The wide spread use of ontologies in many scientific areas creates a wealth of ontologyannotated data and necessitates the development of ontology-based data mining algorithms. We have developed generalization and mining algorithms for discovering cross-ontology relationships via ontology-based data mining. We present new interestingness measures to evaluate the discovered cross-ontology relationships. The methods presented in this dissertation employ generalization as an ontology traversal technique for the discovery of interesting and informative relationships at multiple levels of abstraction between concepts from different ontologies. The generalization algorithms combine ontological annotations with the structure and semantics of the ontologies themselves to discover interesting crossontology relationships. The first algorithm uses the depth of ontological concepts as a guide for generalization. The ontology annotations are translated to higher levels of abstraction one level at a time accompanied by incremental association rule mining. The second algorithm conducts a generalization of ontology terms to all their ancestors via transitive ontology relations and then mines cross-ontology multi-level association rules from the generalized transactions. Our interestingness measures use implicit knowledge conveyed by the relation semantics of the ontologies to capture the usefulness of cross-ontology relationships. We describe the use of information theoretic metrics to capture the interestingness of cross-ontology relationships and the specificity of ontology terms with respect to an annotation dataset. Our generalization and data mining agorithms are applied to the Gene Ontology and the postnatal Mouse Anatomy Ontology. The results presented in this work demonstrate that our generalization algorithms and interestingness measures discover more interesting and better quality relationships than approaches that do not use generalization. Our algorithms can be used by researchers and ontology developers to discover inter-ontology connections. Additionally, the cross-ontology relationships discovered using our algorithms can be used by researchers to understand different aspects of entities that interest them.

No description available.

Cette thèse se situe dans le champ de la gestion des connaissances à l’aide de modèles ontologiques. Pour représenter les connaissances de domaine, nous avons conçu une ontologie hybride à deux niveaux : au niveau local, chaque groupe d’experts (du même métier) a construit sa propre ontologie, au niveau global une ontologie consensuelle regroupant les connaissances partagées est créée de façon automatique. De plus, des liens sémantiques entre les éléments de différentes ontologies locales peuvent être ajoutés.Nous avons construit un système d’aide pour guider les experts dans le processus de création de liens sémantiques ou mises en correspondance. Ses particularités sont de proposer des mesures de similarité en fonction des caractéristiques des ontologies à aligner, de réutiliser des résultats déjà calculés et de vérifier la cohérence des mises en correspondances créées.Par ailleurs, les ontologies locales peuvent être mises à jour. Cela implique des changements au niveau de l’ontologie globale ainsi que des mises en correspondances créées. De ce fait, nous avons développé une approche, adaptée à notre domaine pour gérer l’évolution de l’ontologie hybride. En particulier, nous avons utilisé la notion de versions d’ontologies afin de garder trace de toutes les modifications apportées au niveau des ontologies et de pouvoir revenir à tout moment à une version précédente.Nous avons appliqué notre travail de recherche à la géotechnique qui est un domaine complexe impliquant des experts de différents métiers. Une plateforme logicielle est en cours de réalisation et permettra de tester la faisabilité de nos travaux. / This thesis concerns the scope of knowledge management using ontological models.To represent domain knowledge, we design a hybrid ontology on two levels: In a local level, each experts’ group has designed its own ontology. In a global level, a consensual ontology containing all the shared knowledge is automatically created.We design a computer-aided system to help experts in the process of mapping creation. It allows experts to choice similarity measures relatively to the ontology characteristics, to reuse the calculated similarities and to verify the consistency of the created mappings.In addition, local ontologies can be updated. This involves modifications in the global ontology and on the created mappings. A relevant approach of our domain was developed.In particular, ontology versioning is used in order to keep a record of all the occurred modifications in the ontologies; it allows to return at any time a previous version of the hybrid ontology.The exploited domain is geotechnics which gathers various business experts. A prototype is in progress and currently does not still captures ontology evolution.

The abundance of data at our disposal empowers data-driven applications and decision making. The knowledge captured in the data, however, has not been utilized to full potential, as it is only accessible to human interpretation and data are distributed in heterogeneous repositories. Ontologies are a key technology unlocking the knowledge in the data by providing means to model the world around us and infer knowledge implicitly captured in the data. As data are hosted by independent organizations we often need to use several ontologies and discover the relationships between them in order to support data and knowledge transfer. Broadly speaking, while ontologies provide formal representations and thus the basis, ontology alignment supplies integration techniques and thus the means to turn the data kept in distributed, heterogeneous repositories into valuable knowledge. While many automatic approaches for creating alignments have already been developed, user input is still required for obtaining the highest-quality alignments. This thesis focuses on supporting users during the cognitively intensive alignment process and makes several contributions. We have identified front- and back-end system features that foster user involvement during the alignment process and have investigated their support in existing systems by user interface evaluations and literature studies. We have further narrowed down our investigation to features in connection to the, arguably, most cognitively demanding task from the users’ perspective—manual validation—and have also considered the level of user expertise by assessing the impact of user errors on alignments’ quality. As developing and aligning ontologies is an error-prone task, we have focused on the benefits of the integration of ontology alignment and debugging. We have enabled interactive comparative exploration and evaluation of multiple alignments at different levels of detail by developing a dedicated visual environment—Alignment Cubes—which allows for alignments’ evaluation even in the absence of reference alignments. Inspired by the latest technological advances we have investigated and identified three promising directions for the application of large, high-resolution displays in the field: improving the navigation in the ontologies and their alignments, supporting reasoning and collaboration between users.

No description available.

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

Made available in DSpace on 2015-04-11T14:03:11Z (GMT). No. of bitstreams: 1 DISSERTACAO DANIELLA.pdf: 2040757 bytes, checksum: 21b1bef3ce0330932763dc1d246abb39 (MD5) Previous issue date: 2008-02-22 / Fundação de Amparo à Pesquisa do Estado do Amazonas / This work encompasses elements of a study of knowlwedge representation founded on ontologies that have Linux testing as target domain. The study aims at demonstrating that once knowledge is formalised, it is possible to reuse it, to perform inference, to process it throuch computers, and, what is more, it becames amenable to being communicated between people and software. Towards that, three ontlogies have been developed: OSOnto (Operating System Ontology) which represents concepts of the operating system domain, SwTO (Software Test Ontology) which deals with the software testing domain, and SwTOI (SwTO Integrated) which represents concepts of both the above domains in an integrated way. For implementing the ontologies, OWL DL as ontology specification language, Prot´eg´e as ontology edition environment and Racer as the main reasoner, have been used. A quantitative and qualitative evaluation of the SwTOI ontology has been performed. / Este trabalho reúne elementos de um estudo sobre representacão do conhecimento fundamentado em ontologias tendo como domínio alvo o teste do Linux. O estudo visa demonstrar que uma vez que o conhecimento é formalizado, é possível reusá-lo, realizar inferência, processá-lo computacionalmente, como também tornase passível de comunicação entre pessoas e software. Para tal, foram desenvolvidas três ontologias: a OSOnto (Operating System Ontology) que representa conceitos do domínio de Sistema Operacional, a SwTO (Software Test Ontology) que trata do dom´ıniodetestedesoftware, e a SwTOI (SwTOIntegrated) que representa conceitos destes dois domínios integrados. Para a implementação das ontologias foi utilizada OWL DL como linguagem de especificação, o Protégé como ambiente de edição e o Racer como principal raciocinador. Uma avaliação quantitativa e qualitativa foi realizada da ontologia SwTOI.

Made available in DSpace on 2016-08-17T14:53:28Z (GMT). No. of bitstreams: 1 Suzane Carvalho dos Santos.pdf: 4549168 bytes, checksum: 85d08a343bc93d5bf241da9f6f02f5b4 (MD5) Previous issue date: 2014-08-18 / Ontologies are knowledge representation structures capable of expressing a set of entities of a domain, their relationships and axioms that are being used by modern knowledge based systems (KBS) in the decision making process. However, manual construction of ontology is expensive and subject to errors, thus a viable alternative is the automation of this process. Several techniques and tools have been developed to learn the different components of an ontology from textual sources, named concepts, hierarchies, instances, relationships, properties and axioms. However, these elements are generally acquired in a isolated manner. Due to the lack of approaches to acquire all the elements of an ontology jointly, there is a need to develop a process to make the reuse and the learning of each of the elements of an ontology in a synergistic manner. To attend this need, this work presents Apponto-Pro, an incremental learning process for populating application ontologies from textual information sources that is capable of generating a complete ontology through the integration of different techniques to generate isolated elements of an ontology. The process was evaluated through a case study that consisted in the automatic construction of Family_Law, an application ontology in the field of family law developed with Apponto-ProTool, a software tool to support Apponto-Pro that integrates the approaches that compound the whole process. This evaluation aimed to determine the effectiveness of the ontology constructed with Apponto-ProTool against an ontology manually built by a domain specialist and used as reference ontology. For this reason, the "precision"was calculated for the elements of the ontology automatically generated using the reference ontology. As a result it was found that in some cases the ontology developed with Apponto-ProTool tends to present more suitable results. / As ontologias são estruturas de representação de conhecimento capazes de expressar um conjunto de entidades de um dado domínio, seus relacionamentos e axiomas, sendo utilizadas pelos modernos Sistemas Baseados em Conhecimento (SBC) no processo de tomada de decisões. No entanto, a construção manual de ontologias é cara e sujeita a erros, sendo uma alternativa viável a sua construção de forma automática. Diversas técnicas e ferramentas têm sido desenvolvidas para aprender os diferentes componentes de uma ontologia a partir de fontes textuais, quais sejam conceitos, hierarquias, instâncias, relacionamentos, propriedades e axiomas. Entretanto estes elementos são, em regra, adquidiros de forma isolada. Devido à carência de abordagens que adquirem todos os elementos de uma ontologia de forma conjunta, surgiu a necessidade de desenvolver um processo que faça o reúso e a aprendizagem de cada um dos elementos de uma ontologia de forma completa. Atendendo a esta necessidade, este trabalho apresenta o Apponto-Pro, um processo incremental para o aprendizado e povoamento de ontologias de aplicação a partir de fontes de informação textuais capaz de gerar uma ontologia completa através da integração de diferentes técnicas que geram elementos da ontologia de forma isolada. O processo foi avalizado através de um estudo de caso que consistiu na construção automática da Family_Law, uma ontologia de aplicação no domínio do Direito da Família construída através da aplicação da ferramenta de software Apponto-ProTool, desenvolvida para dar suporte ao processo Apponto-Pro que integrou as ferramentas correspondentes as abordagens contidas no processo. Esta avaliação teve como objetivo verificar a efetividade da ontologia construída pela Apponto-ProTool em relação a uma ontologia construída manualmente por um especialista do domínio e utilizada como ontologia de referência. Para isso foi calculado o valor da medida "precision" para os elementos da ontologia construída utilizando a ontologia de referência. Como resultado verificou-se formalmente que em alguns casos a ontologia desenvolvida pela Apponto-ProTool tende a apresentar resultados mais adequados.

Dans cette thèse, nous présentons des approches d’adaptation d’un processus d’alignement aux caractéristiques des ontologies alignées, qu'il s'agisse de caractéristiques quantitatives telles que leur volume ou de caractéristiques particulières liées par exemple à la façon dont les labels des concepts sont construits. Concernant les caractéristiques quantitatives, nous proposons deux méthodes de partitionnement d'ontologies qui permettent l’alignement des ontologies très volumineuses. Ces deux méthodes génèrent, en entrée du processus d'alignement, des sous ensembles de taille raisonnable des deux ontologies à aligner en prenant en compte dès le départ l'objectif d'alignement dans le processus de partitionnement.Concernant les caractéristiques particulières des ontologies alignées, nous présentons l’environnement TaxoMap Framework qui permet la spécification de traitements de raffinement à partir de primitives prédéfinies. Nous proposons un langage de patrons MPL (the Mapping Pattern Language) que nous utilisons pour spécifier les traitements de raffinement.En plus des approches d’adaptation aux caractéristiques des ontologies alignées, nous présentons des approches de réutilisation des résultats d'alignement pour l'ingénierie ontologique. Nous nous focalisons plus particulièrement sur l'utilisation de l'alignement pour l'enrichissement d'ontologies. Nous étudions l'apport des techniques d'alignement pour l'enrichissement et l'impact des caractéristiques de la ressource externe utilisée comme source d'enrichissement. Enfin, nous présentons la façon dont l'environnement TaxoMap Framework a été implémenté et les expérimentations réalisées : des tests sur le module d'alignement TaxoMap, sur l'approche de raffinement de mappings, sur les méthodes de partitionnement d'ontologies de très grande taille et sur l'approche d'enrichissement d'ontologies. / In this thesis, we present approaches for adapting an alignment process to the characteristics of the aligned ontologies, whether in respect of the quantitative characteristics such as their volume or the particular characteristics related for example to the way in which the labels of the concepts are built.Concerning the quantitative characteristics, we propose two ontology partitioning methods that make the alignment of very large ontologies possible. Both methods generate in the input of the alignment process, subsets of reasonable size of the two ontologies to be aligned by taking into account, as soon as possible, the alignment objective in the partitioning process.Concerning the particular characteristics of the aligned ontologies, we present the TaxoMap Framework environment that allows the specification of refinement treatments from predefined primitives. We propose a pattern language MPL (the Mapping Pattern Language) that we use to specify the refinement treatments.In addition to the adaptation approaches to the characteristics of the aligned ontologies, we present approaches for re-using the alignment results for the ontological engineering. We focus specifically on the use of the alignment for the ontology enrichment. We study the contribution of the alignment techniques for the enrichment and the impact of the characteristics of the external resource used as a source of enrichment.Finally, we present how the TaxoMap Framework environment was implemented and the performed experiments: tests on the TaxoMap alignment module, on the mappings refinement approach, on the partitioning methods of very large ontologies and on the ontology enrichment approach.