We present an intelligent information agent that uses semantic methods and natural language processing capabilites in order to gather tourist information from the WWW and present it to the human user in an intuitive, user-friendly way. Thereby, the information agent is designed such that as background knowledge and linguistic coverage increase, its benefits improve, while it guarantees state-of-the-art information and database retrieval capabilities as its bottom line.

Metalevel tools can support the knowledge-engineering process by assisting developers in the design and implementation of domain-oriented knowledge-acquisition tools. The use of ontologies as a basis for automatic generation of knowledge-acquisition tools simplifies the tool-specification process by taking advantage of ontologies defined as part of the knowledge-engineering process. One of the drawbacks of this approach is that it separates ontology definition (in ontology editors) from instance editing (in knowledge-acquisition tools). Because many application tasks require ontology definition by domain experts, we have experimented with extending the Protege framework to generate ontology editors in addition to knowledgeacquisition tools for instances. We have explored di#erent approaches to ontology-editor specification in a series of prototype extensions to Protege. Here, metaclass and metaslot definitions are the basis for ontology editors, which can be embedded in know...

The software and knowledge engineering literature defines maintenance strategies for seven main types of knowledge: words; sentences; behavioural knowledge; and meta-knowledge. Meta-knowledge divides into problem solving methods; quality knowledge; fix knowledge; social knowl- 5 edge; and processing activities. There are five main ways in which these seven knowledge types are processed: acquire; operationalise; fault; fix; and preserve. We review systems that contribute to these 7 5 = 35 types of knowledge maintenance. 1 Introduction 10 A general trend in the twentieth century is an increasing level of doubt about the things we speak or write or try to enter into programs. Popper argues that all knowledge is an hypothesis since nothing can ever be ultimately proved; Submitted to the Knowledge Engineering Review page 2 of 73 our currently believed ideas are merely those that have survive active attempts to refute them [89]. Knowledge representation theorists stress that KBs are...

Developing tools that allow non-programmers to enter knowledge has been an ongoing challenge for AI. In recent years researchers have investigated a variety of promising approaches to knowledge acquisition (KA), but they have often been driven by the needs of knowledge engineers rather than by end users. This paper reports on a series of experiments that we conducted in order to understandhow far a particular KA tool that we are developing is from meeting the needs of end users, and to collect valuable feedback to motivate our future research. This KA tool, called EMeD, exploits Interdependency Models that relate individual components of the knowledge base in order to guide users in specifying problem-solving knowledge. We describe how our experiments helped us addressseveral questions and hypotheses regarding the acquisition of problem-solving knowledge from end users and the benefits of Interdependency Models, and discuss what we learned in terms of improving not only...

Metalevel tools can support the software development process by automating the design of task- and application-specific tools. Dash is a metalevel tool that allows developers to generate domain-specific knowledge-acquisition tools from domain ontologies. Domain specialists use the knowledge-acquisition tools generated by dash to instantiate the concepts and relationships defined in the domain ontologies. The output of the knowledge-acquisition tools is a collection of instances that constitute the knowledge base for a knowledge-based system.

. The paper introduces an approach for the specification and verification of knowledge-based systems combining conceptual and formal techniques. We identify four elements of the specification of a knowledge-based system: a task definition, a problem-solving method, a domain model, and an adapter that relates the other elements. We present abstract data types and a variant of dynamic logic as formal means to specify and verify these different elements. As a consequence of our conceptual model we can decompose the overall verification task of the knowledge-based systems into different proof obligations. Each proof obligation deals with a different aspect of the entire system. The use of the conceptual model in specification and verification improves understandability and reduces the effort for both activities. The modularization enables reuse of specifications and proofs. A knowledge-based system can be build by combing and adapting different components. 1 INTRODUCTION During the last ...

The paper provides three novel contributions to knowledge engineering. First, we provide a structured approach for the development and adaptation of problem-solving methods. We start from very generic search strategies with weak data structures and add adapters that refine the states and state transitions of the search process and that add assumptions necessary to link the competence of a method with given problem definitions and domain knowledge. Second, we show how the usability-reusability trade-off of taskspecific versus task-independent problem-solving methods can easily be overcome by the virtual existence of specific methods. Third, we provide the concept of an integrated library combining reusable problem definitions, problem-solving methods, and adapters.

In recent years two main technologies for knowledge sharing and reuse have emerged: ontologies and problem solving methods (PSMs). Ontologies specify reusable conceptualizations which can be shared by multiple reasoning components communicating during a problem solving process. PSMs describe in a domain-independent way the generic reasoning steps and knowledge types needed to perform a task. Typically PSMs are specified in a task-specific fashion, using modelling frameworks which describe their control and inference structures as well as their knowledge requirements and competence. In this paper we discuss a novel approach to PSM specification, which is based on the use of formal ontologies. In particular our specifications abstract from control, data flow and other dynamic aspects of PSMs to focus on the logical theory associated with a PSM (method ontology). This approach concentrates on the competence and knowledge requirements of a PSM, rather than internal control de...

When interest in intelligent systems for clinical medicine soared in the 1970s, workers in medical informatics became particularly attracted to rule-based systems. Although many successful rule-based applications were constructed, development and maintenance of large rule bases remained quite problematic. In the 1980s, an entire industry dedicated to the marketing of tools for creating rule-based systems rose and fell, as workers in medical informatics began to appreciate deeply why knowledge acquisition and maintenance for such systems are difficult problems. During this time period, investigators began to explore alternative programming abstractions that could be used to develop intelligent systems. The notions of “generic tasks ” and of reusable problemsolving methods became extremely influential. By the 1990s, academic centers were experimenting with architectures for intelligent systems based on two classes of reusable components: (1) domainindependent problem-solving methods—standard algorithms for automating stereotypical tasks—and (2) domain ontologies that captured the essential concepts (and relationships among those concepts) in particular application areas. This paper will highlight how intelligent systems for diverse tasks can be efficiently automated using these kinds of building blocks. The creation of domain ontologies and problem-solving methods is the fundamental end product of basic research in medical informatics. Consequently, these concepts need more attention by our scientific community.

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