El desarrollo de sistemas que faciliten la gestión de conocimiento es un elemento estratégico para las organizaciones en la actualidad. La necesidad de encontrar soluciones para obtener el conocimiento necesario para construir este tipo de sistemas ha sido la motivación primordial para esta tesis doctoral. La solución propuesta se basa en la mejora de los procesos de integración del conocimiento explicito disponible. Con ello, el desarrollo de sistemas para la gestión de conocimiento seria más eficiente. Dicho objetivo se ha logrado a través de las siguientes actividades: Definición y formalización de un entorno para la integración de ontologias Diseño e implementación de una aplicación software para el desarrollo cooperativo de ontologias Validación de la metodologia de construcción de ontologias Evaluación de la calidad de las ontologias obtenidas a través de los procesos de integración Evaluación de la utilidad de los procesos de integración / The development of knowledge management systems is currently an strategical organizational issue. The main aim of this thesis was the need for solutions to acquire the knowledge needed to build them. The solution here proposed is based on the enhancement of the integration processes of explicit knowledge. This would make more efficient the development of knowledge management systems. This objective has been achieved through the following activities: Definition and formalization of a framework for integrating ontologies Design and implementation of a software tool for the cooperative development of ontologies Validation of the ontology building methodology Evaluation of the quality of the ontologies built through integration processes Evaluation of the usefulness of integration processes

Made available in DSpace on 2015-04-11T14:02:35Z (GMT). No. of bitstreams: 1 andrea.pdf: 7259314 bytes, checksum: d0277f774ef42cd05833ff331802fdc5 (MD5) Previous issue date: 2011-06-17 / Convention on Biological Diversity (CBD) recognizes that biodiversity loss must be reduced to promote poverty alleviation and direct benefit of all live on Earth. To achieve that, we must consider robust strategies and action plans based on knowledge and state of art technology. In this context, we have two current scenarios, the Web as a dissemination environment and the Semantic Web to deal with the complexity of domains of high semantic expressiveness as biodiversity. The research is underway in universities and scientific organization aiming to develop Semantic Web as an additional resource associated to formal ontology and the avoidance of knowledge acquisition problems such as expertise dependence, tacit knowledge, experts availability and ideal time importance. Ontology can structure knowledge acquisition process for the purpose of comprehensive, portable machine understanding and knowledge extraction on the semantic web environment. These technologies applied to biodiversity domain can be a valuable resource for our region future strategy planning and its contribution for the planet. / A Convenção sobre Diversidade Biológica (Convention on Biological Diversity - CBD) reconhece que a perda da biodiversidade deve ser reduzida para promover a redução da pobreza e beneficiar diretamente todas as formas de vida na Terra. Para tanto, devem-se considerar estratégias robustas e planos de ação baseados em conhecimento e no estado da arte da tecnologia. Neste contexto, temos dois cenários atuais: a Web como ambiente de disseminação e a Web Semântica para tratar a complexidade de domínios com grande expressividade semântica, como o da biodiversidade. A pesquisa está em andamento em universidades e organizações científicas objetivando o desenvolvimento da Web Semântica como um recurso adicional associado à ontologia formal e à evasão de problemas na aquisição de conhecimento como a dependência do expertise, o conhecimento tácito, a disponibilidade dos peritos e a importância do tempo ideal. Ontologia estrutura o processo de aquisição de conhecimento para o propósito de compreensão, entendimento de máquinas portáteis e de extração de conhecimento do ambiente da Web Semântica. Estas tecnologias aplicadas ao domínio da biodiversidade são um recurso valioso para o planejamento estratégico do futuro da nossa região e sua contribuição para o planeta

Ontology design patterns are used for creating quality modeling solutions for ontologies. The presentation of ontology design patterns is concerned with reusability of ontologies from a user perspective. The purpose of this research is to identify improvement areas in the presentation of content ontology design patterns. The objective is to analyze different content ontology design patterns and provide suggestions for possible changes in current templates and pattern presentation. The ontology design pattern templates were compared with existing templates of other patterns to identify improvement areas. After this, two surveys were conducted with novice users and expert ontology engineers to improve the readability and usability of content ontology design patterns from the user perspective and to discover differences in opinion while using the patterns. Based on the findings of comparison and survey results, we proposed suggestions to improve the current template and presentation of content ontology design patterns.

The World Wide Web contains large amounts of data, and in most cases this data has no explicit structure. The lack of structure makes it difficult for automated agents to understand and use such data. A step towards a more structured World Wide Web is the Semantic Web, which aims at introducing semantics to data on the World Wide Web. One of the key technologies in this endeavour are ontologies, which provide a means for modeling a domain of interest and are used for search and integration of data. In recent years many ontologies have been developed. To be able to use multiple ontologies it is necessary to align them, i.e., find inter-ontology relationships. However, developing and aligning ontologies is not an easy task and it is often the case that ontologies and their alignments are incorrect and incomplete. This can be a problem for semantically-enabled applications. Incorrect and incomplete ontologies and alignments directly influence the quality of the results of such applications, as wrong results can be returned and correct results can be missed. This thesis focuses on the problem of completing ontologies and ontology networks. The contributions of the thesis are threefold. First, we address the issue of completing the is-a structure and alignment in ontologies and ontology networks. We have formalized the problem of completing the is-a structure in ontologies as an abductive reasoning problem and developed algorithms as well as systems for dealing with the problem. With respect to the completion of alignments, we have studied system performance in the Ontology Alignment Evaluation Initiative, a yearly evaluation campaign for ontology alignment systems. We have also addressed the scalability of ontology matching, which is one of the current challenges, by developing an approach for reducing the search space when generating the alignment.Second, high quality completion requires user involvement. As users' time and effort are a limited resource we address the issue of limiting and facilitating user interaction in the completion process. We have conducted a broad study of state-of-the-art ontology alignment systems and identified different issues related to the process. We have also conducted experiments to assess the impact of user errors in the completion process. While the completion of ontologies and ontology networks can be done at any point in the life-cycle of ontologies and ontology networks, some of the issues can be addressed already in the development phase. The third contribution of the thesis addresses this by introducing ontology completion and ontology alignment into an existing ontology development methodology.

The DBpedia project aims at extracting information based on semi-structured data present in Wikipedia articles, interlinking it with other knowledge bases, and publishing this information as RDF freely on the Web. So far, the DBpedia project has succeeded in creating one of the largest knowledge bases on the Data Web, which is used in many applications and research prototypes. However, the manual effort required to produce and publish a new version of the dataset – which was already partially outdated the moment it was released – has been a drawback. Additionally, the maintenance of the DBpedia Ontology, an ontology serving as a structural backbone for the extracted data, made the release cycles even more heavyweight. In the course of this thesis, we make two contributions: Firstly, we develop a wiki-based solution for maintaining the DBpedia Ontology. By allowing anyone to edit, we aim to distribute the maintenance work among the DBpedia community. Secondly, we extend DBpedia with a Live Extraction Framework, which is capable of extracting RDF data from articles that have recently been edited on the English Wikipedia. By making this RDF data automatically public in near realtime, namely via SPARQL and Linked Data, we overcome many of the drawbacks of the former release cycles.

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“In an ill-structured domain you cannot, by definition, have a pre-compiled schema in your mind for every circumstance and context you may find ... you must be able to flexibly select and arrange knowledge sources to most efficaciously pursue the needs of a given situation.” [57] / In order to interact and collaborate effectively, agents, whether human or software, must be able to communicate through common understandings and compatible conceptualisations. Ontological differences that occur either from pre-existing assumptions or as side-effects of the process of specification are a fundamental obstacle that must be overcome before communication can occur. Similarly, the integration of information from heterogeneous sources is an unsolved problem. Efforts have been made to assist integration, through both methods and mechanisms, but automated integration remains an unachieved goal. Communication and information integration are problems of meaning and interaction, or semantic interoperability. This thesis contributes to the study of semantic interoperability by identifying, developing and evaluating three approaches to the integration of information. These approaches have in common that they are lightweight in nature, pragmatic in philosophy and general in application. / The first work presented is an effort to integrate a massive, formal ontology and knowledge-base with semi-structured, informal heterogeneous information sources via a heuristic-driven, adaptable information agent. The goal of the work was to demonstrate a process by which task-specific knowledge can be identified and incorporated into the massive knowledge-base in such a way that it can be generally re-used. The practical outcome of this effort was a framework that illustrates a feasible approach to providing the massive knowledge-base with an ontologically-sound mechanism for automatically generating task-specific information agents to dynamically retrieve information from semi-structured information sources without requiring machine-readable meta-data. / The second work presented is based on reviving a previously published and neglected algorithm for inferring semantic correspondences between fields of tables from heterogeneous information sources. An adapted form of the algorithm is presented and evaluated on relatively simple and consistent data collected from web services in order to verify the original results, and then on poorly-structured and messy data collected from web sites in order to explore the limits of the algorithm. The results are presented via standard measures and are accompanied by detailed discussions on the nature of the data encountered and an analysis of the strengths and weaknesses of the algorithm and the ways in which it complements other approaches that have been proposed. / Acknowledging the cost and difficulty of integrating semantically incompatible software systems and information sources, the third work presented is a proposal and a working prototype for a web site to facilitate the resolving of semantic incompatibilities between software systems prior to deployment, based on the commonly-accepted software engineering principle that the cost of correcting faults increases exponentially as projects progress from phase to phase, with post-deployment corrections being significantly more costly than those performed earlier in a project’s life. The barriers to collaboration in software development are identified and steps taken to overcome them. The system presented draws on the recent collaborative successes of social and collaborative on-line projects such as SourceForge, Del.icio.us, digg and Wikipedia and a variety of techniques for ontology reconciliation to provide an environment in which data definitions can be shared, browsed and compared, with recommendations automatically presented to encourage developers to adopt data definitions compatible with previously developed systems. / In addition to the experimental works presented, this thesis contributes reflections on the origins of semantic incompatibility with a particular focus on interaction between software systems, and between software systems and their users, as well as detailed analysis of the existing body of research into methods and techniques for overcoming these problems.

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.

[ES] Las ontologías son artefactos computacionales con una amplia gama de aplicaciones. Estos artefactos representan el conocimiento con la mayor precisión posible y brindan a los humanos un marco para representar y aclarar el conocimiento. Además, las ontologías se pueden implementar y procesar agregando semántica a los datos que deben intercambiarse entre sistemas. En los sistemas, los datos transportan información y deben seguir los Principios FAIR para cumplir su propósito. Sin embargo, los dominios del conocimiento pueden ser vastos, complejos y sensibles, lo que hace que la interoperabilidad sea un desafío. Además, el diseño y desarrollo de ontologías no es una tarea sencilla, y debe seguir metodologías y estándares, además de cumplir una serie de requisitos. De hecho, las ontologías se han utilizado para producir FAIRness de datos debido a sus características, aplicaciones y competencias semánticas. Con la creciente necesidad de interoperar datos surgió la necesidad de interoperar ontologías para garantizar la correcta transmisión e intercambio de información. Para satisfacer esta demanda de ontologías interoperativas y, al mismo tiempo, conceptualizar dominios amplios y complejos, surgieron las Redes de Ontologías. Además, las ontologías comenzaron a presentar conceptualizaciones a través de la fragmentación del conocimiento de diferentes maneras, dependiendo de requisitos como el alcance de la ontología, su propósito, si es procesable o para uso humano, su contexto, entre otros aspectos formales, haciendo que la Ingeniería Ontológica sea también un dominio complejo. El problema es que en el Proceso de Ingeniería de Ontologías, las personas responsables toman diferentes perspectivas sobre las conceptualizaciones, provocando que las ontologías tengan sesgos a veces más ontológicos y otras más relacionados con el dominio. Estos problemas dan como resultado ontologías que carecen de fundamento o bien implementaciones de ontologías sin un modelo de referencia previo. Proponemos una (meta)ontología basada en la Ontología Fundacional Unicada (UFO, del inglés, Unified Foundational Ontology) y respaldada por estándares de clasificación ontológica reconocidos, guías y principios FAIR para resolver este problema de falta de consenso en las conceptualizaciones. La Ontología para el Análisis Ontológico (O4OA, del inglés, Ontology for Ontological Analysis) considera perspectivas, conocimientos, características y compromisos, que son necesarios para que la ontología y el dominio faciliten el proceso de Análisis Ontológico, incluyendo el análisis de las ontologías que conforman una red de ontologías. Utilizando O4OA, proponemos el Marco para la Caracterización Ontológica (F4OC, del inglés, Framework for Ontology Characterization) para proporcionar pautas y mejores prácticas a los responsables, a la luz de O4OA. F4OC proporciona un entorno estable y homogéneo para facilitar el análisis ontológico, abordando simultáneamente las perspectivas ontológicas y de dominio de los involucrados. Además, aplicamos O4OA y F4OC a varios estudios de casos en el Dominio de Ciberseguridad, el cual es complejo, extremadamente regulado y sensible, y propenso a dañar a personas y organizaciones. El principal objetivo de esta tesis doctoral es proporcionar un entorno sistemático y reproducible para ingenieros en ontologías y expertos en dominios, responsables de garantizar ontologías desarrolladas de acuerdo con los Principios FAIR. Aspiramos a que O4OA y F4OC sean contribuciones valiosas para la comunidad de modelado conceptual, así como resultados adicionales para la comunidad de ciberseguridad a través del análisis ontológico de nuestros estudios de caso. / [CA] Les ontologies són artefactes computacionals amb una àmplia gamma d'aplicacions. Aquests artefactes representen el coneixement amb la major precisió possible i brinden als humans un marc per a representar i aclarir el coneixement. A més, les ontologies es poden implementar i processar agregant semàntica a les dades que han d'intercanviar-se entre sistemes. En els sistemes, les dades transporten informació i han de seguir els Principis FAIR per a complir el seu propòsit. No obstant això, els dominis del coneixement poden ser vastos, complexos i sensibles, la qual cosa fa que la interoperabilitat siga un desafiament. A més, el disseny i desenvolupament d'ontologies no és una tasca senzilla, i ha de seguir metodologies i estàndards, a més de complir una sèrie de requisits. De fet, les ontologies s'han utilitzat per a produir FAIRness de dades a causa de les seues característiques, aplicacions i competències semàntiques. Amb la creixent necessitat de inter operar dades va sorgir la necessitat de inter operar ontologies per a garantir la correcta transmissió i intercanvi d'informació. Per a satisfer aquesta demanda d'ontologies inter operatives i, al mateix temps, conceptualitzar dominis amplis i complexos, van sorgir Xarxes d'Ontologies. A més, les ontologies van començar a presentar conceptualitzacions a través de la fragmentació del coneixement de diferents maneres, depenent de requisits com l'abast de l'ontologia, el seu propòsit, si és procesable o per a ús humà, el seu context i diversos altres aspectes formals, fent que el Enginyeria Ontològica també és un domini complex. El problema és que en Procés d'Enginyeria d'Ontologies, les persones responsables prenen diferents perspectives sobre les conceptualitzacions, provocant que les ontologies tinguen biaixos a vegades més ontològics i altres més relacionats amb el domini. Aquests problemes donen com a resultat ontologies que manquen de fonament i implementacions d'ontologies sense un model de referència previ. Proposem una (meta)ontologia basada en la Ontologia Fundacional Unificada (UFO, de le inglés, Unified Foundational Ontology) i recolzada per coneguts estàndard de classificació ontològica, guies i principis FAIR per a resoldre aquest problema de falta de consens en les conceptualitzacions. La Ontologia per a l'Anàlisi Ontològica (O4OA, de le inglés, Ontology for Ontological Analysis) considera perspectives, coneixements, característiques i compromisos, que són necessaris perquè l'ontologia i el domini faciliten el procés de Anàlisi Ontològica, incloent-hi l'anàlisi de les ontologies que conformen una xarxa d'ontologies. Utilitzant O4OA, proposem el Marco per a la Caracterització Ontològica (F4OC, de le inglés, Framework for Ontology Characterization) per a proporcionar pautes i millors pràctiques als responsables, a la llum d'O4OA. F4OC proporciona un entorn estable i homogeni per a facilitar l'anàlisi ontològica, abordant simultàniament les perspectives ontològiques i de domini dels involucrades. A més, apliquem O4OA i F4OC a diversos estudis de casos en el Domini de Seguretat Cibernètica, que és complex, extremadament regulat i sensible, i propens a danyar a persones i organitzacions. L'objectiu principal d'aquesta tesi és proporcionar un entorn sistemàtic, reproduïble i escalable per a engineers en ontologies i experts in dominis encarregats de garantir les ontologies desenvolupades d'acord amb els Principis FAIR. Aspirem a fer que O4OA i F4OC aportin valuoses contribucions a la comunitat de modelització conceptual, així com resultats addicionals per a la comunitat de ciberseguretat mitjançant l'anàlisi ontològica dels nostres estudis de cas. / [EN] Ontologies are computational artifacts with a wide range of applications. They represent knowledge as accurately as possible and provide humans with a framework for knowledge representation and clarification. Additionally, ontologies can be implemented and processed by adding semantics to data that needs to be exchanged between systems. In systems, data is the carrier of information and needs to comply with the FAIR Principles to fulfill its purpose. However, knowledge domains can be vast, complex, and sensitive, making interoperability challenging. Moreover, ontology design and development are not easy tasks; they must follow methodologies and standards and comply with a set of requirements. Indeed, ontologies have been used to provide data FAIRness due to their characteristics, applications, and semantic competencies. With the growing need to interoperate data came the need to interoperate ontologies to guarantee the correct transmission and exchange of information. To meet the need to interoperate ontologies and at the same time conceptualize complex and vast domains, Ontology Networks emerged. Moreover, ontologies began to carry out conceptualizations, fragmenting knowledge in different ways depending on requirements, such as the ontology scope, purpose, whether it is processable or for human use, its context, and several other formal aspects, making Ontology Engineering also a complex domain. The problem is that in the Ontology Engineering Process, stakeholders take different perspectives of the conceptualizations, and this causes ontologies to have biases that are sometimes more ontological and sometimes more related to the domain. These problems result in ontologies that lack grounding and ontology implementations without a previous reference model. We propose a (meta)ontology grounded over the Unified Foundational Ontology (UFO) and supported by well-known ontological classification standards, guides, and FAIR Principles to address this problem of lack of consensual conceptualization. The Ontology for Ontological Analysis (O4OA) considers ontological-related and domain-related perspectives, knowledge, characteristics, and commitment that are needed to facilitate the process of Ontological Analysis, including the analysis of ontologies composing an ontology network. Using O4OA we propose the Framework for Ontology Characterization (F4OC) to provide guidelines and best practices in the light of O4OA for stakeholders. The F4OC fosters a stable and uniform environment for ontological analysis, integrating stakeholder perspectives. Moreover, we applied O4OA and F4OC to several case studies in the Cybersecurity Domain, which is intricate, highly regulated, and sensitive to causing harm to people and organizations. The main objective of this doctoral thesis is to provide a systematic and reproducible environment for ontology engineers and domain specialists responsible for ensuring ontologies developed according to the FAIR Principles. We aspire that O4OA and F4OC be valuable contributions to the conceptual modeling community as well as the additional outcomes for the cybersecurity community through the ontological analysis in our case studies. / Franco Martins Souza, B. (2024). A Framework for Conceptual Characterization of Ontologies and its Application in the Cybersecurity Domain [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/204584

財務會計資訊系統是企業基礎且必要的資訊系統，提供內、外部使用者有關企業的經營績效資訊，作為內部經營者的管理依據及外部投資者的決策參考。然而由於法令頻繁的變更及企業本身策略需求的改變，會計資訊系統面臨很高的更新、維護成本。 　　本研究提出一個以本體為基礎的資訊系統架構，首先修改W.E. McCarthy於1982年提出的REA 模型，利用本體工程方法建立財務會計本體，描述企業流程及會計處理知識；再利用規則式系統之技術，於財務會計本體外建立系統存取規則及介面，呈現系統樣貌與功能。於法令變更及需求改變時直接修改本體內容，彈性變更系統內的流程、運作規則，達成減少維護成本、增加彈性之目標。 / Accounting Information System(AIS) is a kind of important system in an enterprise. That provides financial information for users to make decisions. However, accounting principle and manage strategy changing frequently that cause highly maintenance cost. 　　This research proposes an ontology-based system structure. First, we amend the REA model and analyze business processes and accounting rules to build an accounting ontology by ontology engineering. Then, we use the rule-based system skill to develop the system interface. Whenever accounting principle be modified, we update the ontology only. This design promotes the flexibility and decreases the cost.