Construction Of Knowledge-based Systems Research Articles

Study on the Modeling Method of Knowledge Base System in Web Environment

A knowledge model for knowledge base system in the Web environment is proposed, which includes problem description layer and knowledge layer. A knowledge base is made up of a set of knowledge models. Knowledge models are used to support knowledge representation and reasoning. Visual knowledge modeling tool and visual knowledge service tool based on Web environment realize the construction of knowledge base system in Web environment. The knowledge base system supports the construction of agricultural expert system. By comparison with CommonKADS, it is proved that this method can improve the efficiency of knowledge acquisition, management and maintenance in Web environment. This method also has the reference significance to the construction of the knowledge base system in the Web environment in other domains.

Soft Systems Methodology (SSM) as a Viable Methodology for Knowledge Engineering: A Literature Review

<span>This paper presents a baseline perspective of knowledge engineering (KE) methodologies by taking a critical look at the methodological approaches currently used in knowledge engineering domain. A literature review bordering on; the knowledge role concept, knowledge elicitation techniques, and ontological approaches employed in knowledge engineering for the construction of knowledge-based systems (KBSs) was carried out. Journal and conference articles were sourced from multiple journals and research databases and a multi-step manual cross-checking based on carefully selected extraction and quality criteria were employed. The findings of the study show that the existing methodological approaches employed for constructing KBSs in KE is highly deficient and inefficient for solving KE problems under dynamic and uncertain environments. The paper concludes by presenting a strong argument as to why soft systems methodology is best suited for constructing knowledge base systems (KBS) in a spatially distributed, unstructured and shared domain specific context.</span>

Knowledge Engineering: Survey of Methodologies, Techniques and Tools

This paper presents a broad overview of Knowledge Engineering (KE). This is a research area that aims to develop methodologies, techniques and tools to support the construction of knowledge-based systems, supporting management and dissemination of knowledge. The objective of this paper is to bring together, in a single document, important definitions and concepts of KE, presenting its history, main methodologies, tools and techniques found in the literature. Knowledge systems are increasingly needed, due to the constant growth of data and information. The integration and interoperability between these systems are challenges in this area, where KE can contribute through its available mechanisms.

A New Algorithm for the Acquisition of Knowledge from Scientific Literature in Specific Fields Based on Natural Language Comprehension

The acquisition of knowledge and the representation of that acquisition have always been viewed as the bottleneck in the construction of knowledge-based systems. The traditional methods of acquiring knowledge are based on knowledge engineering and communication with field experts. However, these methods cannot produce systematic knowledge effectively, automatically construct knowledge-based systems, or benefit knowledge reasoning. It has been noted that, in specific professional fields, experts often use fixed patterns to describe their expertise in the scientific articles that they publish. Abstracts and conclusions, for example, are key components of the scientific article, containing abundant field knowledge. This paper suggests a method of acquiring production rules from the abstracts and conclusions of scientific articles in specific fields based on natural language comprehension. First, the causal statements in article abstracts and conclusions are extracted using existing techniques, such as text mining. Next, antecedence and consequence fragments are extracted using causal template matching algorithms. As the final step, part-of-speech-tagging production rules are automatically generated according to a syntax parsing tree from the speech pair sequence. Experiments show that this system not only improves the efficiency of knowledge acquisition but also simultaneously generates systematic knowledge and guarantees the accuracy of acquired knowledge.

AN EXPERTISE MODEL FOR THERAPY PLANNING USING ABDUCTIVE REASONING

Generic reasoning models facilitate the construction of knowledge-based systems for solving complex problems, such as therapy planning in an agricultural context. The basic features of these models are problem-solving methods, which are proposed to carry out the tasks ordinarily done by experts in solving a specific problem. This article describes an expertise model in the domain of plant health, which was obtained applying the CommonKADS methodology. The most important result is the abductive method proposed for supplying a solution to treatment problems in domains where there is no protocol for therapy planning tasks. The description of the method is based on the algebraic formulation of a domain-independent general treatment. Therefore, the abductive method proposed is sufficiently generalized to be applied to different domains.

KAMET: A comprehensive methodology for knowledge acquisition from multiple knowledge sources

At the beginning of the 1980s, the Artificial Intelligence (AI) community showed little interest in research on methodologies for the construction of knowledge-based systems (KBS) and for knowledge acquisition (KA). The main idea was the rapid construction of prototypes with LISP machines, expert system shells, and so on. Over time, the community saw the need for a structured development of KBS projects, and KA was recognized as the critical stage and the bottleneck for the construction of KBS. Concerning KA, many publications have appeared since then. However, very few have focused on formal plans to manage knowledge acquisition and from multiple knowledge sources. This paper addresses this important problem. KAMET is a formal plan based on models designed to manage knowledge acquisition from multiple knowledge sources. The objective of KAMET is to improve, in some sense, the phase of knowledge acquisition and knowledge modeling process, making them more efficient.

Building knowledge-based systems with procedural languages

The implementation of knowledge-based (KB) software using traditional languages and hardware is shown. Some techniques that need to be added to the programmer's tool kit for successful construction of KB systems using procedural languages are demonstrated. The discussion covers: reasoning and inferencing; procedural flow of control; basic data structures; explanation facilities; certainty factors; and procedural logic differences.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>