《电子商务 E-business》阅读文献：Ontology-Based User Modeling

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636 Raj Sharman, Rajiv Kishore and Ram Ramesh many organizations have perceived the need to become more"knowledge oriented'or "learning"organizations. KMSs are information systems dedicated to manage knowledge processes and represent a key element for knowledge-oriented organizations Knowledge Management Systems(KMSs)are designed to allow users to access and utilize the rich sources of data, information and knowledge stored in different forms. They also support knowledge creation, knowledge transfer and continuous learning for the knowledge workers. Knowledge Management Systems contain both explicit and implicit or tacit knowledge. Explicit knowledge is the most visible form of knowledge and the one we are most familiar with. It is easily written down and includes artifacts and data stored in documents, reports that are available within and outside the organization, and software. But, Knowledge Management Systems can, to some extent, address the management of tacit knowledge. Tacit Knowledge is more difficult to articulate, and includes the experience, know-how, skills, knacks and the expertise of the people. According to Nonaka and Takeuchi (1995)/The/ more important kind of knowledge is tacit knowledge. This chapter puts forward the arguments for integrating user modeling in KMSS. It emphasizes the role of user modeling within Ontology-based Knowledge Management System (OKMS). A user model is a key component for providing enhanced features such as: personalization expertise discovery, networking, collaboration and learning (Razmerita et al., 2003). More particularly, an ontology-based user modeling approach is proposed and concrete examples of how ontology-based inferences can be used for expertise modeling are provided. The chapter shows the importance of using ontology-based representations for modeling the users and it pinpoints future work directions The integration models in KMSs opens a large number of research questions some of these are common to the general objectives of user modeling, others are more specific to the Human-Computer Interaction and to Knowledge management whilst others are related to the use of ontology for representing user models. The problem of user modeling ddresses two important user needs: a need for enhanced support for filtering and retrieving the knowledge available in the system, and a need to better manage the tacit knowledge. The management of the tacit knowledge includes a need to access the qualification and experience of peer knowledge workers in the company. In Knowledge Management Systems, user models or user profiles have frequently been created to represent user competences or preferences. This view is extended by including other characteristics of the users. For example the Behavior concept models some characteristics of users interacting with a KMS (e.g, type of activity, level of activity, level of knowledge sharing). These characteristics are inferred based on the user

636 Raj Sharman, Rajiv Kishore and Ram Ramesh many organizations have perceived the need to become more “knowledgeoriented” or “learning” organizations. KMSs are information systems dedicated to manage knowledge processes and represent a key element for knowledge-oriented organizations. Knowledge Management Systems (KMSs) are designed to allow users to access and utilize the rich sources of data, information and knowledge stored in different forms. They also support knowledge creation, knowledge transfer and continuous learning for the knowledge workers. Knowledge Management Systems contain both explicit and implicit or tacit knowledge. Explicit knowledge is the most visible form of knowledge and the one we are most familiar with. It is easily written down and includes artifacts and data stored in documents, reports that are available within and outside the organization, and software. But, Knowledge Management Systems can, to some extent, address the management of tacit knowledge. Tacit Knowledge is more difficult to articulate, and includes the experience, know-how, skills, knacks and the expertise of the people. According to Nonaka and Takeuchi (1995) “[..The] more important kind of knowledge is tacit knowledge.” This chapter puts forward the arguments for integrating user modeling in KMSs. It emphasizes the role of user modeling within Ontology-based Knowledge Management System (OKMS). A user model is a key component for providing enhanced features such as: personalization, expertise discovery, networking, collaboration and learning (Razmerita et al., 2003). More particularly, an ontology-based user modeling approach is proposed and concrete examples of how ontology-based inferences can be used for expertise modeling are provided. The chapter shows the importance of using ontology-based representations for modeling the users and it pinpoints future work directions. The integration of user models in KMSs opens a large number of research questions some of these are common to the general objectives of user modeling, others are more specific to the Human-Computer Interaction and to Knowledge Management whilst others are related to the use of ontology for representing user models. The problem of user modeling addresses two important user needs: a need for enhanced support for filtering and retrieving the knowledge available in the system, and a need to better manage the tacit knowledge. The management of the tacit knowledge includes a need to access the qualification and experience of peer knowledge workers in the company. In Knowledge Management Systems, user models or user profiles have frequently been created to represent user competences or preferences. This view is extended by including other characteristics of the users. For example the Behavior concept models some characteristics of users interacting with a KMS (e.g., type of activity, level of activity, level of knowledge sharing). These characteristics are inferred based on the user

Ontology Handbook 637 ctivity in the system. The proposed user model is conceptualized based on the Information Management System Learning Information Package specifications and is defined as user ontology, using Semantic Web technologies The chapter is organized as follows. The second section introduces some of the challenges associated with the development of a next generation of KMSS. Section 3 discusses the role of user modeling in an OKMS. Section 4 presents the process of building the user ontology and proposes a set of user modeling mechanisms for modeling the users behavior in a KMS. Section 5 presents an integrated architecture of an OKMS emphasizing points of entry for the user modeling module. Finally, the last section concludes with discussion on the user modeling and ontology-based modeling and indicates to future work directions TOWARDS A NEXT GENERATION OF KNOWLEDGE MANAGEMENT SYSTEMS 2.1 The Knowledge Management Challenges KMSS have been defined in a number of different ways. Knowledge Management Systems (KMSs) refer to a class of information systems applied to managing organizational knowledge"(Leidner and Alavi, 2001) Many of the current KMSs integrate knowledge processes from an organizational perspective focused on technological solutions. From this technological perspective, the main objective of KMSs is to provide uniform and seamless access to any relevant information for a task to be undertaken Thus, KMSs can be defined as: the process of capturing, organizing and retrieving information based on notions like databases, documents, query languages and knowledge mining. (Thomas and Kellogg, 2001) Nowadays, KMSs are challenged to integrate complex oriented processes which facilitate work processes, knowledge creation. knowledge transfer and continuous learning for the knowledge workers Schutt(2001)emphasizes that the challenge of actual KMSs is to foster knowledge management processes with the final goal to increase the productivity of their employees. The complexity of business processe implies that KMSs capture, store and deploy a critical mass of knowledge in various forms. Large, distributed organizations, such as Indra, do not necessarily have an integrated solution for knowledge management. Generally, large organizations use a portfolio of tools such as enterprise portals, databases, different collaboration tools, forums, threaded discussions or shared spaces, etc. In certain cases, each division creates its own

Ontology Handbook 637 activity in the system. The proposed user model is conceptualized based on the Information Management System Learning Information Package specifications and is defined as user ontology, using Semantic Web technologies. The chapter is organized as follows. The second section introduces some of the challenges associated with the development of a next generation of KMSs. Section 3 discusses the role of user modeling in an OKMS. Section 4 presents the process of building the user ontology and proposes a set of user modeling mechanisms for modeling the user’s behavior in a KMS. Section 5 presents an integrated architecture of an OKMS emphasizing points of entry for the user modeling module. Finally, the last section concludes with a discussion on the user modeling and ontology-based modeling and indicates to future work directions. 2. TOWARDS A NEXT GENERATION OF KNOWLEDGE MANAGEMENT SYSTEMS 2.1 The Knowledge Management Challenges KMSs have been defined in a number of different ways. “Knowledge Management Systems (KMSs) refer to a class of information systems applied to managing organizational knowledge“(Leidner and Alavi, 2001). Many of the current KMSs integrate knowledge processes from an organizational perspective focused on technological solutions. From this technological perspective, the main objective of KMSs is to provide uniform and seamless access to any relevant information for a task to be undertaken. Thus, KMSs can be defined as: “the process of capturing, organizing and retrieving information based on notions like databases, documents, query languages and knowledge mining.” (Thomas and Kellogg, 2001) Nowadays, KMSs are challenged to integrate complex knowledgeoriented processes which facilitate work processes, knowledge creation, knowledge transfer and continuous learning for the knowledge workers. Schutt (2001) emphasizes that the challenge of actual KMSs is to foster knowledge management processes with the final goal to increase the productivity of their employees. The complexity of business processes implies that KMSs capture, store and deploy a critical mass of knowledge in various forms. Large, distributed organizations, such as Indra, do not necessarily have an integrated solution for knowledge management. Generally, large organizations use a portfolio of tools such as enterprise portals, databases, different collaboration tools, forums, threaded discussions or shared spaces, etc. In certain cases, each division creates its own

638 Raj sharman, Rajiv Kishore and Ram Ramesh application for managing knowledge and accumulates valuable information there. Furthermore, there is usually little or no integration of the various knowledge management tools, databases, and portals. Knowledge resources are not centralized and the amount of distributed knowledge sources available can constitute an obstacle for finding and retrieving the relevant knowledge. Moreover this critical mass of corporate resources is also a factor which contributes to an information overload process for its users. As a result knowledge workers waste time searching for the necessary corporate resources to perform work tasks efficiently 2.2 Perceived Needs of the user Some important issues that need to be addressed by the next generation of KMSs have been identified by surveying the opinion of the knowledge workers of two large, geographically-distributed Spanish companies from information technology sector: Indra and Meta4. These surveys pointed out important issues which need to be taken into account in the design of a next generation of KMSs. Amongst these issues are: a need to better organize the content of the Kmss; a need for enhanced support for filtering and retrieving the knowledge available in the system; a need to access the qualifications and experience of peer knowledge worker the company. functionality to be integrated, plex business processes requires more The integration of nd knowledge ma implicitly high functionality applications. However, in an extended survey of the vision of the executives impressions on KMSs(Knowings enquete, 2003) keywords such as: utility, simplicity, conviviality, adaptability to the needs nd specificity of the enterprise were emphasized. Additionally, in this survey personalization is associated with the access to the knowledge assets and with the simplicity of use of the system. The problem of user modeling relates to the aforementioned issues, namely the information overload issue the need for enhanced user support, personalization and the need to better manage the tacit knowledge. The need for enhanced support Is expressed as"to not get lost"amongst hundreds of documents and to filter information and noise". Research on personalization, semantic web technologies, adaptive hypermedia and user modelling is the basis for implementing novel me echanisms for filtering and retrieving the knowledge available in the system

638 Raj Sharman, Rajiv Kishore and Ram Ramesh application for managing knowledge and accumulates valuable information there. Furthermore, there is usually little or no integration of the various knowledge management tools, databases, and portals. Knowledge resources are not centralized and the amount of distributed knowledge sources available can constitute an obstacle for finding and retrieving the relevant knowledge. Moreover this critical mass of corporate resources is also a factor which contributes to an information overload process for its users. As a result knowledge workers waste time searching for the necessary corporate resources to perform work tasks efficiently. 2.2 Perceived Needs of the User Some important issues that need to be addressed by the next generation of KMSs have been identified by surveying the opinion of the knowledge workers of two large, geographically-distributed Spanish companies from information technology sector: Indra and Meta4. These surveys pointed out important issues which need to be taken into account in the design of a next generation of KMSs. Amongst these issues are: • a need to better organize the content of the KMSs; • a need for enhanced user support for filtering and retrieving the knowledge available in the system; • a need to access the qualifications and experience of peer knowledge workers in the company. The integration of complex business processes requires more functionality to be integrated and knowledge management solutions become implicitly high functionality applications. However, in an extended survey of the vision of the executives impressions on KMSs (Knowings enquete, 2003) keywords such as: utility, simplicity, conviviality, adaptability to the needs and specificity of the enterprise were emphasized. Additionally, in this survey personalization is associated with the access to the knowledge assets and with the simplicity of use of the system. The problem of user modeling relates to the aforementioned issues, namely the information overload issue, the need for enhanced user support, personalization and the need to better manage the tacit knowledge. The need for enhanced user support is expressed as “to not get lost” amongst hundreds of documents and to filter “information and noise”. Research on personalization, semantic web technologies, adaptive hypermedia and user modelling is the basis for implementing novel mechanisms for filtering and retrieving the knowledge available in the system

Ontology Handbook 639 2.3 Employing Ontologies in KMSs emantic web technology, ontology, service-oriented architectures including: software agents, web services or grid services and user modelling are emerging technologies to be integrated in the design of a next generation of KMSs Integrated architectures using emerging technologies such as: web services, ontology, and agent components for user-centric, smart office task automation have been recently prototyped (tsai et al. 2003, Razmerita et al 2003, Gandon et al, 2002) 2.3.1 Ontology for KMs Ontology is approached with different senses in different communities Often ontology is just a fancy name denoting a simple taxonomy, or a set of activities performed according to a standardized methodology, or a certain conceptual analysis used to model the domain knowledge. Several definitions for ontology in artificial intelligence have been proposed endler(2001)defines ontology as: a set of knowledge terms, including the vocabulary, the semantic interconnections and some simple rule of inference and logic for some particular topic. The use of ontology has become popular in many application domains including: knowledge engineering, natural language processing, knowledge representation, intelligent information integration and knowledge management. The ontology represents and structures the different knowledge sources in its business domain (OLeary, 1998, Becker et al., 2000, Stojanovic et al., 2001) Existing knowledge sources(documents, reports, videos, etc ) are mapped into the domain ontology and semantically enriched. This semantically enriched information enables better knowledge indexing and searching processes and implicitly a better management of knowledge. According to Kim et al. (2004)an ontology-based system can be used not only to improve precision but also to reduce search time. Due to these reasons, ontology- based approaches will likely be the core technology for the development of a next generation of Knowledge Management Systems(KMSs) However bringing ontology to real world enterprise application is still a challenge. Maedche et al.(2003) explains why ontology-based representations and Semantic Web technology are still in early stages for enterprise OKMs. One reason would be that ontology-based conceptual representations lack certain features which are important for classical database driven information systems. Features such as scalability, persistency, reliability, and transactions standardized in classical data-base driven applications are typically not available in ontology-based systems Another reason is that a large amount of information in an enterprise exist

Ontology Handbook 639 2.3 Employing Ontologies in KMSs Semantic web technology, ontology, service-oriented architectures including: software agents, web services or grid services and user modelling are emerging technologies to be integrated in the design of a next generation of KMSs. Integrated architectures using emerging technologies such as: web services, ontology, and agent components for user-centric, smart office task automation have been recently prototyped (Tsai et al. 2003, Razmerita et al. 2003, Gandon et al., 2002). 2.3.1 Ontology for KMS Ontology is approached with different senses in different communities. Often ontology is just a fancy name denoting a simple taxonomy, or a set of activities performed according to a standardized methodology, or a certain conceptual analysis used to model the domain knowledge. Several definitions for ontology in artificial intelligence have been proposed. Hendler (2001) defines ontology as: “a set of knowledge terms, including the vocabulary, the semantic interconnections and some simple rule of inference and logic for some particular topic.” The use of ontology has become popular in many application domains including: knowledge engineering, natural language processing, knowledge representation, intelligent information integration and knowledge management. The ontology represents and structures the different knowledge sources in its business domain (O’Leary, 1998, Abecker et al., 2000, Stojanovic et al., 2001). Existing knowledge sources (documents, reports, videos, etc.) are mapped into the domain ontology and semantically enriched. This semantically enriched information enables better knowledge indexing and searching processes and implicitly a better management of knowledge. According to Kim et al. (2004) an ontology-based system can be used not only to improve precision but also to reduce search time. Due to these reasons, ontologybased approaches will likely be the core technology for the development of a next generation of Knowledge Management Systems (KMSs). However bringing ontology to real world enterprise application is still a challenge. Maedche et al. (2003) explains why ontology-based representations and Semantic Web technology are still in early stages for enterprise OKMS. One reason would be that ontology-based conceptual representations lack certain features which are important for classical database driven information systems. Features such as scalability, persistency, reliability, and transactions standardized in classical data-base driven applications are typically not available in ontology-based systems. Another reason is that a large amount of information in an enterprise exists

640 Raj sharman, Rajiv Kishore and Ram Ramesh outside classical KMSs, in applications such as: groupware, databases or other applications that have been used regularly within organizations 2.3.2 Ontology-based Knowledge Modeling Ontology entails or embodies some sort of world view with respect to a given domain. The world view is often conceived as a set of concepts(e.g entities, attributes, and processes), their definitions and their inter relationships; this is referred to as a conceptualization. "(Uschold and Gruninger, 1996 Ontology-based modeling has rapidly developed as a new approach for modeling knowledge, in the last few years. Consequently, the term ontology engineering has emerged. "The goal of so called ontological engineering is to develop theories, methodologies and tools suitable to elicit and organize domain knowledge in a reusable and transparent way. Guarino, 1997) Amongst the technological challenges ontology engineering tools needs to provide reliable solutions for: managing multiple ontologies, evolving ontologies, scalability. ( Maedche et al, 2003) Guarino et al. (1994)emphasize the fact that: rigorous ontologic foundation for knowledge representation can result in better methodologies for conceptual design of data and knowledge bases, facilitating knowledge sharing and reuse.” Noy et al.(2001)have highlighted several reasons for developi ontologies: 1)to share common understanding of the structure of information among people or software agents; 2)to enable reuse of the domain knowledge, 3)to make domain assumptions explicit; 4)to separate domain knowledge from the operational knowledge; 5)to analyze domain knowledge Different methodologies for development of ontology-based Knowledge Management Applications have been recently proposed (Sure et al., 2003 Dieng et al., 1999, 2004) We agree with Studer et al.(1998) who emphasizes that building ontology for a particular domain requires a profound analysis, revealing the relevant concepts, attributes, relations, constraints, instances and axioms of that domain. Such knowledge analysis typically results in hierarchy of concepts with their attributes, values and relations. Further, this knowledge analysis phase is followed by an implementation stage. Implementing the designed ontology in a formal language enables to make it a machine processable model. Similar somehow to a software engineering process, Uschold and Gruninger [1996] define a skeletal methodology for building ontology. The process of building ontology is divided into three basic steps: capturing, coding, and integrating with existing ontology

640 Raj Sharman, Rajiv Kishore and Ram Ramesh outside classical KMSs, in applications such as: groupware, databases or other applications that have been used regularly within organizations. 2.3.2 Ontology-based Knowledge Modeling “Ontology entails or embodies some sort of world view with respect to a given domain. The world view is often conceived as a set of concepts (e.g. entities, attributes, and processes), their definitions and their interrelationships; this is referred to as a conceptualization.” (Uschold and Gruninger, 1996) Ontology-based modeling has rapidly developed as a new approach for modeling knowledge, in the last few years. Consequently, the term ontology engineering has emerged. “The goal of so called ontological engineering is to develop theories, methodologies and tools suitable to elicit and organize domain knowledge in a reusable and transparent way.” (Guarino, 1997) Amongst the technological challenges ontology engineering tools needs to provide reliable solutions for: managing multiple ontologies, evolving ontologies, scalability. (Maedche et al., 2003). Guarino et al. (1994) emphasize the fact that: “rigorous ontological foundation for knowledge representation can result in better methodologies for conceptual design of data and knowledge bases, facilitating knowledge sharing and reuse.” Noy et al. (2001) have highlighted several reasons for developing ontologies: 1) to share common understanding of the structure of information among people or software agents; 2) to enable reuse of the domain knowledge; 3) to make domain assumptions explicit; 4) to separate domain knowledge from the operational knowledge; 5) to analyze domain knowledge. Different methodologies for development of ontology-based Knowledge Management Applications have been recently proposed. (Sure et al., 2003, Dieng et al., 1999, 2004). We agree with Studer et al. (1998) who emphasizes that building ontology for a particular domain requires a profound analysis, revealing the relevant concepts, attributes, relations, constraints, instances and axioms of that domain. Such knowledge analysis typically results in hierarchy of concepts with their attributes, values and relations. Further, this knowledge analysis phase is followed by an implementation stage. Implementing the designed ontology in a formal language enables to make it a machine processable model. Similar somehow to a software engineering process, Uschold and Gruninger [1996] define a skeletal methodology for building ontology. The process of building ontology is divided into three basic steps: capturing, coding, and integrating with existing ontology

Ontology Handbook 41 The process of ontology capture comprises: Identification of the key concepts and relationships in the domain of Production of unambiguous text definitions for the concepts and their Identification of the terms to refer to such concepts and relationships Ontology capture corresponds to a specification pha software engineering The process of coding implies the representation of the ontology some formal language. This implies a decision on the representation formalism, namely the ontology representation language, to be used. A set of ontology languages such as: OWL, KAON, extending Resource Description Framework /Schema(RDF/RDFS), recommended by the World wide Web Consortium facilitate the implementation of ontology-based applications These languages enable expression and implementation of ontology-based conceptual models in a computational form. The process of integrating with existing ontology During this phase the problem of interoperability with other existing ontology must be clarified Major problems may arise if similar concepts are already defined in existing ontology or if different representational ontology languages are used Bachimont (2000)views the ontology modelling process as a three step rocess but from another perspective. The first step implies specifying the linguistic meaning of the concepts which correspond to a semantic commitment. The second step is an ontological commitment by specifying the formal meaning of the ontology. In a third step, ontology achieves computational commitment by being integrated in a system 2.3.3 Semantic Services for the users The distributive nature of tasks to be handled in a kms determines a natural choice for multi-agent or service-oriented architectures. Moreover service-oriented architectures are viewed as an attractive solution for enterprise application integration, business process management and the design of advanced information systems. The core technology for service oriented architectures is web services. Web services are software plications that can be discovered, described and accessed based on XML and standard Web protocols over intranets, extranets and the Internet Daconta et al., 2003 ). Initially the web service efforts focused on interoperability, standards and protocols for performing business to business transactions. Web services are a key technology providing solutions for data integration issues. Basic web service technologies are: SOAP (Simple Object Access Protocol), WSDL (Web Service Description Language)and UDDI (Universal Description Discovery Integration)

Ontology Handbook 641 The process of ontology capture comprises: – Identification of the key concepts and relationships in the domain of interest; – Production of unambiguous text definitions for the concepts and their relationships; – Identification of the terms to refer to such concepts and relationships Ontology capture corresponds to a specification phase in software engineering. The process of coding implies the representation of the ontology in some formal language. This implies a decision on the representation formalism, namely the ontology representation language, to be used. A set of ontology languages such as: OWL, KAON, extending Resource Description Framework /Schema (RDF/RDFS), recommended by the World Wide Web Consortium facilitate the implementation of ontology-based applications. These languages enable expression and implementation of ontology-based conceptual models in a computational form. The process of integrating with existing ontology During this phase the problem of interoperability with other existing ontology must be clarified. Major problems may arise if similar concepts are already defined in existing ontology or if different representational ontology languages are used. Bachimont (2000) views the ontology modelling process as a three step process but from another perspective. The first step implies specifying the linguistic meaning of the concepts which correspond to a semantic commitment. The second step is an ontological commitment by specifying the formal meaning of the ontology. In a third step, ontology achieves computational commitment by being integrated in a system. 2.3.3 Semantic Services for the Users The distributive nature of tasks to be handled in a KMS determines a natural choice for multi-agent or service-oriented architectures. Moreover, service-oriented architectures are viewed as an attractive solution for enterprise application integration, business process management and the design of advanced information systems. The core technology for service oriented architectures is web services. Web services are software applications that can be discovered, described and accessed based on XML and standard Web protocols over intranets, extranets and the Internet (Daconta et al., 2003). Initially the web service efforts focused on interoperability, standards and protocols for performing business to business transactions. Web services are a key technology providing solutions for data integration issues. Basic web service technologies are: SOAP (Simple Object Access Protocol), WSDL (Web Service Description Language) and UDDI (Universal Description Discovery & Integration)

642 Raj sharman, Rajiv Kishore and Ram Ramesh Semantic Web services are extensions of web services, providing a richer semantic description for services. They are implemented using languages such as: Web Ontology Language for Services OWL S(OWL S, 2004), Web Services Modeling Ontology WSMO (WSMO, 2005) and Internet Reasoning Service Framework IRS (IRS, 2004). The associated semantic description of services will facilitate the discovery, the comparison or the composition of simple services by other software entities or by humans Ontology constitutes basic vocabularies facilitating the communication, the composition and the interoperability of semantic services and agents. Web services can also be seen as independent agents that produce and consume information, enabling automated business transactions(Paolucci nd Sycara, 2003). Societies of agents can act with the purpose of helping the user or solving problems on behalf of the users. Specialized agents can cooperate, negotiate, and communicate in order to achieve various functions such as: discovery and classification of new knowledge, search and retrieval of information, the automatic evolution of the domain ontology, etc. The following section puts forward the arguments for user modelling processes in Kms USER MODELLING IN KNOWLEDGE MANAGEMENT SYSTEMS In order to support personalized interaction with the users, information ystems need to construct or access and maintain a user model. Moreover, knowledge workers are the most valuable resource of corporate memory and the key element in the management of tacit knowledge. Making the experience of people more visible in organization and capitalizing the knowledge of the employees is important for companies. Organizations are more and more concerned with aspects related to how to capitalize and manage the individual knowledge On the one hand, user modeling processes support the acquisition of competencies, qualifications, and work experience explicitly or implicitly. On the other hand, the implicit complexity of Kmss doesnt necessarily fit the need of the users to have simple systems: systems dapted to their specific needs. The knowledge workers of Indra, a Spanish company, the end-users of the Ontologging system, suggested: to include mechanisms in order to acquire knowledge about user profile and filter information and noise"and to"adapt the tools to each company or sector The heterogeneity of users, differences in users'responsibilities, different domains of interests, different competencies, and work tasks to be handled in a Kms drives a need to focus on the users, on the user needs and variability in KMS design Characteristics of the users integrated in the user models are

642 Raj Sharman, Rajiv Kishore and Ram Ramesh Semantic Web services are extensions of web services, providing a richer semantic description for services. They are implemented using languages such as: Web Ontology Language for Services OWL_S (OWL_S, 2004), Web Services Modeling Ontology WSMO (WSMO, 2005) and Internet Reasoning Service Framework IRS (IRS, 2004). The associated semantic description of services will facilitate the discovery, the comparison or the composition of simple services by other software entities or by humans. Ontology constitutes basic vocabularies facilitating the communication, the composition and the interoperability of semantic services and agents. Web services can also be seen as independent agents that produce and consume information, enabling automated business transactions (Paolucci and Sycara, 2003). Societies of agents can act with the purpose of helping the user or solving problems on behalf of the users. Specialized agents can cooperate, negotiate, and communicate in order to achieve various functions such as: discovery and classification of new knowledge, search and retrieval of information, the automatic evolution of the domain ontology, etc. The following section puts forward the arguments for user modelling processes in KMS. 3. USER MODELLING IN KNOWLEDGE MANAGEMENT SYSTEMS In order to support personalized interaction with the users, information systems need to construct or access and maintain a user model. Moreover, knowledge workers are the most valuable resource of corporate memory and the key element in the management of tacit knowledge. Making the experience of people more visible in organization and capitalizing the knowledge of the employees is important for companies. Organizations are more and more concerned with aspects related to how to capitalize and manage the individual knowledge. On the one hand, user modeling processes support the acquisition of competencies, qualifications, and work experience explicitly or implicitly. On the other hand, the implicit complexity of KMSs doesn’t necessarily fit the need of the users to have simple systems: systems adapted to their specific needs. The knowledge workers of Indra, a Spanish company, the end-users of the Ontologging system, suggested: “to include mechanisms in order to acquire knowledge about user profile and filter information and noise” and to “adapt the tools to each company or sector”. The heterogeneity of users, differences in users’ responsibilities, different domains of interests, different competencies, and work tasks to be handled in a KMS drives a need to focus on the users, on the user needs and variability in KMS design. Characteristics of the users integrated in the user models are

Ontology Handbook 643 the basis for personalization of the user interaction with the system Moreover, the adoption of KMSs also might require a change process of the current work practices of the knowledge workers and implicit changes at the organizational level. For example, the issue of how to motivate people to share their knowledge is not simply solved by offering people tools for doing this. Consequently, some incentives for the adoption of knowledge sharing practices might need to be introduced at the whole organizational level. User modeling mechanisms can be used to determine a behavioural model of a 3er interacting with a KMS, and to provide adapted feedback or rewards to the users(Razmerita, 2003 ). Organizations need to create the enabling factors for the knowledge workers: to be creative, to submit knowledge assets in the system and to diffuse their knowledge. For example, knowledge workers might not be intrinsically motivated to spend time sharing knowledge or to submit knowledge to the system, especially if it requires extra work Personalization An important strand of research in user modeling aims to enhance the interaction between the users and the systems. The goal of this research is to make complex systems more usable, to speed-up and simplify interactions (Kay, 2000). Fischer(2001)provides some insights in the design of human- centred systems supported by user modeling techniques. He emphasizes that high functionality applications must address three problems:(1)the unused functionality must not get in the way;(2)unknown existing functionality must be accessible or delivered at times when it is needed; and (3) commonly used functionality should be not too difficult to be learned, used and remembered. However there clearly exist adaptation methods and personalization techniques that are specific to KMSS. These adaptation methods and personalization techniques relate to specific objectives of KMSS. Amongst these specific objectives are how to motivate people to create knowledge and submit new knowledge assets in the system; how to stimulate collaboration and knowledge sharing between knowledge workers irrespective of their location; how to alleviate information overload, how to simplify business and work tasks. and so forth ersonalization techniques rely on the user's characteristics captured in user models or user profiles. User's characteristics can be used for providing different types of adaptations and personalised services. Personalisation of a KMS is the process that enables interface customization, adaptations of the functionality, structure, content and modality in order to increase its relevance for its individual users(Razmerita, 2005)

Ontology Handbook 643 the basis for personalization of the user interaction with the system. Moreover, the adoption of KMSs also might require a change process of the current work practices of the knowledge workers and implicit changes at the organizational level. For example, the issue of how to motivate people to share their knowledge is not simply solved by offering people tools for doing this. Consequently, some incentives for the adoption of knowledge sharing practices might need to be introduced at the whole organizational level. User modeling mechanisms can be used to determine a behavioural model of a user interacting with a KMS, and to provide adapted feedback or rewards to the users (Razmerita, 2003). Organizations need to create the enabling factors for the knowledge workers: to be creative, to submit knowledge assets in the system and to diffuse their knowledge. For example, knowledge workers might not be intrinsically motivated to spend time sharing knowledge or to submit knowledge to the system, especially if it requires extra work. 3.1 Personalization An important strand of research in user modeling aims to enhance the interaction between the users and the systems. The goal of this research is to make complex systems more usable, to speed-up and simplify interactions (Kay, 2000). Fischer (2001) provides some insights in the design of humancentred systems supported by user modeling techniques. He emphasizes that high functionality applications must address three problems: (1) the unused functionality must not get in the way; (2) unknown existing functionality must be accessible or delivered at times when it is needed; and (3) commonly used functionality should be not too difficult to be learned, used and remembered. However there clearly exist adaptation methods and personalization techniques that are specific to KMSs. These adaptation methods and personalization techniques relate to specific objectives of KMSs. Amongst these specific objectives are: • how to motivate people to create knowledge and submit new knowledge assets in the system; • how to stimulate collaboration and knowledge sharing between knowledge workers irrespective of their location; • how to alleviate information overload, how to simplify business processes and work tasks, and so forth. Personalization techniques rely on the user’s characteristics captured in user models or user profiles. User’s characteristics can be used for providing different types of adaptations and personalised services. Personalisation of a KMS is the process that enables interface customization, adaptations of the functionality, structure, content and modality in order to increase its relevance for its individual users (Razmerita, 2005)

644 Raj sharman, Rajiv Kishore and Ram Ramesh Personalization can be achieved in two different ways: based agent's intervention such as synthetic characters or information filtering agents, or based on various types of intelligent services that are transparent to the users, also addressed as adaptive techniques in the user modeling literature Such personalization mechanisms are based on the user's characteristics nd they could include direct access to customized relevant knowledge assets provide unobtrusive assistance; help to find/to recall information needed for a task; offer to automate certain tasks through implicit or explicit interventions The adaptation techniques, at the level of the user interface, can be classified into three categories: adaptation of structure, adaptation of conten daptation of modality and presentation. For instance, in the range of adaptation of structure, the system can offer personalised views of corporate knowledge based on interest areas and the know ledge of the users or based on the role and competencies of the users. "Personalised views are a way to organise an electronic workplace for the users who need an access to a reasonably small part of a hyperspace for their everyday work. Adaptation of content refers to the process of dynamically tailoring the information that is presented to the different users according to their specific profiles(needs, interests, level of expertise, etc. ) The adaptation of content facilitates the process of filtering and retrieval of relevant information. In KMS recommender systems, information filtering agents and collaborative filtering techniques can be applied with the purpose of adaptation of content. The adaptation of presentation empowers the users to choose between different presentations styles, such as different layouts, skins, or fonts. Other preferences can include the pr esence o absence of anthropomorphic interface agents, the preferred languages, and so forth. Different types of sorting, bookmarks, and shortcuts can also be included in a high functional system. Adaptation of presentation overlaps to a certain extent with interface customisation. The adaptation of modality enables changes from text to other types of media to present the information to the user( text, video, animations, or audio) if they are available in the system. In modern adaptive hypermedia, user can select different types of media These personalisation mechanisms are described and exemplified with details in Razmerita( 2005) Recently, the concept of contextualization of knowledge goes beyond ersonalization. Dzbor et al.(2004)propose to bring the knowledge to the user through personal portals' taking into account timely and situationa sues and using a wider variety of interaction modalities

644 Raj Sharman, Rajiv Kishore and Ram Ramesh Personalization can be achieved in two different ways: based on the agent’s intervention such as synthetic characters or information filtering agents, or based on various types of intelligent services that are transparent to the users, also addressed as adaptive techniques in the user modeling literature. Such personalization mechanisms are based on the user’s characteristics and they could include: • direct access to customized relevant knowledge assets; • provide unobtrusive assistance; • help to find/to recall information needed for a task; • offer to automate certain tasks through implicit or explicit interventions. The adaptation techniques, at the level of the user interface, can be classified into three categories: adaptation of structure, adaptation of content, adaptation of modality and presentation. For instance, in the range of adaptation of structure, the system can offer personalised views of corporate knowledge based on interest areas and the knowledge of the users or based on the role and competencies of the users. “Personalised views are a way to organise an electronic workplace for the users who need an access to a reasonably small part of a hyperspace for their everyday work.” (Brusilovsky, 1998) Adaptation of content refers to the process of dynamically tailoring the information that is presented to the different users according to their specific profiles (needs, interests, level of expertise, etc.). The adaptation of content facilitates the process of filtering and retrieval of relevant information. In KMS recommender systems, information filtering agents and collaborative filtering techniques can be applied with the purpose of adaptation of content. The adaptation of presentation empowers the users to choose between different presentations styles, such as different layouts, skins, or fonts. Other preferences can include the presence or absence of anthropomorphic interface agents, the preferred languages, and so forth. Different types of sorting, bookmarks, and shortcuts can also be included in a high functional system. Adaptation of presentation overlaps to a certain extent with interface customisation. The adaptation of modality enables changes from text to other types of media to present the information to the user (text, video, animations, or audio) if they are available in the system. In modern adaptive hypermedia, user can select different types of media. These personalisation mechanisms are described and exemplified with details in Razmerita (2005). Recently, the concept of contextualization of knowledge goes beyond personalization. Dzbor et al. (2004) propose to bring the knowledge to the user through ‘personal portals’ taking into account timely and situational issues and using a wider variety of interaction modalities

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