The book covers in an integrated fashion the complete route from corporate knowledge management, through knowledge analysis andengineering, to the design and implementation of knowledge-intensiveinformation systems. The disciplines of knowledge engineering and knowledge management are closely tied. Knowledge engineering deals with the development of information systems in which knowledge and reasoning play pivotal roles. Knowledge management, a newly developed field at the intersection of computer science and management, deals with knowledge as a key resource in modern organizations. Managing knowledge within an organization is inconceivable without the use of advanced information systems; the design and implementation of such systems pose great organization as well as technical challenges. The book covers in an integrated fashion the complete route from corporate knowledge management, through knowledge analysis and engineering, to the design and implementation of knowledge-intensive information systems. The CommonKADS methodology, developed over the last decade by an industry-university consortium led by the authors, is used throughout the book. CommonKADS makes as much use as possible of the new UML notation standard. Beyond information systems applications, all software engineering and computer systems projects in which knowledge plays an important role stand to benefit from the CommonKADS methodology.

W H E N THE RESEARCH THAT led to CommonKADS was conceived as part of the European Esprit program in 1983, the AI community as a whole showed little interest in methodological issues. At the time, the prevailing paradigm for building knowledgebased systems was rapid prototyping using special purpose hard-and software, such as LISP machines, expert system shells, and so on. Since then, however, many developers have realized that a structured development approach is just as necessary in knowledgebased systems as it is in conventional software projects. This structured development approach is the aim of CommonKADS.Traditionally, knowledge engineering was viewed as a process of "extracting" knowledge from a human expert and transferring it to the machine in computational form. Today, knowledge engineering is approached as a modeling activity. In the CommonKADS methodology, KBS development entails constructing a set of engineering models of problem solving behavior in its concrete organization and application context. This modeling concerns not only expert knowledge, but also the various characteristics of how that knowledge is embedded and used in the organizational environment. The different models are a means of capturing the different sources and types of requirements that play a role in realistic applications. A KBS, then, is a computational realization associated with a collection of these models. Figure 1 summarizes the suite of models involved in a ComrnonKADS project. A central model in the CommonKADS methodology is the expertise model, which models the problem solving behavior of an agent in terms of the knowledge that is applied to perform a certain task. Other models capture relevant aspects of reality, such as the task supported by an application; the organizational context; the distribution of tasks over different agents; the agents' capabilities and communication; and the computational system design of the KBS. These are engineeringtype models and serve engineering purposes. The models are considered not as "steps along the way," but as independent products in their own right that play an important role during the life cycle of the KBS.Here, we give a brief overview of the Com-28 0885-9000/94/$4.00 0 1994 IEEE monKADS methodology, paying special attention to the expertise modelingan aspect of KBS development that distinguishes it from other types of software development. We illustrate the CommonKADS approach by showing how aspects of the VT system' for elevator design would be modeled (see sidebar, "The VT System" for background). Project management principlesIn CommonKADS, project management and development activities are separated. Project management is represented by a project management activity model that interacts with the development work through model states attached to the CommonKADS models. The development process proceeds in a cyclic, riskdriven way similar to Boehm's spiral model? IEEE EXPERT

Abstract-In this paper, we propose an automatic video retrieval method based on high-level concept detectors. Research in video analysis has reached the point where over 100 concept detectors can be learned in a generic fashion, albeit with mixed performance. Such a set of detectors is very small still compared to ontologies aiming to capture the full vocabulary a user has. We aim to throw a bridge between the two fields by building a multimedia thesaurus, i.e., a set of machine learned concept detectors that is enriched with semantic descriptions and semantic structure obtained from WordNet. Given a multimodal user query, we identify three strategies to select a relevant detector from this thesaurus, namely: text matching, ontology querying, and semantic visual querying. We evaluate the methods against the automatic search task of the TRECVID 2005 video retrieval benchmark, using a news video archive of 85 h in combination with a thesaurus of 363 machine learned concept detectors. We assess the influence of thesaurus size on video search performance, evaluate and compare the multimodal selection strategies for concept detectors, and finally discuss their combined potential using oracle fusion. The set of queries in the TRECVID 2005 corpus is too small for us to be definite in our conclusions, but the results suggest promising new lines of research.Index Terms-Concept learning, content analysis and indexing, knowledge modeling, multimedia information systems, video retrieval.

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