Machine learning research 1989-90

Multifunctional knowledge bases offer a significant advance in artificial intelligence because they can support numerous expert tasks within a domain. As a result they amortize the costs of building a knowledge base over multiple expert systems and they reduce the brittleness of each system. Due to the inevitable size and complexity of multifunctional knowledge bases, their construction and maintenance require knowledge engineering and acquisition tools that can automatically identify interactions between new and existing knowledge. Furthermore, their use requires software for accessing those portions of the knowledge base that coherently answer questions. Considerable progress was made in developing software for building and accessing multifunctional knowledge bases. A language was developed for representing knowledge, along with software tools for editing and displaying knowledge, a machine learning program for integrating new information into existing knowledge, and a question answering system for accessing the knowledge base

Knowledge maintenance in myCBR

CBR systems, being knowledge based systems, process knowledge. Due to changes in the environment a CBR system’s knowledge model can become outdated, thus creating a need for constant maintenance of said knowledge model. In this paper, we describe an implementation of (semi-)automatic knowledge maintenance of two of the four knowledge containers of CBR systems, specifically case base maintenance and maintenance of similarity measures within the CBR system development SDK myCBR. We describe our approach to create, elicit and manage quality measures that are used to trigger maintenance actions if the quality measures fall below defined thresholds, indicating a declining efficiency/accuracy of a case base or particular similarity measure. We further detail on the implementation of our approach into myCBR Workbench to enable a knowledge engineer to incorporate the notion of maintenance already at the design stage of a CBR system. The approach relies on the notion of maintenance attributes to be able to measure the quality of case bases and similarity measures. Initial experiments using the newly introduced quality measurement attributes indicate that our approach is promising

A study of the methodologies currently available for the maintenance of the knowledge-base in an expert system

This research studies currently available maintenance methodologies for expert system knowledge bases and taxonomically classifies them according to the functions they perform. The classification falls into two broad categories. These are: (1) Methodologies for building a more maintainable expert system knowledge base. This section covers techniques applicable to the development phases. Software engineering approaches as well as other approaches are discussed. (2) Methodologies for maintaining an existing knowledge base. This section is concerned with the continued maintenance of an existing knowledge base. It is divided into three subsections. The first subsection discusses tools and techniques which aid the understanding of a knowledge base. The second looks at tools which facilitate the actual modification of the knowledge base, while the last secttion examines tools used for the verification or validation of the knowledge base. Every main methodology or tool selected for this study is analysed according to the function it was designed to perform (or its objective); the concept or principles behind the tool or methodology: and its implementation details. This is followed by a general comment at the end of the analysis. Although expert systems as a rule contain significant amount of information related to the user interface, database interface, integration with conventional software for numerical calculations, integration with other knowledge bases through black boarding systems or network interactions, this research is confined to the maintenance of the knowledge base only and does not address the maintenance of these interfaces. Also not included in this thesis are Truth Maintenance Systems. While a Truth Maintenance System (TMS) automatically updates a knowledge base during execution time, these update operations are not considered \u27maintenance\u27 in the sense as used in this thesis. Maintenance in the context of this thesis refers to perfective, adaptive, and corrective maintenance (see introduction to chapter 4). TMS on the other hand refers to a collection of techniques for doing belief revision (Martin, 1990) . That is, a TMS maintains a set of beliefs or facts in the knowledge base to ensure that they remain consistent during execution time. From this perspective, TMS is not regarded as a knowledge base maintenance tool for the purpose of this study

Human-Readable and Machine-Readable Knowledge Bases Using Specialized Word Processors

The maintenance of knowledge bases is one of the crucial activities in the life cycle of knowledge systems. This paper describes an innovative approach to write complex and large knowledge bases using specialized word processors. According to this, a knowledge model is represented as a conventional document that is written following the standard operations of word processors. Following this approach, domain experts that are not familiar with computer languages could easier read and write complex knowledge models. In addition to that, the processor is able of interpreting the content of the document to automatically perform tasks of the knowledge model. The paper describes the basic characteristics of the document and its specialized word processor and presents our experience following this approach for a knowledge system in the domain of hydrology

An incremental algorithm for generating all minimal models

AbstractThe task of generating minimal models of a knowledge base is at the computational heart of diagnosis systems like truth maintenance systems, and of nonmonotonic systems like autoepistemic logic, default logic, and disjunctive logic programs. Unfortunately, it is NP-hard. In this paper we present a hierarchy of classes of knowledge bases, Ψ1,Ψ2,… , with the following properties: first, Ψ1 is the class of all Horn knowledge bases; second, if a knowledge base T is in Ψk, then T has at most k minimal models, and all of them may be found in time O(lk2), where l is the length of the knowledge base; third, for an arbitrary knowledge base T, we can find the minimum k such that T belongs to Ψk in time polynomial in the size of T; and, last, where K is the class of all knowledge bases, it is the case that ⋃i=1∞Ψi=K, that is, every knowledge base belongs to some class in the hierarchy. The algorithm is incremental, that is, it is capable of generating one model at a time

When are description logic knowledge bases indistinguishable?

Deciding inseparability of description logic knowledge bases (KBs) with respect to conjunctive queries is fundamental for many KB engineering and maintenance tasks including versioning, module extraction, knowledge exchange and forgetting. We study the combined and data complexity of this inseparability problem for fragments of Horn-ALCHI, including the description logics underpinning OWL 2 QL and OWL 2 EL

Selecting Link Resolver and Knowledge Base Software: Implications of Interoperability

Link resolver software and their associated knowledge bases are essential technologies for modern academic libraries. However, because of the increasing number of possible integrations involving link resolver software and knowledge bases, a library’s vendor relationships, product choices, and consortial arrangements may have the most dramatic effects on the user experience and back-end maintenance workloads. A project team at a large comprehensive university recently investigated link resolver products in an attempt to increase efficiency of back-end workflows while maintaining or improving the patron experience. The methodology used for product comparison may be useful for other libraries

Design and construction of maintainable knowledge bases through effective use of entity-relationship modeling techniques

The use of an accepted logical database design tool, Entity-Relationship Diagrams (E-RD), is explored as a method by which conceptual and pseudo-conceptual knowledge bases may be designed. Extensions to Peter Chen\u27s classic E-RD method which can model knowledge structure used by knowledge-based applications are explored. The use of E-RDs to design knowledge bases is proposed as a two-stage process. In the first stage, and E-RD, termed the Essential E-RD, is developed of the realm of the problem or enterprise being modeled. The Essential E-RD is completely independent of any knowledge representation model (KRM) and is intended for the understanding of the underlying conceptual entities and relationships in the domain of interest. The second stage of the proposed design process consists of expanding the Essential E-RD. The resulting E-RD, termed the Implementation E-RD, is a network of E-RD-modeled KRM constructs and will provide a method by which the proper KRM may be chosen and the knowledge base may be maintained. In some cases, the constructs of the Implementation E-RD may be mapped directly to a physical knowledge base. Using the proposed design tool will aid in both the development of the knowledge base and its maintenance. The need for building maintainable knowledge bases and problems often encountered during knowledge base construction will be explored. A case study is presented in which this tool is used to design a knowledge base. Problems avoided by the use of this method are highlighted, as are advantages the method presents to the maintenance of the knowledge base. Finally, a critique of the ramifications of this research is presented, as well as needs for future research