人口老化帶動居家照護需求興起，使許多的健康照護服務因應而盛，居家照護者與照護服務產業分別面臨，從市面上存在種類眾多而數量龐大的服務資源中，獲取與提供適切服務之困難狀況。要能夠幫助居家照護找到需要的服務，必須了解其需求與重視之價值面向，制定包含能媒合供給面與合理評估服務品質的機制與流程，以有效的舉薦適合的照護服務供做服務選擇之決策參考。 　　本研究以服務本體論(Service Ontology)之服務價值(Service Value)作為建置顧客價值構面之結構。使用者需求內容採用巴氏量表(Barthel Index)進行生理功能的量測；服務品質與使用者願意付出之成本則以台灣服務滿意度模型(Taiwan Customer Satisfaction Index, TCSI)做為內容。本研究並以本體論依顧客價值構面，制定使用需求與照護服務提供之描述媒合機制，透過取得居家照護者生理功能須支援之需求，以及同區隔之使用者對服務的滿意度評價，媒合服務經營者對服務內容的描述，即可找出適合的服務。 　　最後以服務導向架構(Service Oriented Architecture, SOA)結合顧客價值之供需媒合機制，並建置雛型系統以驗證概念。 / As the growing home-caring market, demanders have faced problems that it is difficult to choose the right caring services for themselves from large numbers of services base. To help them find suitable services, we need to know what they need and how they evaluate services. In that case, we must develop a mechanism to match supply and demand to effectively recommend services to users. The research refers Service Value of Service Ontology as the structure of Customer Value and uses Barthel Index and Taiwan Customer Satisfaction Index (TCSI) to be the content of Customer Value. We know the situation of users, body function through Barthel Index and their satisfaction to the services through TCSI. The research establishs the supply and demand matching mechanism based on Customer Value. Requiring customers, needs from body function evaluation and customers, satisfaction via TCSI, the mechanism would match the needs and the description of services offering. In the end, it would present the suitable services for customers. The research builds up a Service Oriented Architecture (SOA) prototype to proof the concept of supply and demand matching mechanism.

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.

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