No description available.

No description available.

Ontological engineering utilizes ontologies for various purposes across the spectrum. Just as onotlogies are being used in healthcare, researchers are also developing ontologies for unmanned aerial systems (UAS). A specific ontology domain has been created laying out various aspects of counter unmanned aerial systems (cUAS), known as the “UAS Integration Safety and Security Technology Ontology” (ISSTO). Counter unmanned aerial systems are a collection of technologies and systems that detect, track, identify, and mitigate unmanned aerial systems. ISSTO represents a compilation of classes that encompass UAS and cUAS components. The classes within ISSTO include aircraft types, weather, sensors, UAS air traffic management, and the National Airspace System (NAS). To effectively utilize the ISSTO ontology, UAS pilots and or policymakers require an innovative parsing method to extract the specific information they seek. This methodology makes use of ontology querying and natural language processing methods. By using user input, SPARQL queries are built dynamically by the parsing method. It utilizes synonym expansion and a scoring mechanism that prioritizes results by relevance using label matches, class matches, and annotation keyword matches.

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.

Ontology Matching is one of the hottest topic within the Semantic Web of recent years. There is still ample of space for improvement in terms of performance. Furthermore, current ontology matchers mostly concentrate on simple entity to entity matching. However, matching of whole structures could bring some additional complex relationships. These structures of ontologies can be captured as ontology patterns. The main theme of this thesis is an examination of pattern-based ontology matching enhanced with ontology transformation and pattern-based ontology alignment evaluation. The former is examined due to its potential benefits regarding complex matching and matching as such. The latter is examined because complex hypotheses could be beneficial feedback as complement to traditional evaluation methods. These two tasks are related to four different topics: ontology patterns, ontology transformation, ontology alignment evaluation and ontology matching. With regard to those four topics, this work covers the following aspects: * Examination of different aspects of ontology patterns. Particularly, description of relevant ontology patterns for ontology transformation and for ontology matching (such as naming, matching and transformation patterns). * Description of a pattern-based method for ontology transformation. * Introduction of new methods for an alignment evaluation; including using patterns as a complex structures for more detailed analysis. * Experiments and demonstrations of new concepts introduced in this thesis. The thesis first introduces naming pattern and matching pattern classification on which ontology transformation framework is based. Naming patterns are useful for detection of ontology patterns and for generation of new names for entities. Matching patterns are basis for transformation patterns in terms of sharing some building blocks. In comparison with matching patterns, transformation patterns have transformation links that represent way how parts of ontology patterns are transformed. Besides several evaluations and implementations, the thesis provides a demonstration of getting complex matching due to ontology transformation process. Ontology transformation framework has been implemented in Java environment where all generic patterns are represented as corresponding Java objects. Three main implemented services are made generally available as RESTful services: ontology pattern detection, transformation instruction generation and ontology transformation.

Thesis (M.C.S.)--Miami University, Dept. of Computer Science and Systems Analysis, 2007. / Title from first page of PDF document. Includes bibliographical references (p. 86-88).

Ce travail est consacré à l’étude du projet d’ontologie formelle de la fin du Moyen-Âge à l’époque contemporaine. Issue des recherches du jeune Husserl, l’ontologie formelle est théorie du quelque chose ou de l'objet en général énonçant de manière ontologiquement neutre des lois analytiques, ancrées dans certaines catégories ontologico-formelles, orthogonales à toute ontologie régionale et ne se réduisant pas à celles de la logique formelle, mais leur étant néanmoins corrélées. Une première partie de notre étude visait à montrer l’émergence du réseau conceptuel qui a permis l’émergence d’une telle ontologie. Celui-ci relève de plusieurs disciplines : l’ontologie, la logique, les mathématiques et la psychologie. Ainsi, même s’il s’agit d’un projet métaphysique original, il hérite dans une certaine mesure de la tradition ontologique moderne comprise comme tinologie et issue du processus de noétisation de l’objet de la métaphysique initié par le second commencement de la métaphysique à la fin du Moyen Âge, du problème des représentations sans objet dans la tradition philosophique brentanienne dont devait sortir diverses Gegenstandstheorien, du problème des Gestalten dans cette même tradition et de l’émergence d’une nouvelle conception de la formalité dans la mathématique du XIXe siècle. Les deuxième et troisième parties de ce travail sont consacrées à l’étude systématique de la réalisation technique du projet d’ontologie formelle, en particulier au sein de sa reprise analytique à partir de la fin des années 1970, sous la forme d’une méréologie formelle et de ses multiples extensions (méréotopologie, méréologie temporelle et théorie méréologique de la dépendance existentielle), afin de pouvoir résoudre le problème de l’intégrité ontologique des objets.

The rapid growth in the number of documents available to various end users from around the world has led to a greatly increased need for machine understanding of their topics, as well as for automatic grouping of related documents. This constitutes one of the main current challenges in text mining. We introduce in this thesis a novel approach for identifying document topics. In this approach, we try to utilize human background knowledge to help us to automatically find the best matching topic for input documents. There are several applications for this task. For example, it can be used to improve the relevancy of search engine results by categorizing the search results according to their general topic. It can also give users the ability to choose the domain which is most relevant to their needs. It can also be used for an application like a news publisher, where we want to automatically assign each news article to one of the predefined news main topics. In order to achieve this, we need to extract background knowledge in a form appropriate to this task. The thesis contributions can be summarized into two main modules. In the first module, we introduce a new approach to extract background knowledge from a human knowledge source, in the form of a knowledge repository, and store it in a well-structured and organized form, namely an ontology. We define the methodology of identifying ontological concepts, as well as defining the relations between these concepts. We use the ontology to infer the semantic similarity between documents, as well as to identify their topics. We apply our proposed approach using perhaps the best-known of the knowledge repositories, namely Wikipedia. The second module of this dissertation defines the framework for automatic document topic identification (ADTI). We present a new approach that utilizes the knowledge stored in the created ontology to automatically find the best matching topics for input documents, without the need for a training process such as in document classification. We compare ADTI to other text mining tasks by conducting several experiments to compare the performance of ADTI and its competitors, namely document clustering and document classification. Results show that our document topic identification approach outperforms several document clustering techniques. They show also that while ADTI does not require training, it nevertheless shows competitive performance with one of the state-of-the-art methods for document classification.

ABSTRACT The availability of useful information for research strongly depends on well structured relationships between consistently defined concepts (terms) in that domain. This can be achieved through ontologies. Ontologies are models of the knowledge of specific domain such as petroleum geology, in a computer understandable format. Knowledge is a collection of facts. Facts are represented by RDF triples (subject-predicate-object). A domain ontology is therefore a collection of many RDF triples, which represent facts of that domain. The SWEET ontologies are upper or top-level ontologies (foundation ontologies) consisting of thousands of very general concepts. These concepts are obtained from of Earth System science and include other related concepts. The work in this thesis deals with scientific knowledge representation in which the SWEET ontologies are extended to include wider, more specific and specialized concepts used in Petroleum Geology. Thus Petroleum Geology knowledge modeling is presented in this thesis.

The rapid growth in the number of documents available to various end users from around the world has led to a greatly increased need for machine understanding of their topics, as well as for automatic grouping of related documents. This constitutes one of the main current challenges in text mining. We introduce in this thesis a novel approach for identifying document topics. In this approach, we try to utilize human background knowledge to help us to automatically find the best matching topic for input documents. There are several applications for this task. For example, it can be used to improve the relevancy of search engine results by categorizing the search results according to their general topic. It can also give users the ability to choose the domain which is most relevant to their needs. It can also be used for an application like a news publisher, where we want to automatically assign each news article to one of the predefined news main topics. In order to achieve this, we need to extract background knowledge in a form appropriate to this task. The thesis contributions can be summarized into two main modules. In the first module, we introduce a new approach to extract background knowledge from a human knowledge source, in the form of a knowledge repository, and store it in a well-structured and organized form, namely an ontology. We define the methodology of identifying ontological concepts, as well as defining the relations between these concepts. We use the ontology to infer the semantic similarity between documents, as well as to identify their topics. We apply our proposed approach using perhaps the best-known of the knowledge repositories, namely Wikipedia. The second module of this dissertation defines the framework for automatic document topic identification (ADTI). We present a new approach that utilizes the knowledge stored in the created ontology to automatically find the best matching topics for input documents, without the need for a training process such as in document classification. We compare ADTI to other text mining tasks by conducting several experiments to compare the performance of ADTI and its competitors, namely document clustering and document classification. Results show that our document topic identification approach outperforms several document clustering techniques. They show also that while ADTI does not require training, it nevertheless shows competitive performance with one of the state-of-the-art methods for document classification.