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

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