Intelligent Content Management System Project Presentation

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Intelligent Content Management System Project Presentation

  1. 1. Intelligent Content Management System Project Presentation April 2002
  2. 2. IST-2001-32429 ICONS Intelligent Content Management System www.icons.rodan.pl Project Partners Rodan Systems (PL) The Polish Academy of Sciences (PL) Centro di Ingegneria Economica e Sociale (IT) InfoVide (PL) SchlumbergerSema (BE) University Paris 9 Dauphine (FR) University of Ulster (UK) Intelligent Content Management System Project Presentation Project name Intelligent Content Management System Acronym ICONS Workpackage WP9 Task T9.1 Document type report Title Intelligent Content Management System Subtitle Project Presentation Document acronym D01 Author(s) Witold Staniszkis, Nicola Leone, Pasquale Rullo, Łukasz Balcerek, Michał Śmiałek, Witold Litwin, Gérard Levy, Jules Georges, Kazimierz Subieta, Mariusz Momotko, Dorota Depowska, Janusz Charczuk, Waldemar Piszczewiat, Yaxin Bi, David Bell Reviewer(s) Annette Bleeker, Bartosz Nowicki Accepting Witold Staniszkis Location I:WP9 Project ManagementICONS WP9 T1 D01 0115.doc Version 1.15 Date April 2002 Status final version Distribution public April 2002
  3. 3. Intelligent Content Management System 1.15 History of changes April 2002 History of changes Date Version Author Change description 6.4.02 1.14 Bartosz Nowicki final packaging 4.4.02 1.12 Witold Staniszkis integration of partners’ inputs for chapter 5-9 30.3.02 1.8 partners’ inputs provided Mariusz Momotko (5.5, 7, 8.3) Witold Litwin, Gérard Levy (8.1, 8.2) David Bell, Yaxin Bi (5.1-5.4) Nicola Leone, Pasquale Rullo (5.1-5.4) Kazimierz Subieta (5.6) Jules Georges (9.1) Łukasz Balcerek, Michał Śmiałek (9.2) Dorota Depowska, Waldemar Piszczewiat (6) 30.2.02 1.03 Bartosz Nowicki ICONS template applied 30.2.02 1.02 Witold Staniszkis further elaboration; work distribution among partners 1.2.02 1.01 Witold Staniszkis document creation IST-2001-32429 ICONS Intelligent Content Management System page 3/86
  4. 4. Intelligent Content Management System 1.15 Executive summary April 2002 Executive summary The primary objective of the ICONS Project Presentation report is to provide a baseline platform for all ICONS project stakeholders representing the consensus of the ICONS consortium members with respect to the ICONS research and development strategy. Much effort has gone into interactions among members of the ICONS research and development community aiming at reconciliation of diverse views and specialisations in the relevant research realms. We assume that the ensuing research results may require refinements and modifications of the underlying ICONS assumptions and we plan to reflect them in the ensuing versions of the report. Hence, this report is to “live: status document reflecting the current views of the ICONS consortium. The initial effort has gone into the Knowledge Management System (KMS) feature requirements analysis in order to establish compatibility of requirements voiced by the knowledge management community and the prevailing opinions and conclusions of the on-going research work in the IT field. Our motivation has been to verify the ICONS project goals and objectives and possibly to re-orient some of the principal research and development objectives. The representative results of the management science research pertaining to intellectual capital and knowledge management have been examined. We have concentrated on the work of the Knowledge Management Consortium International [Firestone2000, McElroy1999], the seminal work in the area of learning organisations [Garvin1993] and knowledge modelling [Popper1971, Popper1977], as well as generally accepted views of Nonaka and Takeuchi [Nonaka1995] with respect to knowledge creation and dissemination processes. The principal conclusions are that the current KM needs require IT support for KM processes in order to facilitate innovation leading to enhanced competitive advantage. A mapping of the KM processes and the desirable KMS features has been established. Our findings have been confronted with the prevailing views of the IT research and development community with respect to the KMS architecture requirements. We have developed a KMS reference architecture enumerating the desirable KM features to provide a “common denominator” representation of the current IT research and development work. Principal results of the on-going European KM projects may be found in European KM Forum web site [KMForum2001]. The principal KMS feature sets include knowledge dissemination features, domain ontology features, content repository features, KMS actor collaboration features, knowledge security features, and content integration features. The KMS features role semantics with respect to the KM processes have been specified in order to confront the IT community prevailing views with those represented by management scientists. We have established that the referential KMS architecture is sufficiently powerful to provide significant enabling leverage for the KM field. The above complementary views on the KM scene provide a solid referential background for the ICONS architecture specification providing a backbone for our research and development work. We concentrate our project work on three key technological areas, namely on the Knowledge Management Technologies area, the Human/Computer Interaction (HCI) area, and the Distributed Architecture Technologies area. We further demonstrate that such approach is fully compatible with the stated ICONS project goal and objectives and that it enables us to provide the required technical support for the KMS reference architecture. The complete view of the ICONS architecture comprises additional technological areas, auxiliary to our project, namely the Content Management Technologies area and the Development Technologies area. The software modules within the auxiliary areas are input into the project, preferably as “open source” or proprietary to consortium partners to be subsequently used and/or modified within the ICONS prototype. The cross-reference between the KMS referential architecture and the proposed ICONS architecture indicating research and/or development effort needed shows completeness of the ICONS features with respect to the established requirements. Knowledge-based features are the important building block of the ICONS architecture therefore a multi- paradigm approach has been proposed. The research work on formal aspects of knowledge representation including rules and uncertainty, the Dempster-Shafer theory, and the extended relational model. Disjunctive Datalog inference engine is to be extended and integrated into the system provides principal knowledge-based platform. Procedural knowledge based on workflow specifications is to extent the Workflow Management Coalition model with the time modelling features and the CPM (Critical Path Method) modelling capabilities. Such extensions allow for enhanced support for knowledge management processes usually unsuitable for the WfMC-based process modelling approach. We proposed an advanced graphic HCI interface to support visualisation and manipulation of structural knowledge comprising semantic nets, UML relationships, and process graphs. IST-2001-32429 ICONS Intelligent Content Management System page 4/86
  5. 5. Intelligent Content Management System 1.15 Executive summary April 2002 The knowledge-based capabilities are to be used in development of the intelligent content integration features to support an open ICONS content repository. The ICONS content management functions are to integrate under a unique knowledge map information resources stored internally and those stored in Web information sources, as well as in the legacy information systems and the heterogeneous databases. A wrapper-based architecture is to establish the technological base content integration. The key features of the ICONS workflow management platform are the dynamic workflow participant assignment functions, the dynamic control flow condition modification capabilities, and time modelling features. A knowledge-based support to be used within the workflow management engine is to be developed with the use of the disjunctive Datalog inference engine module. Appropriate extensions to the WfMC model will be developed. The ICONS distributed processing organisation, providing both for data and processing distribution, is to be based on the SDDS approach with appropriate extensions to meet the system requirements. Distributed processing will be enabled by the load balancing algorithms to be embedded in the ICONS control functions. The workflow process distribution and inter-operability is to be based on the distributed workflow communication and synchronisation features to be developed for the ICONS prototype. ICONS capabilities are to be demonstrated by a knowledge management application to be developed by the project team as “The NAS Best Practices Portal”. The application development cycle and techniques are to follow a KMS development methodology to be specified within the ICONS project. A preliminary analysis of the state-of-the-art in the area of KMS methodologies shows that, although sound methodological basis exists in the software engineering area, no generally accepted approach exists in the knowledge management realm. The conclusions of the report show that the proposed approach to the ICONS project research and development work is compatible with the stated project objectives. The ICONS project activities are covering the following research and development areas: (i) knowledge representation techniques and methodologies for a multimedia content repository, (ii) advanced graphic user interface design and management tools, (iii) design and implementation of efficient algorithms for management of large, distributed multimedia content repositories, and an analysis and design methodology for large, knowledge-based content repository systems. IST-2001-32429 ICONS Intelligent Content Management System page 5/86
  6. 6. Intelligent Content Management System 1.15 Table of contents April 2002 Table of contents History of changes................................................................................................................................................... 3 Executive summary ................................................................................................................................................. 4 Table of contents ..................................................................................................................................................... 6 List of figures .......................................................................................................................................................... 8 List of tables ............................................................................................................................................................ 8 1. Introduction ..................................................................................................................................................... 9 1.1 Objectives ................................................................................................................................................ 9 1.2 Scope ....................................................................................................................................................... 9 1.3 Relations to other documents................................................................................................................... 9 1.4 Intended audience .................................................................................................................................... 9 1.5 Usage guidelines...................................................................................................................................... 9 1.6 Notation conventions............................................................................................................................... 9 2. The ICONS Project Goal and Objectives ...................................................................................................... 10 3. Feature Requirements of a Knowledge Management System ....................................................................... 12 3.1 Knowledge Management: A Framework for User Requirements.......................................................... 12 3.2 The KMS Reference Architecture ......................................................................................................... 18 3.2.1 Domain Ontology features............................................................................................................. 19 3.2.2 Content Repository features .......................................................................................................... 21 3.2.3 Knowledge Dissemination features ............................................................................................... 21 3.2.4 Content Integration features .......................................................................................................... 22 3.2.5 Actor Collaboration features.......................................................................................................... 23 3.2.6 Knowledge Security features ......................................................................................................... 24 4. Architecture of the Intelligent CONtent management System (ICONS)....................................................... 25 4.1 The ICONS architecture specification................................................................................................... 25 4.1.1 Development Technologies ........................................................................................................... 25 4.1.2 Content Management Technologies .............................................................................................. 26 4.1.3 Knowledge Management Technologies......................................................................................... 27 4.1.4 Human Computer Interaction Technologies.................................................................................. 28 4.1.5 Distributed Architecture Technologies .......................................................................................... 29 4.2 The ICONS architecture vs. the KMS reference architecture................................................................ 29 5. The ICONS Knowledge Representation Features ......................................................................................... 33 5.1 Requirements for Knowledge Management (KM) ................................................................................ 33 5.2 Syntax/Semantics................................................................................................................................... 33 5.3 Formal foundations of knowledge representation.................................................................................. 35 5.3.1 Rules and uncertainty .................................................................................................................... 35 5.3.2 Data Representation using Dempster-Shafer theory...................................................................... 35 5.3.3 Extended relational database model .............................................................................................. 36 5.3.4 Hyperrelations used for representing mined knowledge................................................................ 36 5.3.5 Hyperrelations as knowledge representation ................................................................................. 36 5.3.6 Metadata ........................................................................................................................................ 37 5.3.7 Sharing data ................................................................................................................................... 37 5.4 Disjunctive Logic Programming............................................................................................................ 38 5.5 Procedural knowledge representation features ...................................................................................... 43 5.6 Knowledge representation and manipulation in the graphic user interface ........................................... 45 6. The ICONS Intelligent Content Integration Features .................................................................................... 50 6.1 The ICONS Global Knowledge Schema ............................................................................................... 50 6.2 The ICONS Content Repository............................................................................................................ 51 6.3 Integration of the heterogeneous content sources.................................................................................. 51 7. The ICONS Intelligent Workflow Features................................................................................................... 53 7.1 Dynamic workflow participant assignment ........................................................................................... 53 7.2 Dynamic control flow condition definition............................................................................................ 53 7.3 Time management ................................................................................................................................. 53 7.4 Task scheduling ..................................................................................................................................... 54 7.5 Extensions with respect to the WfMC's workflow process meta-model................................................ 54 8. The ICONS Distributed Processing Organisation ......................................................................................... 55 8.1 The ICONS scalable, distributed architecture ....................................................................................... 55 8.2 The ICONS distributed processing optimisation and load balancing .................................................... 57 IST-2001-32429 ICONS Intelligent Content Management System page 6/86
  7. 7. Intelligent Content Management System 1.15 Table of contents April 2002 8.3 The ICONS distributed workflow process communication and synchronisation .................................. 58 9. Demonstration of ICONS prototype capabilities........................................................................................... 60 9.1 The “Newly-associated States Best Practices” Portal............................................................................ 60 9.1.1 Introduction ................................................................................................................................... 60 9.1.2 Key Issues for Application Development ...................................................................................... 64 9.1.3 Key Success Factors ...................................................................................................................... 66 9.1.4 Remarks......................................................................................................................................... 66 9.2 The Knowledge Management System Design Methodology................................................................. 67 9.2.1 Approaches to Knowledge Management methodologies............................................................... 67 9.2.2 Requirements for defining a comprehensive KMS development methodology ............................ 67 9.2.3 The ICONS Development Methodology ....................................................................................... 70 10. Conclusions ............................................................................................................................................... 72 10.1 Compatibility with the stated ICONS project goals and objectives....................................................... 72 10.2 Overview of the ICONS project development plan ............................................................................... 72 Appendix A. List of workpackages and deliverables ............................................................................................ 76 Workpackages ................................................................................................................................................... 76 Deliverables list ................................................................................................................................................. 77 Bibliography.......................................................................................................................................................... 78 External references ............................................................................................................................................ 78 ICONS references.............................................................................................................................................. 84 Dictionary.............................................................................................................................................................. 85 IST-2001-32429 ICONS Intelligent Content Management System page 7/86
  8. 8. Intelligent Content Management System 1.15 List of figures April 2002 List of figures Figure 1. The scope of KM activities in 423 corporations surveyed by KPMG [KPMG1999]............................. 12 Figure 2. The Knowledge Life Cycle (KLC)........................................................................................................ 13 Figure 3. Four processes of knowledge conversion [Nonaka1995]....................................................................... 15 Figure 4. ICONS taxonomy of knowledge. ........................................................................................................... 18 Figure 5. The Knowledge Management System reference architecture. .............................................................. 18 Figure 6. The ICONS architecture schematic model............................................................................................ 25 Figure 7. Treatment relation. ................................................................................................................................. 36 Figure 8. A hyperrelation. ..................................................................................................................................... 37 Figure 9. Architecture of the GUI module............................................................................................................. 45 Figure 10. ICONS GUI module with interfaces to databases............................................................................... 47 Figure 11. A graph of objects. ............................................................................................................................... 48 Figure 12. The idea of the user basket................................................................................................................... 48 Figure 13. Models of workflow co-operation........................................................................................................ 58 Figure 14. Main Concept of ICONS portal for NAS Best Practice. ...................................................................... 63 Figure 15. The Knowledge life cycle of the NAS Best Practices Portal. .............................................................. 65 List of tables Table 1. Cross-reference between the KM processes and the KMS features. ....................................................... 16 Table 2. Feature roles within the knowledge management processes. .................................................................. 17 Table 3. Feature requirements of a Knowledge Management System. ................................................................. 19 Table 4. The ICONS focus technological area modules and the Domain Ontology features cross reference ....... 30 Table 5. The ICONS focus technological area modules and the Content Repository features cross reference..... 30 Table 6. The ICONS focus technological area modules and the Knowledge Dissemination features cross reference. ....................................................................................................................................................... 31 Table 7. The ICONS focus technological area modules and the Content Integration features cross reference..... 32 Table 8. The ICONS focus technological area modules and the Actor Collaboration features cross reference.... 32 Table 9. Checklist of the acquis (chapters in Regular Reports). ........................................................................... 61 Table 10. Overview of Phare................................................................................................................................. 62 Table 11. Best practice taxonomy. ........................................................................................................................ 63 Table 12. Key technological issues for development of the NAS Best Practices Portal. ...................................... 66 Table 13. The ICONS project focus technological areas and the project objectives cross-reference.................... 72 Table 14. The ICONS focus technological area modules and the research stream workpackages........................ 75 IST-2001-32429 ICONS Intelligent Content Management System page 8/86
  9. 9. Intelligent Content Management System 1.15 Introduction April 2002 1. Introduction 1.1 Objectives The ICONS project presentation represents a refinement of the technical project specification comprised in the ICONS project proposal and the ensuing Work Description [ICONS CONRACT] document developed as the addendum to the research contract with the European Commission. It also reflects the commitments of project partners represented in the Consortium Agreement. The primary objective is to present the current ICONS consortium views on the scope and directions of the research and development work specified in the project work description as well as on the methods and techniques to reach the stated project objectives. It is assumed that the project presentation document reconciles diverse approaches to attainment of the project objectives proposed by the project consortium partners and harmonises the initial research work on standards, research and technological terms of reference of the ICONS project. Although the preliminary ICONS architecture representing the functional scope of the project has been defined in the Work Description document [ICONS CONTRACT], a flexible approach is adopted to allow for changing views of the project team members, influenced by the ongoing research and development activities in the knowledge management field. Hence, the ICONS Project Presentation is to evolve, under the constraints of the project change management procedure [ICONS D2], to be published as new versions of the document. Each new version of the project presentation is to highlight the important changes with respect to the previous technical approach and the scope of work. The principal project change management rule indicates, that the scope of the project and the corresponding ICONS architecture may not be changed without the written consent of ICONS Project Officer representing the European Commission. 1.2 Scope The scope of this report covers the entire research and development work currently under way in the ICONS project. 1.3 Relations to other documents This report provides a baseline specification of the principal directions of the research and development work to be developed within the ICONS project. In this sense the report represent the consensus of the ICONS consortium members regarding the ICONS architecture and principal features as well as with respect to responsibilities and development tasks comprised in the project development plan. All ensuing technical documents to be produced within the ICONS project should not contradict the design decisions and research assumptions comprised in this report. Should there arise a need to modify the underlying assumptions of the ICONS project development philosophy, appropriate changes will be applied to this report to be published as the succeeding version. 1.4 Intended audience The intended audience comprises all members of the ICONS project consortium as well as the representatives of the European Commission monitoring and evaluating the progress of the project research and development work. 1.5 Usage guidelines The contents of the ICONS Project Presentation must be known to and evaluated by all by all members of the project team. Since the document is to represent the current consensus of the ICONS consortium, it is mandatory that no important deviations from the presented ICONS architecture and the principal technical directions, as represented in the current version of this document, are allowed. 1.6 Notation conventions No special notation conventions are used in this report. IST-2001-32429 ICONS Intelligent Content Management System page 9/86
  10. 10. Intelligent Content Management System 1.15 The ICONS Project Goal and Objectives April 2002 2. The ICONS Project Goal and Objectives Turning information into knowledge has been one of the principal goals of advanced information systems developed in all realms of social and economic life of modern societies. Terms like “knowledge management”, “knowledge engineering” and “knowledge bases” became ubiquitous in corporate board rooms as well as IT departments. Easy access to information enabled by the explosion of Internet technologies has created new problems related to exponentially growing wealth of information sources flooding the information system users. Many advanced information systems are focused on knowledge bases comprising large collections of facts, rules, and heuristics pertaining to a specific application domain. Such knowledge bases are typically divided into two principal parts, namely the content base comprising repositories of mutlimedia information objects and ontologies representing formal knowledge pertaining to the corresponding application domain. Our goal is to develop a prototype of an Intelligent CONtent management System (ICONS) supporting a uniform, knowledge-based access to distributed information resources available in the form of web pages, pre-existing heterogeneous databases (formatted, text, and multimedia), business process specifications and operational information, as well as legacy information processing systems. The principal objectives of our research and development project are to obtain and present novel results in the areas of knowledge representation and inference, heterogeneous information integration, and user- friendly interfaces based on advanced information architecture techniques. The overall approach of the ICONS project is to: (a) provide effective methods for analysing and modelling, (b) develop practical tools for exploiting and using, (c) assess in a pilot system the usefulness of ... an intelligent content management system with advanced knowledge management capabilities integrating internal content repositories with external heterogeneous information sources. To achieve these overall objectives four streams of technical work can be identified comprising the above operational goals: Objective 1: Development of knowledge representation techniques and methodologies for a multimedia content repository. The following specific research problems must be addressed in order to develop the knowledge representation capabilities of ICONS: (a) Application of semantic data models (UML) and deductive data base mechanisms as the domain ontology specification tool. (b) Extraction of knowledge embedded in XML documents and in the associated RDF specifications. (c) Representing knowledge embedded in the schemata of pre-existing heterogeneous databases and legacy information processing system outputs. (d) Design and implementation of an efficient, non-procedural content management framework providing content and knowledge model definition and query capabilities. (e) Development of mechanisms for procedural knowledge definition and its further exploitation in the area of effective knowledge and business processes management. Results obtained in the above research areas will be embedded in the ICONS prototype and they will be verified in the pilot application environment. The principal research approach is to create synergies by integrating known research results in novel configurations and contexts, as well as extending known results in order to meet the identified new requirements. Objective 2: Development of user interface design and management tools meeting the requirements of the information architecture methodology The user interface requirements fall into three distinct areas, namely the user tool set and dialogue model, the content presentation model, and the graphical knowledge presentation and manipulation model. All of the above presentation models must incorporate personalisation capabilities in order to enable dynamic adjustments to changing user preferences discerned from the system usage patterns. IST-2001-32429 ICONS Intelligent Content Management System page 10/86
  11. 11. Intelligent Content Management System 1.15 The ICONS Project Goal and Objectives April 2002 The information architecture methodologies and techniques are considered to be the prime requirements for design and implementation of the ICONS user interface management functionality. The multi-disciplinary research involves skills of industrial designers, psychologists, and computer scientists. The ICONS prototype and pilot application work is to provide a realistic test-bed for the proposed user interface management techniques. Objective 3: Design and implementation of efficient algorithms for management of large, distributed multimedia content repositories There are two dimensions of the ICONS content distribution. The first pertains to distribution of the system content repository comprising the Content Base and the Ontology Base and the hierarchical storage management processes among the ICONS servers. The second concerns integration of external information sources, such as pre-existing heterogeneous databases, legacy information processing systems, and web information resources. Distribution of the ICONS components among the system servers requires efficient load balancing algorithms inter-operational with the selective content and ontology replication mechanism. Research will also concentrate of adaptive data cashing techniques and the multi-criterial data distribution optimisation. Integration of the external information resources is to be performed with the use of the XML wrapper technology. Wrapper programs producing required XML envelopes for extracted data are to be enriched with RDF specifications resulting from extracting semantics from database schemata, in the case of the external databases, or representing semantics, in the case of the legacy information processing system outputs. The wrapper programs will be generated in the form of Enterprise Java Bean modules comprising the necessary query statements. Objective 4: Develop an analysis and design methodology for large, knowledge-based content repository systems. The multimedia content repositories with knowledge representation capabilities require a novel approach to the analysis and design methodology. An application development life-cycle and the associated methods and techniques will be specified and a pilot application of ICONS will be developed. The pilot application is to be the “Best practices of PHARE, SAPARD, and ISPA projects developed within the Newly Associated States” content repository accessible on the Internet. The aim is to present the viability of the proposed methodology and to provide a starting point for the clearly needed knowledge source. IST-2001-32429 ICONS Intelligent Content Management System page 11/86
  12. 12. Intelligent Content Management System 1.15 Feature Requirements of a Knowledge Management System April 2002 3. Feature Requirements of a Knowledge Management System Our objective is to confront the contemporary requirements of the fast growing knowledge management field with the current views on the KMS feature architectures as well as with the already existing IT technology pertaining to the KM realm. 3.1 Knowledge Management: A Framework for User Requirements The knowledge management field has been growing dynamically fuelled by intensification of the global competition in all principal areas of the world economy. The state of the KM field at the turn of centuries is illustrated by a study of 423 corporations performed by KPMG (KPMG1999). The scope of the KM activities in the study sample is presented in Figure 1. KM has been abolished KM is not currently planned 1% 19% KM is currently in operation 34% KM is currently considered 17% KM is currently being implemented 29% Figure 1. The scope of KM activities in 423 corporations surveyed by KPMG [KPMG1999]. High interest in the field was evident (80% of corporations in some stage of KM activities) at the time of the study and judging by the increasing number of trade conferences and exhibitions pertaining to the KM field the discipline has reached maturity. The principal questions from our point of view, to be discussed in this section, are (i) what is the role of IT as the enabling technology?, and (ii) what extension of the currently available information management platforms is required in order to meet the growing requirements of the KM field? The second question has been the root of the ICONS project proposal, so the proper identification of the added value for the KM field emerging from the project is of paramount importance to the project consortium. A critical appraisal of the state-of-the-art of the content management system area, massively claiming to provide direct support for KM, should provide the initial vantage point for evaluation of the ICONS project contribution. We commence with a brief overview of the requirements of the KM field identified in a number of research studies performed in the realm of the European KM Forum [KMForum2001]. We also consider views of the US knowledge management research community comprised in the research papers representing the current views of the Knowledge Management Consortium International (KMCI) [Firestone2000, McElroy1999] and focusing the KM research and practice in the USA [Garvin1993, Quinn1996, Baek1999, Becker1999, Coleman1999, Davenport1999, Huntington1999]. The common fallacy of the IT side of the KM scene is focusing on the purely technological view of the field with the tendency to highlight features that are already available in advanced content management systems. Such systems are commonly referred to as corporate portal platforms or, more to the point, as the knowledge portal platforms. From the KM perspective, as discussed in [McElroy1999], such claims may be justified only with respect to a narrow view of the field focusing on distribution of existing knowledge throughout the organisation. The above views, called by some authors the “First Generation Knowledge Management (FGKM)” or “Supply-side KM”, provides a natural link into the realm of currently used content management IST-2001-32429 ICONS Intelligent Content Management System page 12/86
  13. 13. Intelligent Content Management System 1.15 Feature Requirements of a Knowledge Management System April 2002 techniques, such as groupware, information indexing and retrieval systems, knowledge repositories, data warehousing, document management, and imaging systems. We shall briefly refer to existing content management technologies in the ensuing sections of the report to show that, within the above narrow view, the existing commercial technologies meet most of the user requirements. With the growing maturity of the KM field the emerging opinions are that IT support for accelerating the production of new knowledge is a much more attractive proposition from the point of view of gaining the competitive advantage. Such focus, exemplified in stated feature requirements for so called “Second Generation Knowledge Management (SGKM)”, is on enhancing the conditions in which innovation and creativity naturally occur. This does not mean that such FGKM required features as systems support for knowledge preservation and sharing are to be ignored. A host of new KM concepts, such as knowledge life cycle, knowledge processes, organisational learning and complex adaptive systems (CAS), provide the underlying conceptual base for the SGKM, thus challenging the architects of the new generation Knowledge Management Systems (KMS). The Knowledge Life Cycle (KLC), developed within the KMCI sponsored research [Firestone2000], provides us with the high-level feature requirements abstraction to be used as the starting point for evaluation of the ICONS architecture. The KLC as proposed by KMCI is presented in Figure 2. Knowledge Knowledge Knowledge Organizational Knowledge Production Claims Validation Knowledge Integration •Individual and group interaction •Knowledge claim peer review •Knowledge sharing and transfer •Data/Info acquisition •Application of validation criteria •Teaching and training •New knowledge claims •Weighting of value in practice •Operationalizing new knowledge •Initial knowledge codification •Formal knowledge codification •Production of knowledge artifacts Experiental feedback loop Figure 2. The Knowledge Life Cycle (KLC). The concepts underlying the KLC model of knowledge management comprise the notion of a Natural Knowledge Management System (NKMS) defined in [Firestone2000] as “the on-going, conceptually distinct, persistent, adaptive interaction among intelligent agents: (a) whose interaction properties are not determined by design, but instead emerge from the dynamics of the enterprise interaction process itself, (b) that produces, maintains, and enhances the knowledge base produced by the interaction”. The above definition of the knowledge management system fits the notion of a complex adaptive system (CAS) defined as “a goal-directed open system attempting to fit itself to its environment and composed of interacting adaptive agents described in terms of rules applicable with respect to some specified class of environmental inputs” [Holland1995]. In order to keep compatibility with our project terminology we shall distinguish two classes of actors interacting within the KM environment; human beings called employees or knowledge workers, and knowledge-based computer programs called intelligent agents. A thorough discussion of the intelligent agent technology may be found in [Baek1999] while a taxonomy of intelligent agent knowledge-based features is presented in [Huntington1999]. The Knowledge Base (KB) of the system is “the set of remembered data, validated propositions and models (along with metadata related to their testing), refuted propositions and models (along with metadata related to their refutation), metamodels, and (if the system produces such an artifact) software used for manipulating these, pertaining to the system and produced by it” [Firestone2000]. IST-2001-32429 ICONS Intelligent Content Management System page 13/86
  14. 14. Intelligent Content Management System 1.15 Feature Requirements of a Knowledge Management System April 2002 A knowledge base, not necessarily meant as the IT-related concept, constitutes the principal element of any knowledge management system and therefore requires a more detailed consideration. There are emerging schools of thought, deviating from the popular definition of knowledge as the “justified, true belief” [Goldman1991] in several important aspects. First of all, the knowledge base is to comprise justified knowledge, where justification is specific to the validation criteria used by the system (note, that such validation criteria may vary from organisation to organisation), and, although the definition is consistent with the idea, that individual knowledge is a particular kind of belief, the notion of belief extends beyond cognition alone to evaluation. The concept of the learning organization, defined in [Garvin1993] as “an organization skilled at creating, acquiring, and transferring knowledge, and at modifying its behaviour to reflect new knowledge and insights”, provides an important context for the KMS feature analysis. Garvin introduces five main activities, acting as the building blocks of a learning organization, namely; “systematic problem solving, experimentation with new approaches, learning from one’s own experience and past history, learning from experiences and best practices of others, transferring knowledge quickly and efficiently throughout the organization”. Attributes of a learning organization, important for management of professional intellect, have been identified in [Quinn1996]. The intellectual capital of an organization comprises such elements as: cognitive knowledge (know what) – the basic mastery of a discipline that professionals achieve through extensive training and certification, advanced skills (know how) – the ability to apply the rules of a discipline to complex real-world problems, systems understanding (know why) – deep knowledge of the web of cause-and-effect relationships underlying a discipline, and self-motivated creativity (care why) – the will, motivation and adaptability for success. An important notion discriminating between the content management systems and the knowledge management systems is that of the domain ontology defined in [Becker1999] as “an explicit conceptualization model comprising objects, their definitions, and relationships among objects”. A well-defined terminology, called taxonomy [Letson2001], is used within a particular ontology to describe the classes of objects, their properties, and relationships. Domain ontologies are important elements of knowledge management systems, quite similar to the conceptual schema of the database management model, serving to organize the knowledge of an organization. Thus, the domain ontology management features of a knowledge management system directly pertain to modelling of knowledge. We concentrate on two distinct, but compatible, views pertaining to modelling of knowledge, represented by the seminal work of Popper [Popper1971, Popper1977], and by the generally accepted views of Nonaka and Takeuchi [Nonaka1995]. The above results directly relate to the KLC model, thus providing a base for the ensuing discussion of feature requirements for a knowledge management system. Popper’s views the body of knowledge existing in an organisation as three distinct worlds, namely; (a) the first world (World 1) made of material entities: things, oceans, towns etc., (b) the second world (World 2) made of psychological objects and emergent predispositional attributes of intelligent systems: minds, cognitions, beliefs, perceptions, intentions, evaluations, emotions etc., (c) the third world (World 3) made of abstractions created by the second world acting upon the first world objects. This approach provides us with a two-tier view of knowledge: 1. Knowledge viewed as a belief is a second world predispositional object. This pertains to such situations, where individuals, groups of individuals, and organizations, hold beliefs (subjectively considered to be true), that are immediate precursors of their decisions and actions. The predispositional knowledge is “personal” in the sense that other individuals have no direct access to one’s own knowledge in full detail and therefore can not either “know it” as their own belief, or validate it. 2. Knowledge viewed as validated models, theories, arguments, descriptions, problem statements, etc., is a third world linguistic object. One can talk about the truth, or nearness to the truth of such knowledge, defined as the above third world objects in terms of being closer to truth then those hold by the competitors. This kind of knowledge is not an immediate precursor of decisions and actions, it rather impacts the second world beliefs and these, in turn, impact the behaviour of the KMS actors. Such knowledge is objective, in the sense that it is not agent specific and is shared among agents. The above characteristics bring to the forefront the issue of community validation of the shared knowledge. Looking at the above two distinct categories of knowledge, we may conclude, that the third world knowledge is the principal product of a knowledge management system. Whereas the knowledge of the individuals in a social organisation is not produced by the system alone, although it may be strongly influenced by interaction with the objective knowledge represented by the third world abstractions. IST-2001-32429 ICONS Intelligent Content Management System page 14/86
  15. 15. Intelligent Content Management System 1.15 Feature Requirements of a Knowledge Management System April 2002 Importance of a widely recognized distinction between tacit and explicit knowledge, first introduced by Polonyi [Polonyi1966], is emphasized by the work of Nonaka and Takeuchi [Nonaka1995]. The principal idea is that knowledge is created by interaction between tacit and explicit knowledge presented schematically in Figure 3. Note, that the above two knowledge base models are compatible, since the tacit vs. explicit knowledge distinction corresponds closely to Popper’s subjective (World 2) vs. objective knowledge (World 3) distinction. Considering the knowledge categorisations and transformations from the organizational knowledge point of view, constituting the principal knowledge management perspective, we view the following aspects of the model as crucial from the knowledge creation process perspective: Tacit Explicit knowledge To knowledge Tacit knowledge Socialisation Externalisation From Explicit Internalisation Combination knowledge Figure 3. Four processes of knowledge conversion [Nonaka1995]. 1. Transformation from tacit to explicit knowledge. The process corresponds to the externalisation transformation of Nonaka and Takeuchi and that of abstracting the objective knowledge, or transformation of World 2 beliefs into the World 3 objective knowledge, in Popper’s model. The process corresponds to the knowledge claim formulation in the KLC. However, in view of the KLC model, knowledge claims do not constitute the “objective knowledge’ until they successfully pass the knowledge validation process. Only then the validated knowledge claims become the organisational knowledge, after having been formalised and edited in the knowledge integration process of the KLC. 2. Transformation from tacit to tacit knowledge. The process corresponds to the socialisation transformation of Nonaka and Takeuchi as well as to sharing of “personal” knowledge by intelligent agent interactions implied in Popper’s approach. The process, although does not create “new” organisational knowledge may be crucial to maintaining and enhancing the competitive advantage of many creative organisations (e.g. a software company). This transformation fits into the knowledge production process of the KLC. 3. Transformation from explicit to tacit knowledge. The process corresponds to the internalisation transformation of Nonaka and Takeuchi and to the “impact” of the objective knowledge on the World 2 beliefs, and consequently on the organizational decision making process, presented in Popper’s model. This transformation matches closely the knowledge operationalization step of the knowledge integration process of the KLC. Although no new knowledge is produced at this stage, the transformation may be very important for highly innovative organizations. We do not consider the explicit knowledge combination to be relevant to knowledge management, since either a mechanical process of external knowledge takes place through some mechanism of information categorisation, or an intelligent agent must be involved in inferring new knowledge from a combination of external knowledge artifacts. In the latter case, other transformations, namely the internalisation-externalisation path, would have to be followed. A distinction must be made at this stage between knowledge management, dealing with the above classes of structural and procedural knowledge, and information derived from information systems supporting the daily operation of an organisation. Data and results of such information systems are considered, for the sake of our IST-2001-32429 ICONS Intelligent Content Management System page 15/86
  16. 16. Intelligent Content Management System 1.15 Feature Requirements of a Knowledge Management System April 2002 KMS feature requirement analysis, to be a representations of Popper’s World 1 entities and their relationships and are, therefore considered merely objects of the KMS actors’ activities and decisions. A similar view is taken with respect to ad hoc or unstructured business processes with flows determined by subjective knowledge of an intelligent agent, rather then by a validated artifact of objective knowledge. An artifact of the objective procedural knowledge may be, for example, a formal workflow definition controlling execution of all processes belonging to a given class. The above discussion sets the stage for an analysis of the principal feature requirements pertaining to the distinct knowledge management processes of the KLC and to the characteristics of the knowledge transformations underlying the knowledge production process. Note, that the KMS features are technological categories providing a taxonomy for user functions viewed collectively as the KMS architecture and, as such, they should be discussed in the context of the knowledge management processes present in the KLC. We relate the KMS features to the knowledge management processes in Table 1. KLC Knowledge Knowledge Knowledge KMS features Production (KP) Validation (KV) Integration (KI) Domain Ontology (DO) DO-KP DO-KV DO-KI Content Repository (CR) CR-KP CR-KV Knowledge Dissemination (KD) KD-KP KD-KV KD-KI Content Integration (CI) CI-KP CI-KV Knowledge Security (KS) KS-KI Actor Collaboration (AC) AC-KP AC-KV AC-KI Table 1. Cross-reference between the KM processes and the KMS features. The user functions clustered in the principal KMS features may play varying support roles within the knowledge management processes. Collectively, the sum of user requirements for a given principal feature, defined within the distinct knowledge management processes, represents the user requirement set for a given principal KMS feature. We discuss the support role semantics corresponding to the principal KMS features in Table 2. The principal KMS features serve as the basic building blocks for the reference KMS architecture presented in the ensuing section. Feature role Feature role semantics DO-KP The domain ontology functionality supports: 1. The externalisation transformation by providing the KMS actor with the means for the initial knowledge codification during formulation of knowledge claims. Codification is performed on both declarative and procedural knowledge. 2. Referencing the content artifacts providing supporting evidence or providing the fact base for knowledge inference. The reference information provides a knowledge map serving as the principal access path to the content repository. DO-KV The domain ontology functionality supports: 1. The formal knowledge codification pertaining to the validated knowledge claims. 2. The formal specification of the models and rules supporting the knowledge claim screening and validation activities, in particular those involving complex networks of experts. DO-KI The domain ontology functionality supports: 1. The internalization transformation by providing means to interpret and learn from objective knowledge as well as to find reference to supporting evidence exemplified in the real world cases comprised in the content repository. 2. The socialization transformation by providing means to find reference to peer expertise and work results, including formulation of knowledge claims, thus fostering interaction between the KMS actors. CR-KP The content repository comprises all content artifacts, actual and virtual, that support the daily operation of an organization. In this sense, the content repository provides the principal platform of information processing support for the knowledge worker (a KMS actor that uses and/or produces knowledge) activities. The knowledge map, provided by the KMS domain ontology, defines the structure and scope of the content repository. IST-2001-32429 ICONS Intelligent Content Management System page 16/86
  17. 17. Intelligent Content Management System 1.15 Feature Requirements of a Knowledge Management System April 2002 CR-KV The content repository provides the body of supporting evidence as well as the documentation means for the knowledge claim validation activities. Information comprised in the content repository may be used and processed during the normal activities of knowledge workers and it may be the basis for new knowledge claim formulations. CR-KI N/A KD-KP The body of organisational knowledge, formally codified in the domain ontology, and supported by information comprised in the content repository, must be accessible to the knowledge workers in order to influence their subjective beliefs and predispositions (tacit knowledge) and thus to impact their activities and decisions. The quality of systems support for this process determines the efficiency of the knowledge externalization transformation fundamental for the knowledge production process. KD-KV The knowledge claim validation process may heavily depend on the existing body of information, accessible through the content repository, as well as on the already validated and integrated objective knowledge pertaining to the subject domain. The validation process typically involves complex, and variable, interactions among experts drawing upon declarative as well as procedural knowledge. The quality of systems support, as in the case above, is of paramount importance to the efficiency of the validation process, which, additionally, must be supported by complex and flexible workflow procedures representing the procedural knowledge. KD-KI The dissemination functionality supports the principal facets of the knowledge integration process, namely the knowledge sharing and transfer, as well as teaching and training. Both the codified objective knowledge and the supporting information must be made available. CI-KP Information represented in content artifacts may, either be created and retained in the content repository, or may be derived from heterogeneous information sources, usually maintained by external information systems. The derived content artifacts may be stored in the repository or they may be materialized on demand by the appropriate interaction with the external source. The content integration functionality entails selection and retrieval of structured and semi-structured information, homogenization into a common content model, and derivation of semantics into the domain ontology representations. CI-KV Same semantics as above. CI-KI Same semantics as above. KS-KP N/A KS-KV N/A KS-KI The organisational knowledge comprised in the KMS, both in the form of the codified objective knowledge artifacts, and of the supporting information artifacts, represents an important part of the intellectual capital. Hence the system integrity and privacy must be maintained. AC-KP Interaction of knowledge workers is the basis of socialization processes. Interaction may be spontaneous, or it may result from a, more or less formally, specified and supported procedure. Automatic support for such interactions may vary from typical groupware functions, such as chat rooms and messaging, to advanced ontology-based workflow procedures. An important by-product of automatic support may be the possibility to capture operational metrics characterising the knowledge production process. AC-KV Knowledge claim validation may entail interactions within a complex network of experts, both internal and external to the organisation, using a variety of information processing environments. As in the case above, supporting expert interaction, possibly involving also intelligent agents, may be a critical success factor of the knowledge claim validation processes. AC-KI Production of the objective knowledge artifacts and of the supporting content, inherent in the knowledge integration process, may require well-defined editorial procedures. Such procedures may typically be supported by automatic workflow management functionality. The requirements may vary from simple groupware-like support to complex, ontology-based workflow management environments. Table 2. Feature roles within the knowledge management processes. Further analysis of the KMS feature requirements in the context of the knowledge life-cycle, leading to development of Use Case models [Rumbaugh1999] to be used for design and validation of the ICONS architecture, is to be performed in the succeeding phases of the ICONS project. We believe that the above discussion provides sufficient user requirements context for the ensuing presentation of the KMS reference IST-2001-32429 ICONS Intelligent Content Management System page 17/86
  18. 18. Intelligent Content Management System 1.15 Feature Requirements of a Knowledge Management System April 2002 architecture. The reference architecture is to provide a beacon for the further unfolding of the research and development work of the ICONS project. Within the document we use several types of the knowledge. Figure 4 presents the ICONS knowledge taxonomy while Dictionary presents their meaning. knowledge declarative procedural knowledge knowledge structural knowledge knowledge-based knowledge maps reasoning Figure 4. ICONS taxonomy of knowledge. 3.2 The KMS Reference Architecture The European KM Forum [KMForum2001, KMForum2001_D11, KMForum2001_D11a, KMForum2001_D12] is an IST project with the goal to collect the current KM practices and to create an almost complete overview of the KM domain in Europe. The KMS reference architecture presented in Figure 5 has been developed on the basis of the current KM technologies discussed in the EKMF project reports, as well as on the KMS feature requirements identified in the preceding section. Full Knowledge Text Content Object Map Business Properties graphs Intelligence Data Systems Bases Taxonomies Knowledge Semantic SDM nets Dissemination nets Time Push modelling Content technology Web Pages Files Knowledge-based Integration Conceptual Semantic Domain reasoning trees nets Ontology Legacy Hyper-text Information Intelligent Document Semantic Data Process Systems Agents Management A Models graphs Knowledge Management RDF System XML Encryption Files Systems Knowledge Discussion Content Security Electronic Version Forums Repository signature control Access Control Knowledge KMS Actor Autenthication Engineering Collaboration HSM DBMS Rendering Workflow Message Management Exchange Internet Intranet Figure 5. The Knowledge Management System reference architecture. Table 3 presents the above presented feature requirements of a KMS reference architecture in the tabular form. IST-2001-32429 ICONS Intelligent Content Management System page 18/86
  19. 19. Intelligent Content Management System 1.15 Feature Requirements of a Knowledge Management System April 2002 Feature requirements of a Knowledge Management System Domain Content Knowledge Content Actor Security Ontology repository Dissemination integration Collaboration Semantic Nets XML Push technology Files Message Encryption exchange Conceptual trees RDF Content object Data bases Discussion Access Control repository forums Semantic data File systems Knowledge map Business Knowledge Authentication models graphs Intelligence engineering Process graphs Version control Full text Web pages Workflow Electronic management signature Hyper text DBMS Semantic data Legacy Internet/Intranet models net information systems Knowledge-based HSM Semantic nets Intelligent agents reasoning Time modelling Rendering Document management Taxonomies Table 3. Feature requirements of a Knowledge Management System. The KMS features, grouped into six principal feature sets, represent our current views pertaining to the KM technology requirements. Some of the features are already common in the advanced content management systems, referred to as the corporate portal platforms, some other are subject to the on-going KMS research efforts. We discuss each of the principal feature sets in more detail in order to define reference feature requirements for the ICONS architecture presented in the succeeding section. 3.2.1 Domain Ontology features The Domain Ontology features pertain primarily to knowledge representation including the declarative knowledge representation features, such as taxonomies, conceptual trees, semantic nets, and semantic data models, as well as the procedural knowledge representation features exemplified by the process graphs. Time modelling and knowledge-based reasoning features pertain both to the declarative and the procedural knowledge representations. Hyper-text links are considered as a mechanism to create ad hoc relationships between content artifacts comprised in the repository. Taxonomies Taxonomies provide means to categorize information objects stored in the content repository. Categorisation classes may be arbitrary hierarchical structures grouping information objects selected by the class predicates. Class predicates are defined in the form of queries comprising information object property values or as full text queries comprising key word and/or phrases. Categorisation classes are not necessarily disjoint. Dictionaries are a special class of taxonomies, also organized into hierarchical structures, which may comprise any number of categories, usually corresponding to occurring information object property value (e.g. a name directory) with the maximum number of categories equal to the cardinality of the property value domain. Automatic categorisation of information objects may also be based on arbitrary functions defined on object property values and/or content and implemented as an arbitrary analytical algorithm or a knowledge-based reasoning function. In the latter case, an inference engine provides for the actual categorisation of information objects. Analytical algorithms provide for automatic categorisation of formatted data objects, textual objects, as well as multimedia objects, such as audio, images and video frames. Taxonomies provide a powerful navigation device for browsing the content repositories, since they usually represent intuitive semantics of the user information requirements. Conceptual trees Conceptual trees are also a categorisation device used in conjunction with full text queries providing means to define concepts on the basis of its hierarchical relationships with other concepts, key words, and phrases. Usually IST-2001-32429 ICONS Intelligent Content Management System page 19/86
  20. 20. Intelligent Content Management System 1.15 Feature Requirements of a Knowledge Management System April 2002 conceptual trees allow for the full text query relevance ranking. This technique allows for easy extension of the domain ontology terminology with the use of, usually abstract, concepts with arbitrarily rich semantics. Semantic Nets Semantic networks provide means to represent binary 1:1 relationships, expressed usually as named arcs of a directed graph, where vertices are information objects belonging to any of the information object classes. Normally, the linked object classes are determined by the binary relationship semantics of the corresponding named arc. An example of a simple semantic net may be a binary relation Descendants defined as a subset of the Cartesian product of the set of Persons. Semantic nets may be constructed over an arbitrary number of information object classes and binary relationships. Semantic Data Models The Unified Modelling Language (UML) [Rumbaugh1999] is the currently prevailing specification platform for semantic data models allowing for definition of structural as well as behavioural semantics. Class Association Diagrams provide easy to read, intuitive semantics closely matching the mental models of the KMS users. The UML-based knowledge representation, in order to be useful, must be supplemented with a navigation facility allowing the user to transverse the network of specified object associations and to view/retrieve the corresponding object sets. Hyper-text links The hyper-text links support referential link semantics that may exist among the information objects belonging to arbitrary object classes existing in the content repository. The ad hoc character of hyper-text links, usually no schema level information exists, limits their usefulness as a knowledge representation feature. However, they are a useful annotation tool to express, possible transient, referential relationships of information objects stored in the content repository. Time modelling Time represented in domain ontologies, as well as in the content repository, conveys important information. Time valued properties may be important elements of search and automatic categorisation operations. Hence, formal representation of time is of paramount importance for knowledge descriptions and content characterization. Problems that exist today are related to the lack of standard representation of time instances and periods, incompatible time scales, granularities as well as periodicity definitions. Precise rules must be established as to representation and treatment of temporal properties to be comprised in a knowledge management system. Time modelling is also an important element of the procedural knowledge representation. CPM-like (Critical Path Method) have been proposed for representation of time constraints and for optimisation of process execution times in advanced workflow management systems. Knowledge-based reasoning Knowledge-based (k-b) reasoning systems may be built for a wide range of decision-making problems. The reasoning is based on a collection of facts, usually represented by content property values, and heuristics represented as rules. The prevailing paradigms are production rules (forward and backward chaining), logic programming, and neural nets (reasoning about quantitative data). The k-b reasoning may be used for expert knowledge representation, knowledge and content categorisation and distribution, as well as for the intelligent agent implementation. Intelligent workflow management is a new application area for k-b reasoning both for process routing as well as for the dynamic role modification. Process graphs Business processes are usually represented by process graphs, typically by the Event-Condition Petri Nets or by directed graphs. Petri Net representation allows for expressing richer process semantics, in particular the pre-and post-conditions for process activities. The process specification must also be supplemented by the set of role definitions, one definition for each process activity, to enable the workflow management engine to properly assign tasks to KMS actors. The process graph representation should comprise a set of process metrics and, possibly, performance constraints and exception conditions. IST-2001-32429 ICONS Intelligent Content Management System page 20/86
  21. 21. Intelligent Content Management System 1.15 Feature Requirements of a Knowledge Management System April 2002 3.2.2 Content Repository features Extensible Markup Language (XML) Light version, tag-oriented meta-language of SGML standard adapted to the web that provides facilities to describe and diffuse structured documents through Internet. Also used as the emerging industry standard for exchange of data between information systems as well as for storage and retrieval of complex, multimedia objects in content repositories. Resource Description Facility (RDF) Extension of XML used to define complex relationships between documents or data. Popular as the target data structure for mapping UML semantics into the content repository data models. RDF schema is used as a template to define annotation in RDF syntax. File Systems File systems are commonly used in multimedia content repositories to serve as containers for large content objects represented as files. The use of file systems is a convenient technique for mapping content onto diverse hardware storage devices in order to exploit their inherent characteristics. E.g. for permanent non-modifiable storage of electronic documents an optical storage device may be used. File systems are composed into storage hierarchies usually controlled by the content repository management software. Hierarchical Storage Management The hierarchical storage management (HSM) functions control allocation of storage space available in a hierarchy of storage devices to large content object files. Such systems are based on a directory of all content objects including information pertaining to storage allocation rules and migration predicates. Content objects are automatically migrated up and down the storage hierarchy, where the top layer is the object-relational database management system, and the bottom layer may be an optical storage jukebox or a mass storage tape system. Migration predicates usually determine content object residence time at any given storage hierarchy level and serve to fire the storage allocation rules controlling the file migration operations. Database Management System (DBMS) Object-relational database management systems serve as an implementation platform for the domain ontology management functions and the content management functions. Solution architectures vary, yet a typical use would be for storage of all KMS directories and control blocks, for representation of the domain ontology data model, and for storage of content object files and attributes. Main memory relational database management systems may also be used to store frequently used ontology structures as well as to provide a platform for representing data structures representing facts in knowledge-based reasoning algorithms. Version control Content evolves over time. In some cases history of content change is as much important as the content itself. The versioning mechanism allows for transparent identification (incremental revision number) and storage (either full version or increments) of particular versions of content and content object properties. Access schemas pertaining to multiuser access problems is the neighbouring subject. Rendering Content is held within the repository in a variety of native formats. Therefore the content can also be viewed or edited in the tool that originally created the content. However, a uniform web based browser requires rendering that facilitates for presenting all of them in a consistent way. Content can be rendered and renditions include HTML and XML, as well as PDF and other well know formats. 3.2.3 Knowledge Dissemination features Push Technology Push technologies providing facilities for automatic supply of selected content objects to a predefined group of recipients (a role), who are usually the KMS actors (knowledge workers, intelligent agents), are the best approach to combat the information glut. The push technologies are strongly correlated with such knowledge representation features as the automatic content categorisation and knowledge-based reasoning. IST-2001-32429 ICONS Intelligent Content Management System page 21/86
  22. 22. Intelligent Content Management System 1.15 Feature Requirements of a Knowledge Management System April 2002 Content Object Properties Content object properties characterize the principal object properties, such as object identifier, origin, author(s), date, etc, as well as provide information, usually in the form of key words, characterizing the content. The latter type of properties are usually obtain at the object creation (storage) instant through automatic content analysis and categorisation, or through a manual content object description process (e.g. description of an ancient manuscript image). Either way the content object properties provide a convenient access path for content repository queries, taxonomy structure allocations, and for materialisation of content object relationships. Full Text Full text indexing and retrieval is a classical approach to content management. The full text retrieval techniques, used in conjunction with conceptual trees, are commonly used in automatic categorisation features. Often content object property values are automatically obtained through a full text search-based categorisation process. Knowledge Map Graphs Multi-level taxonomy trees, semantic nets and content object associations are usually represented as graphs on the user interface level. This fits nicely with the user mental model of the domain ontology structure and its relationships with the underlying content object model. Because of substantial scope and complexity of knowledge map advanced graph construction and manipulation techniques must be employed to provide the required ergonomic level of the KMS user interface. The knowledge map graphs are used, usually in a query mode, for navigation within the semantically meaningful structures and for browsing the associated content. Semantic Nets Graphic representation of semantic nets (SN-graphs), although quite straightforward, must be supplemented by manipulation functions supporting transversal, SN-graph node visualisation/retrieval, and SN-graph selection (entry). SN-graphs, representing a given semantic net class implementation, may either be materialised dynamically, or, usually in the case of complex association functions and large scope, may be cached as the persistent ontology structures. Transient storage and off-line semantic net materialisation techniques may be used to achieve the required KMS performance levels. Note, that the SN-graph navigation typically occurs at the content object instance level, where the SN-graph arc represents a 1:1 content object relationship. Semantic Data Model Nets SDM net graphs (SDM-graph) are envisaged as a representation of the UML graphic conceptual model notation. Hence, content object classes well represent subsets of the corresponding content object instances constrained by class association used for navigational selection. Hence, navigation, list manipulation, visualisation/retrieval, and SDM structure entry functions are necessary to exploit the rich semantic potential of navigation on the content object class level. Note, the as opposed to the SN-graph navigation presented above, the SDM-graph navigation yields subsets of content object instances at each visit at a corresponding SDM-graph node. The only similarity is the SDM-graph selection effected as selection of the entry content object instance (e.g. a particular Person occurrence). 3.2.4 Content Integration features All entities, regardless of their character (structural, procedural), participating in the content integration process must be accessible via the knowledge map graph, or via other existing access path to the content repository. Any of the integrated content objects, constrained by the corresponding descriptions of the content repository schema, may either be physically stored in the repository as a content object (snapshot, re-freshable), or may be dynamically materialised at the reference time. Usage of the above integration modes should be entirely transparent to the KMS user. Files Files feature among candidates for content integration, due to the widely diffused usage of file systems as repositories of large, multimedia content objects. Little, or no, analysis of the multimedia objects content, apart from the automatic categorisation analysis, is performed during the integration process. Data Bases Heterogeneous databases are a typical source of data for content integration. Multi-database query and integration techniques, as well as the homogenization of heterogeneous data models, are the underlying technologies. The most straightforward cases entail querying a single database to materialise the required content to be further exploited in the KMS context, either as an element of a content object stored in the repository, as a virtual content object materialised on-the-fly. IST-2001-32429 ICONS Intelligent Content Management System page 22/86
  23. 23. Intelligent Content Management System 1.15 Feature Requirements of a Knowledge Management System April 2002 Business Intelligence Systems Data warehouses and OLAP system deliver relevant knowledge content, that should be integrated into the KMS environment. The BIS-generated content may be integrated into repositories as elements of content objects or may be delivered dynamically. Legacy Information Systems Similarly, the legacy information systems are the source of content that may be relevant to the KMS users. Selected legacy system reports may be accessible as content objects, or their elements, via the KMS content repository. Intelligent Agents Intelligent agent (IA) technology is a rapidly growing area of research and new application development. Applications of IA technologies in the KMS context are discussed in [Baek1999]. The definition of an intelligent agent proposed by IBM [IBM1995] states that an intelligent agent is “a software entity that carries out some set of operations on behalf of a user or another program with some degree of independence or autonomy, and in so doing, employs some knowledge or representation of the user’s goals or desires”. The IA technologies are clearly useful and applicable in the KMS context, meeting two broad functionalities, that of a personal assistant or that of a communicating/collaborating agent. In both roles the intelligent agents are relevant as knowledge-based support for the content integration features. Document Management Systems Document management systems are a particular class of legacy information systems providing a rich content infrastructure directly relevant to the KMS users. Electronic documents and image-based information typically integrated into the KMS content repositories as principal factual knowledge artifacts. Some KMS architectures the document management functionalities are subsumed by the KMS features. Web Pages Paradoxically, the genuine knowledge is perfectly hidden in the enormous amount of data volumes that is available on web pages. Therefore even more intelligent and flexible mechanism are to be developed in the area of external knowledge acquisition and, what is even more important, keeping it up-to-date. Interoperability of systems and ability to choose the best offered content are of the primary importance. 3.2.5 Actor Collaboration features Message Exchange Instant messaging relevant to the socialisation process (tacit to tacit knowledge transformation) is an important vehicle supporting the knowledge production process. Hence, the KMS functionality should provide a platform for a semi-disciplined exchange of electronic messages that may subsequently be categorised and stored in the content repository. Some collaboration metrics, similar to activity measures used in e-learning systems, may also usefully applied for management of the knowledge production process. Discussion Forums Discussion forums are the electronic equivalent of the water cooler or cafeteria discussions, that have long ago been discovered as vital knowledge production activities. Again relevant and valuable statements and comments should be categorised, stored in the content repository and measures (e.g. attributed to the originating sources). Knowledge Engineering Knowledge-based reasoning applications and intelligent agents require analytical support to glean the expert knowledge out of individual (outstanding knowledge workers). The process of obtaining expert knowledge, required to build knowledge-based (or expert) applications, called traditionally knowledge engineering, requires specific methodologies and tools for the formal knowledge representation. Such tools may coincide with the knowledge representation paradigms used, both for declarative and procedural knowledge, within a specific KMS environment. Workflow Management The workflow management technology is an important platform supporting, both the knowledge management processes of the KLC and the business processes of the organizations. In the latter case, application of the workflow technology provides in-sight into the organization operations that is an important feed back into the knowledge production process. In fact it may be disputed that, in the case of organizations where knowledge management in an explicit management function, the KLC process may be considered to belong to the realm of IST-2001-32429 ICONS Intelligent Content Management System page 23/86

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