Domain-driven knowledge modelling for knowledge acquisition

Knowledge modelling is undoubtedly a major problem in knowledge acquisition. Drawing from industrial case studies that have been carried out, the paper lists some key problems which still dog knowledge modelling. Next, it critically reviews current knowledge modelling techniques and tools and concludes that these real knowledge acquisition issues are not tackled by them. We consider the spelling out of these problems and the fact that they are not addressed by current tools and techniques to be a major contribution of this paper. The paper strongly argues for knowledge modelling to be domain-driven, i.e. driven by the nature of the domain being modelled. The key argument in this paper is that ignoring the nature or characterization of the domain inevitably results in knowledge imposition rather than knowledge acquisition as domains get shoe-horned into some (current) set of models, representations and tools. After examining the nature of domains, the paper proceeds to outline an emerging hypothesis for knowledge modelling. It concludes with a specification of a tool suite for addressing the issues identified in this paper.     

A framework for knowledge base refinement through multistrategy learning and knowledge acquisition

This paper presents a general approach to knowledge base refinement which integrates multistrategy learning, active experimentation and guided knowledge elicitation. Three main features characterize this approach. First, knowledge base refinement is based on a multistrategy learning method that dynamically integrates the elementary inferences (such as deduction, analogy, abduction, generalization, specialization, abstraction and concretion) that are employed by the single-strategy learning methods. Second, much of the knowledge needed by the system to refine its knowledge base is generated by the system itself. Therefore, most of the time, the human expert will need only to confirm or reject system-generated hypotheses. Third, the knowledge base refinement process is efficient due to the ability of the multistrategy learner to reuse its reasoning process. The paper illustrates a cooperation between a learning system and a human expert in which the learner performs most of the tasks and the expert helps it in solving the problems that are intrinsically difficult for a learner and relatively easy for an expert.     

Metatool support for custom-tailored,domain-oriented knowledge acquisition

Metatool support for knowledge acquisition is an approach to automate the implementation of domain-specific knowledge-acquisition tools. Dots is a metatool that can be used by developers to generate domain-specific knowledge-acquisition tools. Whenever a domain model useful for expressing the relevant expertise can be established, developers can use Dots to specify and generate a knowledge-acquisition environment for development of expert systems. Dots assumes that the knowledge-acquisition tools generated are based on the knowledge-elicitation technique of graphical knowledge editing. A salient aspect of Dots is that no particular domain, task or problem-solving method is presupposed by the metatool. We achieve this generalization by introducing an abstract-architecture view—that is, an architectural model of the target knowledge-acquisition tool—as the framework for specifying target knowledge-acquisition tools. Dots provides facilities for editing this abstract architecture and for instantiating knowledge-acquisition tools from it.     

Focus groups as a group knowledge acquisition technique

Architectures and methodologies provide knowledge acquisition researchers and practitioners broad working frameworks within which to explore a variety of questions. However, in order to pursue the development of robust reasoning systems rigorously with reasonable expectations of the development process, empirical assessments of the knowledge acquisition process and of heuristic inputs to the process are essential. In this paper we discuss an experiment whose results support the use of a group knowledge acquisition technique in combination with other knowledge acquisition techniques.     

Underlying assumptions of knowledge acquisition and machine learning

The paradigm of knowledge-based systems has become of practical interest to a broad variety of persons: software engineers, knowledge engineers and domain experts. Therefore, it becomes necessary to make explicit the underlying assumptions of the field. In this paper, the terms “knowledge” and “modeling” as they occur in texts on knowledge acquisition and machine learning are investigated. It is shown that the terms are used with very different meanings corresponding to different views of knowledge acquisition. The transfer view, the performance or building-blocks view, the knowledge-level or stepwise refinement view, and the constructive view of knowledge and its acquisition are described. The implications for designing systems which support a user in constructing a knowledge base are indicated. In particular, it is stressed that systems must support revisions of all modeling decisions if we want to prevent from the next bottleneck, the bottleneck of knowledge-base maintenance.     

Design knowledge acquisition: task analysis and a partial implementation

Design is conceptualized as an ill-structured process that requires diverse knowledge that is hard to acquire. Systematic analysis of design and the knowledge requirements it has in general and in the context of bridge design, shows that the knowledge needed can be semi-automatically acquired by using machine learning techniques. Although there are limitations to the approach, preliminary results in the bridge design domain are promising and can potentially transfer to other design domains.     

Self-efficacy,information-processing strategies,and acquisition of health knowledge

Informed by the Cognitive Mediation Model of media learning, this study explores how self-efficacy and information-processing strategies jointly impact the learning of health knowledge. Using survey data (N=1409), the study examines the roles that self-efficacy, motivation of media use, news attention, and elaboration play in acquiring knowledge about swine flu during the 2009 global pandemic crisis. Results support the hypothesized relationships among self-efficacy, motivation, attention to and elaboration of swine flu news, and knowledge about the flu. Implications of the findings to advance the research in mediated cognitive learning are discussed.     

Linked-learning for knowledge acquisition: a pilot's associate case study

We developed a knowledge acquisition system that uses an Explanation-Based Learning domain theory as a knowledge repository from which general knowledge structures can be compiled and then translated by smart translators into the various specialized representations required for the separate expert system modules of a distributed pilot aiding system. We call this two-stage learning-plus-translation process linked learning. This architecture addresses learning for multiple modules with different knowledge representations and performance goals, but which must all perform together in an integrated fashion. It also addresses learning for an intelligent agent which must perform in a real-world, dynamically-changing environment with multiple sources of uncertainty. Finally, it serves as a case study offering insights into the integration of machine learning into the system engineering process for a large knowledge-based system development effort.     

Distributed knowledge acquisition for the automatic construction of integrated expert systems

The authors discuss the problem of distributed knowledge acquisition for the construction of complete and consistent databases in integrated expert systems via the sharing of knowledge sources of different topologies (experts, problem-oriented texts, and electronic media in the form of databases). The emphasis is on the models, methods, and algorithms of distributed knowledge acquisition from databases as additional knowledge sources. The authors describe the architecture and basic facilities of distributed knowledge acquisition, which function as a part of the AT-TECHNOLOGY tool complex.     

Capturing multiple perspectives: a user-centered approach to knowledge and design acquisition

Many efforts in knowledge acquisition are designed from a knowledge engineer's perspective and as a consequence fall short of allowing experts to elaborate successfully their own situated knowledge. Knowledge engineering approaches are typically not user-centered and consequently are often the cause of a bottleneck in system development. This paper describes and evaluates the Advanced Knowledge And Design Acquisition Methodology (AKADAM) project as an attempt to overcome such inadequacies by provision of user-centered knowledge acquisition techniques. Both theoretical and practical issues are examined. The role of multiple perspectives (i.e. "knowledge as rules", "knowledge as concepts", and "knowledge as designs"), their relationship to a user-centered approach, and the necessity of flexible knowledge integration are portrayed by applying AKADAM to a complex, real-world domain (i.e. the development of an electronic associate for fighter pilots). Results suggest that this approach is capable of providing: (a) a naturalistic knowledge elicitation environment endorsed by users, (b) an externalization of experts' intuitive knowledge in a form which is similar to their own mental representation and (c) an integrated, large-scale knowledge set suitable for infusing knowledge into AI architectures and human-computer interface design.     

Towards promotion and dissemination of indigenous knowledge: A case of NIRD

This paper describes what indigenous knowledge (IK) is and why IK is important. This paper highlights the concepts of rural technology and indigenous knowledge. It also explains the initiatives of the National Institute of Rural Development (NIRD) towards development, promotion and dissemination of IK to the rural community to improve their quality of life. Explained in brief are the practices of IK at NIRD like traditional mud houses, handmade paper, vermi compost, medicinal plants nursery, etc. Among the issues covered are collection, recording and documenting, storage, problems, intellectual property rights, documentation and dissemination of IK, and the role of ICTs in this regard. Finally, it also proposes a digital information resources base on IK.     

Untangling the web of e-Research: Towards a sociology of online knowledge

e-Research is a rapidly growing research area, both in terms of publications and in terms of funding. In this article we argue that it is necessary to reconceptualize the ways in which we seek to measure and understand e-Research by developing a sociology of knowledge based on our understanding of how science has been transformed historically and shifted into online forms. Next, we report data which allows the examination of e-Research through a variety of traces in order to begin to understand how knowledge in the realm of e-Research has been and is being constructed. These data indicate that e-Research has had a variable impact in different fields of research. We argue that only an overall account of the scale and scope of e-Research within and between different fields makes it possible to identify the organizational coherence and diffuseness of e-Research in terms of its socio-technical networks, and thus to identify the contributions of e-Research to various research fronts in the online production of knowledge.     

Towards promotion and dissemination of indigenous knowledge: A case of NIRD

This paper describes what indigenous knowledge (IK) is and why IK is important. This paper highlights the concepts of rural technology and indigenous knowledge. It also explains the initiatives of the National Institute of Rural Development (NIRD) towards development, promotion and dissemination of IK to the rural community to improve their quality of life. Explained in brief are the practices of IK at NIRD like traditional mud houses, handmade paper, vermi compost, medicinal plants nursery, etc. Among the issues covered are collection, recording and documenting, storage, problems, intellectual property rights, documentation and dissemination of IK, and the role of ICTs in this regard. Finally, it also proposes a digital information resources base on IK.