Generating Knowledge-Based System Generators

2010 ◽  
Vol 6 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Sabine Moisan
Keyword(s):  
Software Engineering ◽  
Model Calibration ◽  
Classification Model ◽  
Knowledge Based Systems ◽  
Significant Gain ◽  
Knowledge Based System ◽  
Domain Specific ◽  
Knowledge Based ◽  
Inference Engines ◽  
Component Frameworks

This article investigates software engineering techniques for designing and reengineering knowledge-based system generators, focusing on inference engines and domain specific languages. Indeed, software development of knowledge-based systems is a difficult task. We choose a software engineering approach to favor code reuse, evolution, and maintenance. We propose a software platform named Lama to design the different elements necessary to produce a knowledge-based system. This platform offers software toolkits (mainly component frameworks) to build interfaces, inference engines, and expert languages. We have used the platform to build several KBS generators for various tasks (planning, classification, model calibration) in different domains. The approach appears well fitted to knowledge-based system generators; it allows developers a significant gain in time, as well as it improves software readability and safeness.

Generating Knowledge-Based System Generators

2012 ◽  
pp. 1-18
Author(s):  
Sabine Moisan
Keyword(s):  
Software Engineering ◽  
Classification Model ◽  
Knowledge Based Systems ◽  
Significant Gain ◽  
Knowledge Based System ◽  
Domain Specific ◽  
Engineering Approach ◽  
Knowledge Based ◽  
Inference Engines ◽  
Component Frameworks

This paper investigates software engineering techniques for designing and reengineering knowledge-based system generators, focusing on inference engines and domain specific languages. Indeed, software development of knowledge-based systems is a difficult task. We choose a software engineering approach to favor code reuse, evolution, and maintenance. We propose a software platform named Lama to design the different elements necessary to produce a knowledge-based system. This platform offers software toolkits (mainly component frameworks) to build interfaces, inference engines, and expert languages. We have used the platform to build several KBS generators for various tasks (planning, classification, model calibration) in different domains. The approach appears well fitted to knowledge-based system generators; it allows developers a significant gain in time, as well as it improves software readability and safeness.

scholarly journals Software-engineering challenges of building and deploying reusable problem solvers

2009 ◽  
Vol 23 (4) ◽  
pp. 339-356 ◽  
Author(s):  
Martin J. O'Connor ◽  
Csongor Nyulas ◽  
Samson Tu ◽  
David L. Buckeridge ◽  
Anna Okhmatovskaia ◽  
...  
Keyword(s):  
Software Engineering ◽  
Domain Knowledge ◽  
Public Libraries ◽  
Conceptual Modeling ◽  
Building Blocks ◽  
Knowledge Based Systems ◽  
Knowledge Based ◽  
Application Systems ◽  
Structuring Knowledge ◽  
Problem Solvers

AbstractProblem solving methods (PSMs) are software components that represent and encode reusable algorithms. They can be combined with representations of domain knowledge to produce intelligent application systems. A goal of research on PSMs is to provide principled methods and tools for composing and reusing algorithms in knowledge-based systems. The ultimate objective is to produce libraries of methods that can be easily adapted for use in these systems. Despite the intuitive appeal of PSMs as conceptual building blocks, in practice, these goals are largely unmet. There are no widely available tools for building applications using PSMs and no public libraries of PSMs available for reuse. This paper analyzes some of the reasons for the lack of widespread adoptions of PSM techniques and illustrate our analysis by describing our experiences developing a complex, high-throughput software system based on PSM principles. We conclude that many fundamental principles in PSM research are useful for building knowledge-based systems. In particular, the task–method decomposition process, which provides a means for structuring knowledge-based tasks, is a powerful abstraction for building systems of analytic methods. However, despite the power of PSMs in the conceptual modeling of knowledge-based systems, software engineering challenges have been seriously underestimated. The complexity of integrating control knowledge modeled by developers using PSMs with the domain knowledge that they model using ontologies creates a barrier to widespread use of PSM-based systems. Nevertheless, the surge of recent interest in ontologies has led to the production of comprehensive domain ontologies and of robust ontology-authoring tools. These developments present new opportunities to leverage the PSM approach.

Knowledge Extraction from Domain-Specific Documents

2017 ◽  
pp. 106-123
Author(s):  
Ram Kumar ◽  
Shailesh Jaloree ◽  
R. S. Thakur
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Knowledge Bases ◽  
Domain Ontology ◽  
Knowledge Based Systems ◽  
Domain Specific ◽  
Knowledge Based ◽  
Representation Systems ◽  
Terminological Confusion ◽  
Knowledge Representation Systems

Knowledge-based systems have become widespread in modern years. Knowledge-base developers need to be able to share and reuse knowledge bases that they build. As a result, interoperability among different knowledge-representation systems is essential. Domain ontology seeks to reduce conceptual and terminological confusion among users who need to share various kind of information. This paper shows how these structures make it possible to bridge the gap between standard objects and Knowledge-based Systems.

A knowledge-based system for multi-disciplinary conceptual design synthesis

2012 ◽  
Vol 226 (12) ◽  
pp. 2950-2966 ◽  
Author(s):  
Ze-Lin Liu ◽  
Yong Chen ◽  
You-Bai Xie
Keyword(s):  
Conceptual Design ◽  
Design Synthesis ◽  
Synthesis Algorithm ◽  
Design Task ◽  
Knowledge Based System ◽  
Domain Specific ◽  
Knowledge Based ◽  
Viable Approach ◽  
Prototype Software

Exploring wide multi-disciplinary solution spaces to create conceptual design solutions is a difficult task for human designers due to lack of sufficient multi-disciplinary knowledge. A viable approach would be to develop a computer-aided system to synthesize the wide variety of knowledge for a given design task. However, the existing design synthesis systems are mainly domain-specific, focusing on conceptual design synthesis in a single or few limited disciplines. Therefore, this article introduces the development of a knowledge-based system for multi-disciplinary conceptual design synthesis, including the establishment of a knowledge base for organizing multi-disciplinary principle solutions and a design synthesis algorithm. The implementation of a prototype software is also reported, with the conceptual design of a solar fountain as a demonstrative case. The results of the case study show that the system can automatically and conveniently generate multi-disciplinary conceptual solutions.

scholarly journals Terminology and the construction of ontology

Terminology ◽  
2005 ◽  
Vol 11 (1) ◽  
pp. 55-81 ◽  
Author(s):  
Lee Gillam ◽  
Mariam Tariq ◽  
Khurshid Ahmad
Keyword(s):  
Knowledge Management ◽  
Information Retrieval ◽  
Linguistic Analysis ◽  
Statistical Techniques ◽  
Knowledge Based Systems ◽  
Arbitrary Domain ◽  
Domain Specific ◽  
Knowledge Based ◽  
Ontology Design ◽  
Conceptual Hierarchies

This paper discusses a method for corpus-driven ontology design: extracting conceptual hierarchies from arbitrary domain-specific collections of texts. These hierarchies can form the basis for a concept-oriented (onomasiological) terminology collection, and hence may be used as the basis for developing knowledge-based systems using ontology editors. This reference to ontology is explored in the context of collections of terms. The method presented is a hybrid of statistical and linguistic techniques, employing statistical techniques initially to elicit a conceptual hierarchy, which is then augmented through linguistic analysis. The result of such an extraction may be useful in information retrieval, knowledge management, or in the discipline of terminology science itself.

ELIMINATING REDUNDANCY, CONFLICT, AND INCOMPLETENESS FROM KNOWLEDGE-BASED SYSTEMS

1996 ◽  
Vol 06 (04) ◽  
pp. 673-697 ◽  
Author(s):  
ROSE F. GAMBLE ◽  
TERESA M. SHAFT
Keyword(s):  
Software Engineering ◽  
Structural Properties ◽  
Knowledge Based Systems ◽  
Program Derivation ◽  
Knowledge Based ◽  
Formal Program ◽  
The Subject

The reliability of knowledge-based systems (KBSs) has been the subject of a great deal of recent research. Much of this research focuses on the verification of KBSs, specifically to eliminate redundant rules, conflicting rules, and to ensure that the KBS is complete. Often, verification is approached by testing the structural properties of the KBS after the rules have been defined. In this paper we take a different approach and show how the process of formal program derivation from software engineering can be applied to KBSs. The use of these techniques eliminates the need for post-development verification because program derivation guarantees that the KBS will not contain redundant rules, conflicting rules, or be incomplete. As such, verification concerns are addressed during development.

Two Case-Based Systems for Explaining Exceptions in Medicine

2011 ◽  
pp. 227-249
Author(s):  
Rainer Schmidt
Keyword(s):  
Medical Practice ◽  
Knowledge Based Systems ◽  
Medical Studies ◽  
Knowledge Source ◽  
Knowledge Based System ◽  
Therapy Recommendations ◽  
Knowledge Based ◽  
Theoretical Hypothesis ◽  
Two Systems ◽  
Case Based

In medicine, a lot of exceptions usually occur. In medical practice and in knowledge-based systems, it is necessary to consider them and to deal with them appropriately. In medical studies and in research, exceptions shall be explained. In this chapter, we present two systems that deal with both sorts of these situations. The first one, called ISOR-1, is a knowledge-based system for therapy support. It does not just compute therapy recommendations, but it especially investigates therapy inefficacy. The second system, ISOR-2, is designed for medical studies or research. It helps to explain cases that contradict a theoretical hypothesis. Both systems are working in close co-operation with the user, who is not just considered as knowledge provider to build the system but is incorporated as additional knowledge source at runtime. Within a dialogue between the doctor and the system solutions respectively explanations are searched.

Structured models to assist in justifying investment in knowledge-based systems

1997 ◽  
Vol 211 (8) ◽  
pp. 579-589
Author(s):  
K Oldham ◽  
A K Kochhar ◽  
R M Hather ◽  
J Halton
Keyword(s):  
Life Cycle ◽  
Business Case ◽  
Knowledge Based Systems ◽  
Opportunity Identification ◽  
Knowledge Based System ◽  
System Models ◽  
Knowledge Based

This paper investigates the issues that should be considered before investing in knowledge-based systems. The desirability of having a knowledge-based system strategy is recognized and the issues associated with such a strategy are identified. Having defined the phases in the life-cycle of a knowledge-based system, models are proposed showing the activities and roles involved in the stages of opportunity identification and business case establishment. These models ensure that all relevant issues are addressed in a structured way. Particular attention is paid to the issues associated with people.

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