F.A.H. van Harmelen

OIL: an ontology infrastructure for the Semantic Web

Researchers in artificial intelligence first developed ontologies to facilitate knowledge sharing and reuse. Ontologies play a major role in supporting information exchange across various networks. A prerequisite for such a role is the development of a joint standard for specifying and exchanging ontologies. The authors present OIL, a proposal for such a standard. Ontologies applied to the World Wide Web are creating the Semantic Web.

The Semantic Web: the roles of XML and RDF

XML and RDF are the current standards for establishing semantic interoperability on the Web, but XML addresses only document structure. RDF better facilitates interoperation because it provides a data model that can be extended to address sophisticated ontology representation techniques. We explain the role of ontologies in the architecture of the Semantic Web. We then briefly summarize key elements of XML and RDF, showing why using XML as a tool for semantic interoperability will be ineffective in the long run. We argue that a further representation and inference layer is needed on top of the Webs current layers, and to establish such a layer, we propose a general method for encoding ontology representation languages into RDF/RDF schema. We illustrate the extension method by applying it to Ontology Interchange Language, an ontology representation and inference language.

The semantic Web and its languages

The Web has drastically changed the availability of electronic information, but its success and exponential growth have made it increasingly difficult to find, access, present and maintain such information for a wide variety of users. In reaction to this bottleneck many new research initiatives and commercial enterprises have been set up to enrich available information with machine-processable semantics. The paper considers how the semantic Web will provide intelligent access to heterogeneous and distributed information, enabling software products (agents) to mediate between user needs and available information sources. The paper discusses the Resource Description Framework, XML and other languages.

It's a Streaming World! Reasoning upon Rapidly Changing Information

In this column, we describe two concrete examples of stream-reasoning applications and introduce stream-reasoning research problems. We also present a list of research areas that we believe should be investigated to turn stream reasoning into a reality.

Unifying Reasoning and Search to Web Scale

The Web and reasoning started to meet around 1996 with the first projects that added semantics to Web page descriptions in much the same way that HTML added formatting information. Despite the subsequent growth of these semantic Web efforts into a dynamic and well-established research area, serious doubt remains whether reasoning really adds something useful in the Web context. Traditional notions of complete and correct reasoning are obviously based on a heavily simplified world view naively applied to reality. Researchers have developed reasoning methods for rather small, closed, trustworthy, consistent, and static domains. They usually provide a small set of axioms; a proof engine can typically provide complete and correct inferences of the knowledge contained in them

Ontology-based information visualisation

The main contribution is to show how visual representations of information can be based on ontological classifications of that information. We first discuss the central role of ontologies on the Semantic Web. We subsequently outline our general approach to the construction of ontology-based visualisations of data. This is followed by a set of examples of ontology-based visualisations which all differ in interesting respects. We conclude with a brief discussion of related work.

A comparison of languages which operationalise and formalise KADS models of expertise

In the field of Knowledge Engineering, dissatisfaction with the rapid-prototyping approach has led to a number of more principled methodologies for the construction of knowledgebased systems. Instead of immediately implementing the gathered and interpreted knowledge in a given implementation formalism according to the rapid-prototyping approac h, many such methodologies centre around the notion of a conceptual model: an abstract, implementation independent description of the relevant problem solving expertise . A conceptual model should describe the task which is solved by the system and the knowledge which is required by it. Although such conceptual models have often been formulated in an informal way, recent years have seen the advent of formal and operational languages to describe such conceptual models more precisely, and operationally as a means for model evaluation. In this paper, we study a number of such formal and operational languages for specifying conceptual models. In order to enable a meaningful comparison of such languages, we focus on languages which are all aimed at the same underlying conceptual model, namely that from the KADS method for building KBS. We describe eight formal languages for KADS models of expertise, and compare these languages with respect to their modelling primitives, their semantics, their implementations and their applications. Future research issues in the area of formal and operational specification languages for KBS are identified as the result of studying thes e languages. The paper also contains an extensive bibliography of research in this area.

Exploring large document repositories with RDF technology: the DOPE project

This thesaurus-based search system uses automatic indexing, RDF-based querying, and concept-based visualization of results to support exploration of large online document repositories. Innovative research institutes rely on the availability of complete and accurate information about new research and development. Information providers such as Elsevier make it their business to provide the required information in a cost-effective way. The semantic Web will likely contribute significantly to this effort because it facilitates access to an unprecedented quantity of data. The DOPE project (Drug Ontology Project for Elsevier) explores ways to provide access to multiple life-science information sources through a single interface.

Formal Methods in Knowledge Engineering

This paper presents a general discussion of the role of formal methods in Knowledge Engineering. We give an historical account of the development of the field of Knowledge Engineering towards the use of formal methods. Subsequently, we discuss the pros and cons of formal methods. We do this by summarising the proclaimed advantages, and by arguing against some of the commonly heard objections against formal methods. We briefly summarise the current state of the art and discuss the most important directions that future research in this field should take. This paper presents a general setting for the other contributions in this issue of the Journal, which each deal with a specific issue in more detail.

Construction of problem-solving methods as parametric design

The knowledge-engineering literature contains a number of approaches for constructing or selecting problem solvers. Some of these approaches are based on indexing and selecting a problem solver from a library, others are based on a knowledge acquisition process, or are based on search-strategies. None of these approaches sees constructing a problem solver as a configuration task that could be solved with an appropriate configuration method. We introduce a representation of the functionality of problem-solving methods that allows us to view the construction of problem solvers as a configuration problem, and specifically as a parametric design problem. From the available methods for parametric design, we use propose-critique-modify for the automated configuration of problem-solving methods. We illustrate this approach by a scenario in a small car domain example.