Users of online search engines often find it difficult to express their need for information in the form of a query. However, if the user can identify examples of the kind of documents they require then they can employ a technique known as relevance feedback. Relevance feedback covers a range of techniques intended to improve a user's query and facilitate retrieval of information relevant to a user's information need. In this paper we survey relevance feedback techniques. We study both automatic techniques, in which the system modifies the user's query, and interactive techniques, in which the user has control over query modification. We also consider specific interfaces to relevance feedback systems and characteristics of searchers that can affect the use and success of relevance feedback systems.

Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mappings has been the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping.

The benefits of using ontologies have been recognised in many areas such as knowledge and content management, electronic commerce and recently the emerging field of the Semantic Web. These new applications can be seen as a great success of research in ontologies. On the other hand, moving into real application comes with new challenges that need to be addressed on a principled level rather than for specific applications. This special issue will be devoted to less well-explored topics that have come into focus recently as a response to the new problems we face when trying to use ontologies in heterogeneous distributed environments. These environments include the use of ontologies in peer-to-peer and pervasive computing systems.

In recent years there have been some efforts to automate the ontology acquisition and construction process. The proposed systems differ from each other in some factors and have many features in common. This paper presents the state of the art in Ontology Learning (OL) and introduces a framework for classifying and comparing OL systems. The dimensions of the framework concern what to learn, from where to learn it and how it may be learnt. They include features of the input, the methods of learning and knowledge acquisition, the elements learned, the resulting ontology and also the evaluation process. To extract this framework, over 50 OL systems or modules thereof that have been described in recent articles are studied here and seven prominent ones, which illustrate the greatest differences, are selected for analysis according to our framework. In this paper after a brief description of the seven selected systems we describe the dimensions of the framework. Then we place the representative ontology learning systems into our framework. Finally, we describe the differences, strengths and weaknesses of various values for our dimensions in order to present a guideline for researchers to choose the appropriate features to create or use an OL system for their own domain or application.

This document describes COBRA-ONT, an ontology for supporting pervasive context-aware systems. COBRA-ONT, expressed in the Web Ontology Language OWL, is a collection of ontologies for describing places, agents and events and their associated properties in an intelligent meeting-room domain. This ontology is developed as a part of the Context Broker Architecture (CoBrA), a broker-centric agent architecture that provides knowledge sharing, context reasoning and privacy protection supports for pervasive context-aware systems. We also describe an inference engine for reasoning with information expressed using the COBRA-ONT ontology and the ongoing research in using the DAML-Time ontology for context reasoning.

Matching algorithms automatically discover semantic relations between two autonomously developed conceptual representations of two overlapping domains. Typical examples of such conceptualisations are electronic market catalogues (e.g., unspsc and eCl@ss) and Web directories (e.g., google and yahoo). The objective of this paper is the description of a use case in which the matching algorithm CtxMatch, developed at ITC-IRST and the University of Trento, has been used to re-classify into the Universal Standard Products and Services Classification (unspsc) the catalogue of office equipment and accessories used by a worldwide telecommunication company to classify its suppliers. On the basis of this experience we are envisaging new applications of the algorithm in the area of demand aggregation. We conclude the paper by briefly describing a future application in this area.

Over the last ten years a community that is interested in context has emerged. Brézillon (1999) gave a survey of the literature on context in artificial intelligence. There is now a series of conferences on context, a website and a mailing list. The number of web pages with the word “context” has increased tenfold in the last five years. Being among the instigators of the use of context in real-world applications, I present in this paper the evolution of my thoughts over the last years and the results that have been obtained, including a representation formalism based on contextual graphs and the use of this formalism in a real-world application called SART. I present how procedures, practices and context are intertwined, as identified in the SART application and in different domains. I root my view of context in the artificial intelligence area and give a general presentation of my view of context under the three aspects – external knowledge, contextual knowledge and proceduralised context – with the implementation of this view in contextual graphs. I discuss how reasoning is carried out, based on procedure and practices, in the formalism of contextual graphs and show how incremental acquisition of practices is integrated in this formalism.

This paper gives an overview of electronic marketplaces which employ agent technology or similar techniques. Special attention is given to negotiation issues. A classification scheme for competitive negotiation depending on the type of the marketplace (business-to-business, business-to-consumer and consumer-to-consumer) and on the range of players (1:1, 1:n and n:m) is introduced. Furthermore, we distinguish between one-issue and multi-issue negotiation, and we consider crisp and fuzzy constraints on either negotiating side. Several existing electronic marketplaces and prototypes of marketplaces are discussed with respect to the classification schema. Our analysis shows that agent-based e-marketplaces are a promising area of e-commerce but they are still far away from real marketplaces. In conclusion we formulate some challenges for further research in the field of multi-agent electronic marketplaces with negotiation between agents.

Knowledge-based systems have often been criticised for the limited theoretical base upon which they are constructed. This view asserts that systems are often developed in an ad hoc, individual way that leads to unmaintainable, unreliable and non-rigorous systems. The last decade, however, has seen an increased effort to produce methodologies to counter this view as well as continued research into validation and verification techniques. This paper presents a brief discussion of some of the important research in knowledge-based system life cycles and development methods. Methodologies are considered and are discussed in light of two sets of quality assurance criteria.

The current trend in the production of information appliances is the human-centred, customer-centred approach, where technology serves human needs invisibly, unobtrusively. The emphasis shifts from the application programs to the users, their tasks and their workplaces, making computation often move off the desktop to become embedded in the world around us. In this scenario the role of the visual interface becomes crucial since, as far as the customer is concerned, the interface is the product. In this paper we briefly survey the most recent results in the field of advanced visual interfaces, with focus on users' needs and ways to serve them.

In this paper, we identify and analyse a set of characteristics that increasingly distinguish today's complex software systems from ‘‘traditional” ones. Several examples in different areas show that these characteristics are not limited to a few application domains but are widespread. Then, we discuss how these characteristics are likely to impact dramatically on the very way software systems are modelled and engineered. In particular, we appear to be on the edge of a radical shift of paradigm, which is about to change our very attitudes in software systems modelling and engineering.

Ontologies are entering widespread use in many areas such as knowledge and content management, electronic commerce and the Semantic Web. In this paper we show how the use of ontologies has helped us overcome some important problems in the development of pervasive computing environments. We have integrated ontologies and Semantic Web technology into our pervasive computing infrastructure. Our investigations have shown that Semantic Web technology can be integrated into our CORBA-based infrastructure to augment several important services. This work suggests a number of requirements for future research in the development of ontologies, reasoners, languages and interfaces.

Negotiation is essential in settings where autonomous agents have conflicting interests and a desire to cooperate. For this reason, mechanisms in which agents exchange potential agreements according to various rules of interaction have become very popular in recent years as evident, for example, in the auction and mechanism design community. However, a growing body of research is now emerging which points out limitations in such mechanisms and advocates the idea that agents can increase the likelihood and quality of an agreement by exchanging arguments which influence each others' states. This community further argues that argument exchange is sometimes essential when various assumptions about agent rationality cannot be satisfied. To this end, in this article, we identify the main research motivations and ambitions behind work in the field. We then provide a conceptual framework through which we outline the core elements and features required by agents engaged in argumentation-based negotiation, as well as the environment that hosts these agents. For each of these elements, we survey and evaluate existing proposed techniques in the literature and highlight the major challenges that need to be addressed if argument-based negotiation research is to reach its full potential.

We have produced an ontology specifying a model of computer attack. Our ontology is based upon an analysis of over 4000 classes of computer intrusions and their corresponding attack strategies and is categorised according to system component targeted, means of attack, consequence of attack and location of attacker. We argue that any taxonomic characteristics used to define a computer attack be limited in scope to those features that are observable and measurable at the target of the attack. We present our model as a target-centric ontology that is to be refined and expanded over time. We state the benefits of forgoing dependence upon taxonomies in favour of ontologies for the classification of computer attacks and intrusions. We have specified our ontology using the DARPA Agent Markup Language+Ontology Inference Layer and have prototyped it using DAMLJessKB. We present our model as a target-centric ontology and illustrate the benefits of utilising an ontology in lieu of a taxonomy, by presenting a use-case scenario of a distributed intrusion detection system.

This paper explores the hypothesis that ontologies can be used to improve the capabilities and performance of on-board route planning for autonomous vehicles. We name a variety of general benefits that ontologies may provide, and list numerous specific ways that ontologies may be used in different components of our chosen infrastructure: the 4D/RCS system architecture developed at NIST. Our initial focus is on simple roadway driving scenarios where the controlled vehicle encounters objects in its path. Our approach is to develop an ontology of objects in the environment, in conjunction with rules for estimating the damage that would be incurred by collisions with the different objects in different situations. Automated reasoning is used to estimate collision damage; this information is fed to the route planner to help it decide whether to avoid the object. We describe our current experiments and plans for future work.

Ontologies provide potential support for knowledge and content management on a P2P platform. Although we can design ontologies beforehand for an application, it is argued that in P2P environments static or predefined ontologies cannot satisfy the ever-changing requirements of all users. So we propose every user should make proposals for what kind of ontology is the most apt to his need. Collecting all these proposals (or votes) helps the drift of ontologies. This paper introduces OntoVote, a scalable distributed vote-collecting mechanism based on application-level broadcast trees, and describes how OntoVote can be applied to ontology drift on a P2P platform by discussing several problems involved in the voting process.

We think of match as an operator that takes two graph-like structures (e.g. database schemas or ontologies) and produces a mapping between elements of the two graphs that correspond semantically to each other. The goal of this paper is to propose a new approach to matching, called semantic matching. As its name indicates, in semantic matching the key intuition is to exploit the model-theoretic information, which is codified in the nodes and the structure of graphs. The contributions of this paper are (i) a rational reconstruction of the major matching problems and their articulation in terms of the more generic problem of matching graphs, (ii) the identification of semantic matching as a new approach for performing generic matching and (iii) a proposal for implementing semantic matching by testing propositional satisfiability.