The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform
Richard V. Kelly - One of the best experts on this subject based on the ideXlab platform.
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Knowledge Representation: The Knowledge Engineer as Epistemologist
Practical Knowledge Engineering, 1991Co-Authors: Richard V. KellyAbstract:Publisher Summary This chapter discusses the concept of Knowledge representation. Knowledge representation is simply a way of organizing Knowledge inside an expert system. Representation refers to the packaging, storage, and manipulation of Knowledge in an expert system by methods called schemes. Just as the record and the relation are the schemes used in databases to package data, the frame, the rule, and their variations are the formats used in expert systems to bundle Knowledge. Selecting the appropriate Knowledge representation schemes to use in a given application is the first step in coding an expert system. The representation choices made at the start of a project influences tool selection, program performance, and the functionality of the system later on. However, choosing Knowledge representation schemes need first not be a gruelingly painful process. In many cases, the schemes simply fall out at the start of the project. Certain schemes become obvious when the application and the users' needs are understood.
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Knowledge Acquisition: The Knowledge Engineer as Cognitive Psychologist
Practical Knowledge Engineering, 1991Co-Authors: Richard V. KellyAbstract:Publisher Summary This chapter discusses the process of Knowledge acquisition. Knowledge acquisition is the process of interviewing an expert in a particular domain and then translating the Knowledge gleaned in those interviews into machine readable code. However, there is more to Knowledge acquisition than simple stenography. Knowledge acquisition requires that the Knowledge Engineer (KE) actually understand the domain the system is being built in, and also the motivations and needs of the people cooperating in the project. With this in mind, a KE should approach the Knowledge acquisition sessions in three phases—before, during, and after the interviews. Each phase has its important elements. Some understanding of the expert's incentive in cooperating in the system's development will be invaluable when future problems inevitably arise. Knowing why an expert is cooperating in the system's construction helps in resolving political problems that rear up late in a system's development. Just knowing that an expert sincerely backs the system's completion can go a long way toward saving a threatened project.
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Introduction: What Is Knowledge Engineering?
Practical Knowledge Engineering, 1991Co-Authors: Richard V. KellyAbstract:his chapter explains the term Knowledge Engineering” Knowledge Engineering is the task of building expert systems. A Knowledge Engineer (KE) is the person who does everything necessary to guarantee the success of an expert system development project, including initiation, management, coding, and maintenance of the system. Knowledge Engineering involves the entire process of expert system development, including acquisition, representation, prototyping, and delivery. A KE assumes a number of roles during a system's creation. On any given day, for example, a KE can be required to carry out one of the following tasks—prospect for suitable applications, explain Knowledge representation to project funders, code the integration linkages between an inference engine, a Knowledgebase and a database, demonstrate a well-developed prototype to users, interview domain experts,and plan the expansion of systems already in operation. As a result, KEs have become technological generalists. They straddle the line between the science of cognitive psychology and the art of programming.
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Beginning a Project: The Knowledge Engineer as Groundbreaker
Practical Knowledge Engineering, 1991Co-Authors: Richard V. KellyAbstract:Publisher Summary This chapter discusses the role of the Knowledge Engineer as a groundbreaker. Experienced Knowledge Engineers employ certain rules of thumb when they begin a new project. The more important of these rules are: (1) realization that not all problems can be solved by an expert system; (2) be perceived as a giver and not as a taker; (3) provide information, that is, avoid hype and tripe; (4) understand the problem to be tackled before making major purchases of hardware, software, or consulting services; and so on. An expert system is built to assist troubleshooters in diagnosing problems that have occurred on a telecommunications network. The system analyzes, diagnoses, and makes solution recommendations after reviewing errors and failures noted by the alarm-detection system already installed.
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Common Problems: The Knowledge Engineer as Thaumaturgist
Practical Knowledge Engineering, 1991Co-Authors: Richard V. KellyAbstract:Publisher Summary This chapter discusses the role of the Knowledge Engineer (KE) as thaumaturgist. The successful development of an expert system has as much to do with human ego as machine intelligence; as much to do with political intrigue as technological innovation; as much to do with base instinct as noble intellect or soaring creativity. Technical problems can make projects difficult. However, it is the human, rather than the technical side of developing expert systems that most often leads to their failure or underachievement. A KE trying to implement a commercial expert system ignores the odd complexities of human nature at his peril. An expert system cannot be built against the will of anyone on the development team. An expert system cannot be foisted on unwilling participants in a project. Consensus building is crucial. Stealth, guile, and subterfuge are no substitute, because ninja Knowledge Engineering does not work. Construction of an expert system has to be a cooperative enterprise.
Alicja Wakulicz-deja - One of the best experts on this subject based on the ideXlab platform.
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Exploration of rule-based Knowledge bases: A Knowledge Engineer’s support
Information Sciences, 2019Co-Authors: Agnieszka Nowak-brzezińska, Alicja Wakulicz-dejaAbstract:Abstract Data exploration helps us understand the investigated reality in a faster and better way. In this paper, the data to be explored are domain Knowledge bases with rules representation. The specificity of rule representation requires optimum selected analysis methods to provide useful new Knowledge to both the Knowledge Engineer and the user of a decision support system with a rule-based Knowledge base. Effective exploration of rule-based Knowledge bases can be carried out through the creation of if... then clusters of rules and their representatives using hierarchical methods. This is a new and unique approach to the managing of domain Knowledge bases as it facilitates the creation of cohesive and well-described clusters and the detection of rare rules (those dissimilar to other rules) while concurrently providing visualization of a Knowledge base. In the experiments, four Knowledge bases with a varying number of attributes and rules have been used. The Knowledge bases have been explored using four different methods of determining clusters’ representatives, four clustering methods and nine similarity measures. It turns out that each of the factors substantially influences to the size of the resulting clusters, the number of outliers and the occurrence frequency of overgeneral and overspecific rule clusters’ representatives.
Alicja Wakuliczdeja - One of the best experts on this subject based on the ideXlab platform.
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exploration of rule based Knowledge bases a Knowledge Engineer s support
Information Sciences, 2019Co-Authors: Agnieszka Nowakbrzezinska, Alicja WakuliczdejaAbstract:Abstract Data exploration helps us understand the investigated reality in a faster and better way. In this paper, the data to be explored are domain Knowledge bases with rules representation. The specificity of rule representation requires optimum selected analysis methods to provide useful new Knowledge to both the Knowledge Engineer and the user of a decision support system with a rule-based Knowledge base. Effective exploration of rule-based Knowledge bases can be carried out through the creation of if... then clusters of rules and their representatives using hierarchical methods. This is a new and unique approach to the managing of domain Knowledge bases as it facilitates the creation of cohesive and well-described clusters and the detection of rare rules (those dissimilar to other rules) while concurrently providing visualization of a Knowledge base. In the experiments, four Knowledge bases with a varying number of attributes and rules have been used. The Knowledge bases have been explored using four different methods of determining clusters’ representatives, four clustering methods and nine similarity measures. It turns out that each of the factors substantially influences to the size of the resulting clusters, the number of outliers and the occurrence frequency of overgeneral and overspecific rule clusters’ representatives.
Geoffrey I. Webb - One of the best experts on this subject based on the ideXlab platform.
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Integrating machine learning with Knowledge acquisition through direct interaction with domain experts
Knowledge-Based Systems, 1996Co-Authors: Geoffrey I. WebbAbstract:Knowledge elicitation from experts and empirical machine learning are two distinct approaches to Knowledge acquisition with differing and mutually complementary capabilities. Learning apprentices have provided environments in which a Knowledge Engineer may collaborate with a machine learning system allowing for a synergy between the complementary approaches. The Knowledge Factory is a Knowledge acquisition environment that allows a domain expert to collaborate directly with a machine learning system without the need for assistance from a Knowledge Engineer. This requires a different form of environment to the learning apprentice. The paper describes techniques for supporting such interactions and their implementation in a Knowledge acquisition environment called The Knowledge Factory.
Agnieszka Nowak-brzezińska - One of the best experts on this subject based on the ideXlab platform.
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Exploration of rule-based Knowledge bases: A Knowledge Engineer’s support
Information Sciences, 2019Co-Authors: Agnieszka Nowak-brzezińska, Alicja Wakulicz-dejaAbstract:Abstract Data exploration helps us understand the investigated reality in a faster and better way. In this paper, the data to be explored are domain Knowledge bases with rules representation. The specificity of rule representation requires optimum selected analysis methods to provide useful new Knowledge to both the Knowledge Engineer and the user of a decision support system with a rule-based Knowledge base. Effective exploration of rule-based Knowledge bases can be carried out through the creation of if... then clusters of rules and their representatives using hierarchical methods. This is a new and unique approach to the managing of domain Knowledge bases as it facilitates the creation of cohesive and well-described clusters and the detection of rare rules (those dissimilar to other rules) while concurrently providing visualization of a Knowledge base. In the experiments, four Knowledge bases with a varying number of attributes and rules have been used. The Knowledge bases have been explored using four different methods of determining clusters’ representatives, four clustering methods and nine similarity measures. It turns out that each of the factors substantially influences to the size of the resulting clusters, the number of outliers and the occurrence frequency of overgeneral and overspecific rule clusters’ representatives.
