Most enterprises agree that knowledge is an essential asset for success and survival on a increasingly competitive and global market. This awareness is one of the main reasons for the exponential growth of knowledge management in the past decade. Our approach to knowledge management is based on ontologies, and makes knowledge assets intelligently accessible to people in organizations. Most company-vital knowledge resides in the heads of people, and thus successful knowledge management does not only consider technical aspects, but also social ones. In this paper, we describe an approach to intelligent knowledge management that explicitly takes into account the social issues involved. The proof of concept is given by a large-scale initiative involving knowledge management of a virtual organization.

The next generation of the Web, called Semantic Web, has to improve the Web with semantic (ontological) page annotations to enable knowledge-level querying and searches. Manual construction of these ontologies will require tremendous efforts that force future integration of machine learning with knowledge acquisition to enable highly automated ontology learning. In the paper we present the state of the-art in the field of ontology learning from the Web to see how it can contribute to the task of semantic Web querying. We consider three components of the query processing system: natural language ontologies, domain ontologies and ontology instances. We discuss the requirements for machine learning algorithms to be applied for the learning of the ontologies of each type from the Web documents, and survey the existent ontology learning and other closely related approaches.

Ontologies and problem-solving methods are promising candidates for reuse in Knowledge Engineering. Ontologies define domain knowledge at a generic level, while problem-solving methods specify generic reasoning knowledge. Both type of components can be viewed as complementary entities that can be used to configure new knowledge systems from existing, reusable components. In this paper, we give an overview of approaches for ontologies and problem-solving methods. 1 Introduction In 1991, the ARPA Knowledge Sharing Effort [NFF 91] envisioned a new way in which intelligent systems could be built. They proposed the following: "Building knowledgebased systems today usually entails constructing new knowledge bases from scratch. It could be done by assembling reusable components. Systems developers would then only need to worry about creating the specialized knowledge and reasoners new to the specific task of their system. This new system would interoperate with existing systems, us...

When building a Bayesian belief network, usually a large number of probabilities have to be assessed by experts in the domain of application. Experience shows that experts often are reluctant to assess all probabilities required, feeling that they are unable to give assessments with a high level of accuracy. We argue that the elicitation of probabilities from experts can be supported to a large extent by iteratively performing sensitivity analyses of the belief network in the making, starting with rough, initial assessments. Since it gives insight into which probabilities require a high level of accuracy and which do not, performing a sensitivity analysis allows for focusing further elicitation efforts. We propose an elicitation procedure in which, alternatingly, sensitivity analyses are performed and probability assessments are refined, until satisfactory behaviour of the belief network is obtained, until the costs of further elicitation outweigh the benefits of higher accur...

The multiplicity of network management models may imply in some scenarios the use of multiple management information languages defining the resources to be managed. Each language has a different level of semantic expressiveness which is not easily measurable. And these management in-formation models cannot be easily integrated due to a difficult translation of the semantics they contain. This paper proposes the use of ontologies as a new approach to improve the semantic expressiveness of management in-formation languages. Ontologies are being currently used, for instance, to provide web pages and web services the semantics they usually lack (what is known today as “Semantic Web”). Applying ontologies to management information languages can also be useful for the integration of information defini-tions specified by different management languages and for the addition of behavior information to them.

Ontologies are used in agent--oriented software development, information systems, and expert systems in order to support interoperability, declarativity, and intelligent services. In the FRODO project, we design a scalable, agent--based middleware for distributed Organizational Memories (OM). In this paper, we investigate which ontology--related services should be provided as middleware components. To this end, we discuss three basic dimensions to characterize stored information that determine the concrete specification of ontology--based systems, formality, stability, and sharing scope. A short discussion of techniques which are suited to find a balance on these dimensions leads to a characterization of roles of ontology--related actors in the OM scenario which are described with respect to their goals, knowledge, competencies, rights, and obligations. These actor classes and the related competencies are candidates to define agent types, speech acts, and standard se...

Currently computers are changing from single isolated devices to entry points into a worldwide network of information exchange and business transactions. Therefore, support in the exchange of data, information, and knowledge is becoming the key issue in computer technology today. Ontologies provide a shared and common understanding of a domain that can be communicated between people and across application systems. Ontologies will play a major role in supporting information exchange processes in various areas. A prerequisite for such a role is the development of a joint standard for specifying and exchanging ontologies. This paper deals with precisely this necessity. We will present OIL which is a proposal for such a standard. It is based on existing proposals such as OKBC, XOL and RDF, and enriches them with necessary features for expressing rich ontologies. The paper presents the motivation, underlying rationale, modeling primitives, syntax, semantics, and tool environment of OIL. With OIL, we want to make a proposal that initiates a discussion leading to a useful and well defined consensus amongst a large community which could use such an approach.

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of data Web mining operates on, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web resources and navigation behavior are increasingly being used. This fits exactly with the aims of the Semantic Web: the Semantic Web enriches the WWW by machineprocessable information which supports the user in his tasks. In this paper, we discuss the interplay of the Semantic Web with Web Mining, with a specific focus on usage mining.

This paper presents a novel approach to automated negotiation that is particularly suitable to open environments, such as the Internet. In this approach agents can negotiate in any type of marketplace regardless of the negotiation mechanism that these adopt. In order to support a wide variety of negotiation mechanisms, protocols are no longer hard-coded in the agents participating to negotiations, but are now expressed in terms of a shared ontology, thus making this approach particularly suitable for flexible applications such as electronic commerce.

In order to support semantic interoperation in open environments, where agents can dynamically join or leave and no prior assumption can be made on the ontologies to align, the different agents involved need to agree on the semantics of the terms used during the interoperation. Reaching this agreement can only come through some sort of negotiation process. Indeed, agents will differ in the domain ontologies they commit to; and their perception of the world, and hence the choice of vocabulary used to represent concepts. We propose an approach for supporting the creation and exchange of different arguments, that support or reject possible correspondences. Each agent can decide, according to its preferences, whether to accept or refuse a candidate correspondence. The proposed framework considers arguments and propositions that are specific to the matching task and are based on the ontology semantics. This argumentation framework relies on a formal argument manipulation schema and on an encoding of the agents ’ preferences between particular kinds of arguments. 1.