One of the important problems in data mining is the evaluation of subjective interestingness of the discovered rules. Past research has found that in many real-life applications it is easy to generate a large number of rules from the database, but most of the rules are not useful or interesting to the user. Due to the large number of rules, it is difficult for the user to analyze them manually in order to identify those interesting ones. Whether a rule is of interest to a user depends on his/her existing knowledge of the domain, and his/her interests. In this paper, we propose a technique that analyzes the discovered rules against a specific type of existing knowledge, which we call general impressions, to help the user identify interesting rules. We first propose a representation language to allow general impressions to be specified. We then present some algorithms to analyze the discovered classification rules against a set of general impressions. The results of the analysis tell us ...

Association rules are a class of important regularities in databases. They are found to be very useful in practical applications. However, association rule mining algorithms tend to produce a huge number of rules, most of which are of no interest to the user. Due to the large number of rules, it is very difficult for the user to analyze them manually in order to identify those truly interesting ones. In this paper, we propose a new approach to assist the user in finding interesting rules (in particular, unexpected rules) from a set of discovered association rules. This technique is characterized by analyzing the discovered association rules using the user's existing knowledge about the domain and then ranking the discovered rules according to various interestingness criteria, e.g., conformity and various types of unexpectedness. This technique has been implemented and successfully used in a number of applications. Keywords: subjective interestingness, association rules, interestingness analysis in data mining. 1.

The success of large software engineering projects depends critically on the specification, which must represent the requirements of a large number of people with widely differing perspectives. Conventional approaches to software engineering do not address the process of identifying and integrating these perspectives, but instead concentrate on the maintenance of a single consistent description. This results in a specification which represents only one point of view, often the analyst's, excluding suggestions which do not fit with this view. The processes which led to the adoption of this point of view will go unrecorded, making any rationale attached to such a specification incomplete. Other participants will not be able to validate it properly, as it does not relate to their requirements. This thesis integrates ideas drawn from the study of knowledge acquisition, computer-supported co-operative work and negotiation into a model of the specification activity which allows the capture ...

An architecture for knowledge acquisition systems is proposed based upon the integration of existing methodologies, techniques and tools developed within the knowledge acquisition, machine learning, expert systems, hypermedia and knowledge representation research communities. Existing tools are analyzed within a common framework to show that their integration can be achieved in a natural and principled fashion. A detailed architecture for integrated knowledge acquisition systems is proposed that also derives from parallel cognitive and theoretical studies. 1 INTRODUCTION The past decade has seen an explosion in research on, and application of, knowledge acquisition methodologies, techniques and tools (Marcus, 1988; Boose & Gaines, 1988, 1990; Gaines & Boose, 1988; Boose, 1989). The knowledge acquisition community world-wide has grown in numbers and scope of projects. There are significant international collaborative developments involving the sharing of ideas and software. The problem ...

This paper reports on the development of a realistic knowledge-based application using the MOBAL system. Some problems and requirements resulting from industrial-caliber tasks are formulated. A step-by-step account of the construction of a knowledge base for such a task demonstrates how the interleaved use of several learning algorithms in concert with an inference engine and a graphical interface can fulfill those requirements. Design, analysis, revision, refinement and extension of a working model are combined in one incremental process. This illustrates the balanced cooperative modeling approach. The case study is taken from the telecommunications domain and more precisely deals with security management in telecommunications networks. MOBAL would be used as part of a security management tool for acquiring, validating and refining a security policy. The modeling approach is compared with other approaches, such as KADS and stand-alone machine learning. What online ML can do for KA -...

In order to help the knowledge engineer and the expert during knowledge acquisition phase, the ACACIA Group is working on a knowledge acquisition methodology and tool (KATEMES) allowing knowledge acquisition from mul-tiple experts, exploiting the specificities of design problems and preparing the assistance to the end-user and the quality of explanations he will be provided with. This paper describes our research program. After a brief description of our previous knowledge acquisition tool 3DKAT, we will present the prim-itives of KATEMES and the problems we intend to study and the ideas we intend to deepen about the link between knowledge acquisition and explana-tions, knowledge acquisition from multiple experts and methodological aspects.

One of the key phases in Knowledge Based Systems (KBS) construction is Knowledge Acquisition. However, human knowledge about domains is so complex that without an analysis stage that probes the underlying nature of the real world problem and how human experts conceptualise it, the knowledge incorporated within a KBS remains shallow and incomplete. In this paper, we highlight foundational details of a structured approach to knowledge analysis and describe its application to domains associated with software installation and neural networks. 1 Introduction: Domain Characterisation and Knowledge Acquisition In this paper we present the foundations of an approach to the characterisation of problem solving domains for the development of knowledge based systems. These foundations come from a broadly based, multi-disciplinary perspective which includes the cognitive sciences, computer science and the philosophy of science. The breadth of disciplines required reflects the need to deal with the...

: The strengths and weaknesses of our earlier system, KEATS-1, have led us to embark upon the design and implementation of a new knowledge engineering environment, KEATS-2, which provides a novel, integrated framework for performing both bottom-up and top-down knowledge acquisition. In this paper we discuss the nature of the knowledge acquisition activities and we introduce the support tools embedded in KEATS-2. We characterize knowledge acquisition as the composition of knowledge elicitation, data analysis and domain conceptualization and we emphasize that a knowledge engineering tool has to support these activities as well as bridging the gap between acquiring the data and implementing the final system. Acknowledgement: This research is supported by a grant from British Telecommunications, plc. Steven Rose and Mike Stewart of the Open University's Brain Research Group provided valuable domain expertise. 1. THE PROBLEM OF KNOWLEDGE ACQUISITION The most popular principle in knowledge...

First generation expert systems rely on the use of surface knowledge, such as associational or heuristic. This knowledge is typically acquired from domain experts through exhaustive knowledge engineering sessions. On the other hand, second generation knowledge acquisition technology is characterized by two main features: the use of deep knowledge and machine learning. In the paper we review three second generation methods that partially automate the knowledge acquisition process: inductive learning of rules from examples, model-based rule learning, and qualitative model acquisition. Results of their application to some medical domains are presented. Finally, we outline different stages of expert system development. An extended expert system shell schema is presented which includes a knowledge acquisition and a knowledge explanation module. 1 Introduction Knowledge acquisition is a field of artificial intelligence concerned with the development of methods, techniques and tools for buil...

We compare the process of knowledge based system development to the software engineering approach of the more traditional application development. Many similarities can be identified in the process structures. The most important differences are in the knowledge acquisition and conceptual modelling of the KBS which have no direct counterpart in the traditional software life cycle. We also study the cooperation of the KBS development environment and ordinary CASE tools. We develop a scheme to support this cooperation. The method is based on transformations between KBS development tools and the repository supporting the CASE toolset. For the case where no repository is available, the scheme can be modified to use transformations between each KBS development tool and the corresponding CASE tool. Since the phase structures of a KBS project and a SE project ar...