paper introduces such an integration tool: the Active Glossary. The Active Glossary is part of the Spark, Burn, FireFighter knowledge-engineering environment. It assists a development team with describing workplaces and programming constructs so that their similarities and differences are made explicit. The result is an explicit mapping between the outcome of a workplace analysis and the design of a problem-solving approach. The Active Glossary further assists the development team with exploiting the similarities for the purpose of reusing previously defined workplace descriptions and programming constructs for new situations.","PeriodicalId":100857,"journal":{"name":"Knowledge Acquisition","volume":"54 1","pages":"173-197"},"PeriodicalIF":0.0,"publicationDate":"1993-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77609533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Formally specifying reusable knowledge model components","authors":"M. Aben","doi":"10.1006/KNAC.1993.1005","DOIUrl":"https://doi.org/10.1006/KNAC.1993.1005","url":null,"abstract":"This paper outlines some of the problems with using predefined building blocks to specify knowledge level models of problem solving, in particular in the context of the KADS methodology. The definition of the basic building blocks in KADS, the primitive inferences, or knowledge sources, often seems to be inadequate to aid the knowledge engineer in constructing an abstract model of problem solving. We argue that the informal, verbal way in which the building blocks are defined is the cause of this problem, and propose to formalize them to make their semantics clear and to assess the consequences of various modeling decisions. We discuss choices among different formalizations, and show in detail the formalization of one class of knowledge sources.","PeriodicalId":100857,"journal":{"name":"Knowledge Acquisition","volume":"2 1","pages":"119-141"},"PeriodicalIF":0.0,"publicationDate":"1993-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90092195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Integrating conceptual and operational modeling: a case study","authors":"M. Linster","doi":"10.1006/KNAC.1993.1006","DOIUrl":"https://doi.org/10.1006/KNAC.1993.1006","url":null,"abstract":"Abstract We argue that it is important for the development of large knowledge models to integrate conceptual and operational modeling. We show that conceptual models can be operationalized by continuous refinement, without the need for a separate manual and structure-transforming implementation phase. Moreover, we show that such a continuity can be the basis for a fruitful integration of both kinds of modeling in a spiral development cycle. This allows us to integrate the best of both worlds: (1) the sloppiness required by conceptual modeling in order to develop structures unhampered by the constraints of an operational language; and (2) the feedback that an operational language provides for the ongoing model development process by allowing for testing, validating, and analysing the formalized structures of the model. To support our claims, we show how a large conceptual model of cancer-chemotherapy administration benefits from this integrating view on modeling.","PeriodicalId":100857,"journal":{"name":"Knowledge Acquisition","volume":"108 1","pages":"143-171"},"PeriodicalIF":0.0,"publicationDate":"1993-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87589967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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