The Master's Thesis is focused on methods and tools in knowledge management. The paper introduce knowledge management its history and present. Meanwhile history is presented as a list of important events present view is supported by current trends in field of knowledge management, business analysis of selected companies and evaluation of available publications on the topic of knowledge management. The main aim the paper is to present suitable methodology for the initial phase of Mitroff's model - conceptualization. The selected approach is then applied to the model situation. It is the decision of the city government for the construction and expansion of local factory. The last part is about Business Model Canvas which aims to outline the possible application of the methodology in practice.

This study examined the level of trust of information on social media. Specifically, I investigated the factors of performance expectancy with information-seeking motives that appear to influence the level of trust of information on various social network sites. This study utilized the following theoretical models: elaboration likelihood model (ELM), the uses and gratifications theory (UGT), the unified theory of acceptance and use of technology model (UTAUT), the consumption value theory (CVT), and the Stimulus-Organism-Response (SOR) Model to build a conceptual research framework for an exploratory study. The research investigated the extent to which information quality and source credibility influence the level of trust of information by visitors to the social network sites. The inductive content analysis on 189 respondents’ responses carefully addressed the proposed research questions and then further developed a comprehensive framework. The findings of this study contribute to the current research stream on information quality, fake news, and IT adoption as they relate to social media.

Gegenstand dieser Arbeit ist die Konstruktion eines Ordnungsrahmens für den systemischen Umgang mit Wissen im Enterprise 2.0. Dieser Ordnungsrahmen zeigt Einsatzpotentiale und Handlungsfelder des Enterprise 2.0-Konzepts für Wissensmanagement systematisch auf und vermittelt ein Verständnis über die Zusammenhänge zwischen den Gestaltungsdimensionen Mensch, Organisation und Technologie im speziellen Fall des Enterprise 2.0. Ergänzend zu den theoretisch erarbeiteten Aspekten wird der konzipierte Ordnungsrahmen verwendet, um die Eignung eines realen Enterprise 2.0-Ansatzes (Wiki-Plattform) für Wissensmanagement zu bewerten.:ABKÜRZUNGEN III ABBILDUNGEN IV TABELLEN V 1 Einleitung 1 1.1 Problemstellung 2 1.2 Zielsetzung 2 1.3 Aufbau der Arbeit 4 2 Theorie des Wissensmanagements 6 2.1 Überblick zum Wissensmanagement 6 2.1.1 Probleme in Theorie und Praxis 8 2.1.2 Wissensbegriff und Abgrenzung 10 2.1.3 Wissensprozesse 12 2.2 Das SECI-Modell 13 2.2.1 Konzeptionelle Probleme 16 2.2.2 Kritische Würdigung 21 2.3 Systemisches Wissensmanagement mit IDEA 22 2.3.1 IDEA-Ordnungsrahmen 22 2.3.1.1 Interaktion 23 2.3.1.2 Dokumentation 24 2.3.1.3 Evolution 24 2.3.1.4 Adoption 25 2.3.2 Anwendung des IDEA-Ordnungsrahmens 25 3 Enterprise 2.0 28 3.1 Vom Web 2.0 zum Enterprise 2.0 28 3.2 Enterprise 2.0 als Organisationsparadigma 30 3.3 Social Software und Groupware 30 3.3.1 Anwendungsklassen 32 3.3.1.1 Wiki 32 3.3.1.2 Weblog 33 3.3.1.3 Social Networking-Dienste 33 3.3.1.4 Microblog 33 3.3.1.5 Social Tagging 34 3.3.1.6 Social Bookmarking 34 3.4 Systematisierung 34 4 Wissensmanagement im Enterprise 2.0 37 4.1 Erfolgsfaktoren 37 4.1.1 Interaktion 37 4.1.1.1 Identitäts- und Reputationsmanagement 39 4.1.1.2 Beziehungs- und Kollaborationsmanagement 39 4.1.1.3 Kultivierung von Communities 40 4.1.1.4 Robuste Wissensnetzwerke 40 4.1.2 Dokumentation 41 4.1.2.1 Webbasierte Wissensarbeit 41 4.1.2.2 Flexible Wiki-Anwendungsmodi 42 4.1.2.3 Motivation der Wissensarbeiter 44 4.1.3 Evolution 45 4.1.3.1 Perpetual Beta-Wissenskultur 46 4.1.3.2 Kreativität im Netzwerk 47 4.1.3.3 Kollektive Objektivierung 48 4.1.4 Adoption 48 4.1.4.1 Navigationskonzepte 49 4.1.4.2 Ganzheitliche Awareness 50 4.1.4.3 Kontextualisierung 51 4.2 Herausforderungen 52 4.2.1 Information Overload und Orientierungslosigkeit 52 4.2.2 Organisationale Destabilisierung 53 4.3 Neue Aufgaben des Wissensmanagements 53 5 Fallstudie: T-Systems MMS 56 5.1 Vorstellung des Unternehmens 56 5.2 Das TeamWeb 58 5.3 Zielsetzung und Vorgehen 60 5.4 Ergebnisse der Umfrage 61 5.5 Handlungsempfehlungen 66 6 Zusammenfassung und Ausblick 70 6.1 Ergebnisse der Arbeit 70 6.2 Kritische Betrachtung 72 6.3 Ausblick und weiterer Forschungsbedarf 73 LITERATUR VI GLOSSAR IV ANHANG IX

Ziel der Arbeit ist die Entwicklung und Prüfung eines semantischen Informationssystems zur Unterstützung des Informations- und Wissenstransfers bei kollektiver, internetbasierter Informationsarbeit – sowohl innerhalb des Teams, als auch in dessen Übertragung in das Unternehmen. Als Informationssystem wird dabei ein System verstanden, welches die menschlichen Aufgabenträger, die organisationale und technische Ausgestaltung, aber auch die zu unterstützende Aufgabe determiniert und beinhaltet und von der Systemumwelt abgrenzt. Der Fokus der Arbeit ist in den Gegenstandsbereich der Wirtschaftsinformatik einzuordnen. Die Wirtschaftsinformatik besitzt nach dem Verständnis des Autors einen transdisziplinären Charakter und der Nukleus der Fachdisziplin ist als Intermediär verschiedener beteiligter, angrenzender Disziplinen zu verstehen. So sind bei der Betrachtung der avisierten Arbeit ebenso Einflüsse der Betriebswirtschaftslehre, der Informations- und Kommunikationstechno- logie, soziologischer sowie psychologischer Aspekte und nicht zuletzt linguistischer Grundlagen von Interesse für das Resultat bzw. Einflussdimensionen des Ergebnisses.:GRUNDLEGUNG I Problemkontext II Motivation zu dieser Arbeit III Ziel der Arbeit IV Forschungsdesign V Gang der Arbeit TEIL A: VERSTEHEN, LERNEN, WISSEN 1 Wissen und dessen Konstitution 1.1 Gegenstand des Wissens 1.2 Organisationale Epistemologie von Wissen 1.3 Einige Daten und Informationen zum Wissen 1.4 Die Natur des Wissens 1.5 Systematisierung der Arten von Wissen 1.6 Implikationen für diese Arbeit 2 Kommunikation & Verstehen 2.1 Partnerorientiertes Handeln 2.2 Kommunikation, Sprache und Textproduktion 2.3 Wahrnehmen, Verstehen & Rezipieren 2.4 Symbolische Interaktion 2.5 Barrieren der symbolischen Interaktion 2.6 Konsolidierung 3 Wissen & Lernen 3.1 Lernen in Schleifen 3.2 Theorien des individuelles Lernen 3.3 Kollektives Lernen 3.4 Die lernende Organisation 3.5 Kontinuum des Lernens 4 Wissenstransfer und die lernende Organisation 4.1 Strategien des Wissensmanagements 4.2 Wissens(re-)konstruktion 4.3 Erfolgsfaktoren für Wissenstransfer 4.4 Zyklische Wissensentstehung 4.5 Organisationale Wissensschaffung TEIL B: KOLLEKTIVE INFORMATIONSARBEIT & ORGANISATIONALES LERNEN 5 Informationsarbeit & Wissensschaffung 5.1 Vom Suchen und Finden von Informationen 5.2 Information Literacy & Recherchekompetenz 5.3 Informationsarbeit 5.4 Recherchestrategien 5.5 Phasen der internetbasierten Recherche 6 Organisation kollektiver Informationsarbeit 6.1 Intensität von Zusammenarbeit 6.2 Formale & informelle Arbeitsgruppen 6.3 Informationsarbeit im Team 6.4 Die Aufgabe als konstituierendes Merkmal 6.5 Individuelle Dispositionen 6.6 Gruppendynamik im Projektteam 6.7 Externe Unterstützung des Teams 6.8 Soziotechnischer Rahmen 6.9 Kollektive, internetbasierte Informationsarbeit 7 Die suchende Hypertextorganisation 7.1 Die organisationale Wissensbasis 7.2 Soziales Kapital & soziale Netzwerke 7.3 Wissen aus kollektiver Informationsarbeit 7.4 Kollektive Informationsarbeit und die lernende Organisation 7.5 Die lernende Organisation als Community of Communitys TEIL C: AUTOMATISIERTE KONTEXTMODELLIERUNG KOLLEKTIVER INFORMATIONSARBEIT 8 Konzeptuelle & technische Prämissen 8.1 Kernfunktionen der kollektiven Rechercheumgebung 8.2 Multidimensionalität vs. Multiperspektivität 8.3 Das Kontinuum semantischer Strukturmodelle 8.4 Konzepte der technischen Realisierung 9 Kontextschaffung bei kollektiver Informationsarbeit 9.1 Rechercheprozess und formale Objekte 9.2 Kollektive Terminologiearbeit und formale Rechercheobjekte 9.3 Kollektive Schaffung informationeller Mehrwerte 9.4 Explikation von Wissensträgern 10 Konzeption des soziotechnischen Informationssystems 10.1 Aufbau und Grundlegung des Systems 10.2 Grammatisches Metamodell – das Template der Templates 10.3 Templates der Nutzerkontexte und Gültigkeiten 10.4 Konzeptuelle Templates des Rechercheprozesses 10.5 Konzeptuelle Templates der kollektiven Informationsarbeit 10.6 Das Object Repository und die realen Objekte 10.7 Anwendung, Vernetzung & Synopsis des Systems 11 Wissensrepräsentation und kontextbasierte Wiederverwendung 11.1 Grundlagen der Wissensrepräsentation 11.2 Barrieren der Kognition expliziter Wissensrepräsentationen 11.3 Repräsentationsformen bei kollektiver Informationsarbeit 11.4 Dissemination & Zusammenarbeit: Nutzung der Rechercheumgebung 11.5 Six Pieces kollektiver Informationsarbeit für die lernende Organisation SYNOPSIS 12 Erkenntnisse der Arbeit und kritische Würdigung 12.1 Grundlegende Erkenntnisse der theoriegeleiteten Exploration 12.2 Hypothesenbildung: Synopsis der Erfahrungen 12.3 Hypothesenprüfung: Machbarkeit des Systems

(in English) The subject of the Diploma thesis is the process of sharing of economic information and knowledge suitable for corporate governance. A special attention is focused on an effective utilization and development of information and communication technologies (ICT). At the beginning, information society and basic terms are defined, such as information economy, economics of information, information management and knowledge management. Furthermore, the strategic importance of enterprise information systems and other solutions for improving business information processes are described in general. The final chapters deal with Competitive Intelligence (CI) and Business Intelligence (BI) and contain the overview and analyses of the most important information products of economic information from commercial and non-commercial sectors [Author's abstract].

In the presented dissertation, there is, based on a given topic “Internet tools for lifelong learning in the IT sector”, expressed an appropriate coverage of created partial research tasks with an active solution of them at the Faculty of Business and Management, Brno University of Technology for branch Company Management and Economics, and that in accordance with the defined areas of research and publication activities. For systemic approach to the whole dissertation, it was defined area of theoretical perspectives and expressed methodology for addressing systemic models of education on Internet means and for that practical research work was chosen adaptable (“learning”) Company 1 as a reference (for solving individual practical research tasks) and a methodology of modern approach to modelling was created (“for modern management and information communication between systems”) and also modelling of a required adaptable (“directly also improving with modelling”) Internet (and intranet) environment of companies and its use in the process of lifelong learning of knowledge workers (“teachers”) of Company 1 and possible additional (“knowledge workers and users”) Company 2, 3, ..., m i.e. “businesses as other customers” and all that to actively create an adaptable and new space with the possible use of an evolutionary (“new and also modern and intelligent and self-learning”) means of information and communication technologies (ICT) both from the world and from the Czech Republic and especially new (and evolutionary perspective) Internet tools and resources for business management, lifelong learning in IT sector and necessary monitoring of demanded company's competitiveness operating in dynamically developing IT. By systemically solved modelling on computers, there is expressed an issue of appropriately selected internet tools in a competitive environment of companies, and also there is addressed by computer modelling a new approach to modelling leading to an anticipated modernization of modern electronic lifelong learning forms built on tools and resources of the Internet. The work also includes a creation of models on PCs allowing comparison of expected changes in competitiveness of companies reacting to the benefits of created methodology and that leads to an anticipated improvement in the quality of management and economics of companies in terms of relative improvement of their competitiveness. Solving of these modern issues is conducted based on an appropriate analysis of selected enterprises, corresponding to the solved topic and solved objectives of the work and also obtained information from publications in world information sources on the Internet and from virtual libraries of the world, publishing, seminars, proceedings and conferences. The results of system analytical approach to solving tasks and activities of the research work have been regularly published in articles of professional and peer-reviewed journals and also in conference contributions. In these research results, there is systematically expressed a new essence of solving interface of social and IT technical system with selected resources of internet tools for lifelong learning companies working in IT sector as a possible contribution into modern creation of self-learning organizations.

Malaysia is a transition economic country that aims to be a developed country by 2020. In realising this mission (Vision 2020), the cluster concept has been an interest and adopted by the central authorities. There are few years ahead to reach the targeted year and it interest of this study to investigate the relevant development on its own engineered cluster of the Multimedia Super Corridor (MSC) that was put forward on the success of Silicon Valley in the US. This thesis focuses on the development of the MSC cluster in the Malaysia context. It examines and measures the state of the cluster, the role played by its core actors (from Triple Helix perspective) and their relationship in the MSC. The role of collaboration has been used to measure the relationship among actors with the key determinants of cluster formation. A mixed data collection method was used to answer the research question and objectives involved. A conceptual model for analysing the MSC cluster is proposed, bringing together insights from the literature on clusters, role of actors, collaborative relationship and the complex systems of innovation approach. This conceptual model uncover the weaknesses of social dimension (social infrastructure) in Porter’s diamond model and the general approach of Triple Helix model in the cluster development. The cluster lifecycle model is used to add the depth to the analysis on the condition of cluster development.

Industrial revolutions bring dynamic change to industry through major technological advances (Freeman & Louca, 2002). People and companies must take advantage of industrial revolutions in order to reap its benefits (Bruland & Smith, 2013). Currently, the 4th industrial revolution, industry is transforming advanced manufacturing and engineering capabilities through digital transformation. Company X’s production system was investigated in the research. Detailed evaluation the production process revealed bottlenecks and inefficiency (Melton, 2005). Using the Digital Twin and Discrete Event Factory Simulation, the researcher gathered factory and production input data to simulate the process and provide a system level, holistic view of Company X’s production system to show how factory simulation enables process improvement. The National Academy of Engineering supports Discrete Event Factory Simulation as advancing Personalized Learning through its ability to meet the unique problem solving needs of engineering and manufacturing process through advanced simulation technology (National Academy of Engineering, 2018). The directed project applied two process optimization experiments to the production system through the simulation tool, 3DExperience wiht the DELMIA application from Dassualt Systemes (Dassault, 2018). The experiment resulted in a 10% improvement in production time and a 10% reduction in labor costs due to the optimization

Music is perceived and described very subjectively by every individual. Nowadays, people often get lost in their steadily growing, multi-placed, digital music collection. Existing music player and management applications get in trouble when dealing with poor metadata that is predominant in personal music collections. There are several music information services available that assist users by providing tools for precisely organising their music collection, or for presenting them new insights into their own music library and listening habits. However, it is still not the case that music consumers can seamlessly interact with all these auxiliary services directly from the place where they access their music individually. To profit from the manifold music and music-related knowledge that is or can be available via various information services, this information has to be gathered up, semantically federated, and integrated into a uniform knowledge base that can personalised represent this data in an appropriate visualisation to the users. This personalised semantic aggregation of music metadata from several sources is the gist of this thesis. The outlined solution particularly concentrates on users’ needs regarding music collection management which can strongly alternate between single human beings. The author’s proposal, the personal music knowledge base (PMKB), consists of a client-server architecture with uniform communication endpoints and an ontological knowledge representation model format that is able to represent the versatile information of its use cases. The PMKB concept is appropriate to cover the complete information flow life cycle, including the processes of user account initialisation, information service choice, individual information extraction, and proactive update notification. The PMKB implementation makes use of SemanticWeb technologies. Particularly the knowledge representation part of the PMKB vision is explained in this work. Several new Semantic Web ontologies are defined or existing ones are massively modified to meet the requirements of a personalised semantic federation of music and music-related data for managing personal music collections. The outcome is, amongst others, • a new vocabulary for describing the play back domain, • another one for representing information service categorisations and quality ratings, and • one that unites the beneficial parts of the existing advanced user modelling ontologies. The introduced vocabularies can be perfectly utilised in conjunction with the existing Music Ontology framework. Some RDFizers that also make use of the outlined ontologies in their mapping definitions, illustrate the fitness in practise of these specifications. A social evaluation method is applied to carry out an examination dealing with the reutilisation, application and feedback of the vocabularies that are explained in this work. This analysis shows that it is a good practise to properly publish Semantic Web ontologies with the help of some Linked Data principles and further basic SEO techniques to easily reach the searching audience, to avoid duplicates of such KR specifications, and, last but not least, to directly establish a \"shared understanding\". Due to their project-independence, the proposed vocabularies can be deployed in every knowledge representation model that needs their knowledge representation capacities. This thesis added its value to make the vision of a personal music knowledge base come true.:1 Introduction and Background 11 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.2 Personal Music Collection Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2 Music Information Management 17 2.1 Knowledge Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.1 Knowledge Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.1.1.1 Knowledge Representation Models . . . . . . . . . . . . . . . . . 18 2.1.1.2 Semantic Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.1.1.3 Ontologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.1.1.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.1.2 Knowledge Management Systems . . . . . . . . . . . . . . . . . . . . . . . 19 2.1.2.1 Information Services . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.1.2.2 Ontology-based Distributed Knowledge Management Systems . . 20 2.1.2.3 Knowledge Management System Design Guideline . . . . . . . . 21 2.1.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2 Semantic Web Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2.1 The Evolution of the World Wide Web . . . . . . . . . . . . . . . . . . . . . 22 Personal Music Knowledge Base Contents 2.2.1.1 The Hypertext Web . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.2.1.2 The Normative Principles of Web Architecture . . . . . . . . . . . 23 2.2.1.3 The Semantic Web . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.2.2 Common Semantic Web Knowledge Representation Languages . . . . . . 25 2.2.3 Resource Description Levels and their Relations . . . . . . . . . . . . . . . 26 2.2.4 Semantic Web Knowledge Representation Models . . . . . . . . . . . . . . 29 2.2.4.1 Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.2.4.2 Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.2.4.3 Context Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.2.4.4 Storing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.2.4.5 Providing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.2.4.6 Consuming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.3 Music Content and Context Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.3.1 Categories of Musical Characteristics . . . . . . . . . . . . . . . . . . . . . 37 2.3.2 Music Metadata Formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 2.3.3 Music Metadata Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 2.3.3.1 Audio Signal Carrier Indexing Services . . . . . . . . . . . . . . . . 41 2.3.3.2 Music Recommendation and Discovery Services . . . . . . . . . . 42 2.3.3.3 Music Content and Context Analysis Services . . . . . . . . . . . 43 2.3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.4 Personalisation and Environmental Context . . . . . . . . . . . . . . . . . . . . . . 44 2.4.1 User Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 2.4.2 Context Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 2.4.3 Stereotype Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3 The Personal Music Knowledge Base 48 3.1 Foundations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.1.1 Knowledge Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.1.2 Knowledge Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.2 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.3 Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.3.1 User Account Initialisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.3.2 Individual Information Extraction . . . . . . . . . . . . . . . . . . . . . . . . 53 3.3.3 Information Service Choice . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.3.4 Proactive Update Notification . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.3.5 Information Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.3.6 Personal Associations and Context . . . . . . . . . . . . . . . . . . . . . . . 56 3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4 A Personal Music Knowledge Base 57 4.1 Knowledge Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.1.1 The Info Service Ontology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.1.2 The Play Back Ontology and related Ontologies . . . . . . . . . . . . . . . . 61 4.1.2.1 The Ordered List Ontology . . . . . . . . . . . . . . . . . . . . . . 61 4.1.2.2 The Counter Ontology . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.1.2.3 The Association Ontology . . . . . . . . . . . . . . . . . . . . . . . 64 4.1.2.4 The Play Back Ontology . . . . . . . . . . . . . . . . . . . . . . . . 65 4.1.3 The Recommendation Ontology . . . . . . . . . . . . . . . . . . . . . . . . 69 4.1.4 The Cognitive Characteristics Ontology and related Vocabularies . . . . . . 72 4.1.4.1 The Weighting Ontology . . . . . . . . . . . . . . . . . . . . . . . 72 4.1.4.2 The Cognitive Characteristics Ontology . . . . . . . . . . . . . . . 73 4.1.4.3 The Property Reification Vocabulary . . . . . . . . . . . . . . . . . 78 4.1.5 The Media Types Taxonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 4.1.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 4.2 Knowledge Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 4.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 5 Personal Music Knowledge Base in Practice 87 5.1 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.1.1 AudioScrobbler RDF Service . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.1.2 PMKB ID3 Tag Extractor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 5.2 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5.2.1 Reutilisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5.2.2 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.2.3 Reviews and Mentions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.2.4 Indexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 6 Conclusion and Future Work 93 6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94