Designing and discovering novel materials is challenging problem in many domains such as fuel additives, composites, pharmaceuticals, and so on. At the core of all this are models that capture how the different domain-specific data, information, and knowledge regarding the structures and properties of the materials are related to one another. This dissertation explores the difficult task of developing an artificial intelligence-based knowledge modeling environment, called Hybrid Ontology-Learning Materials Engineering System (HOLMES) that can assist humans in populating a materials science and engineering ontology through automatic information extraction from journal article abstracts. While what we propose may be adapted for a generic materials engineering application, our focus in this thesis is on the needs of the pharmaceutical industry. We develop the Columbia Ontology for Pharmaceutical Engineering (COPE), which is a modification of the Purdue Ontology for Pharmaceutical Engineering. COPE serves as the basis for HOLMES. The HOLMES framework starts with journal articles that are in the Portable Document Format (PDF) and ends with the assignment of the entries in the journal articles into ontologies. While this might seem to be a simple task of information extraction, to fully extract the information such that the ontology is filled as completely and correctly as possible is not easy when considering a fully developed ontology. In the development of the information extraction tasks, we note that there are new problems that have not arisen in previous information extraction work in the literature. The first is the necessity to extract auxiliary information in the form of concepts such as actions, ideas, problem specifications, properties, etc. The second problem is in the existence of multiple labels for a single token due to the existence of the aforementioned concepts. These two problems are the focus of this dissertation. In this work, the HOLMES framework is presented as a whole, describing our successful progress as well as unsolved problems, which might help future research on this topic. The ontology is then presented to help in the identification of the relevant information that needs to be retrieved. The annotations are next developed to create the data sets necessary for the machine learning algorithms to perform. Then, the current level of information extraction for these concepts is explored and expanded. This is done through the introduction of entity feature sets that are based on previously extracted entities from the entity recognition task. And finally, the new task of handling multiple labels for tagging a single entity is also explored by the use of multiple-label algorithms used primarily in image processing.

Chen, Wenhao. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (p. 63-70). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Ontologies and Folksonomies --- p.1 / Chapter 1.2 --- Motivation --- p.3 / Chapter 1.2.1 --- Semantics in Folksonomies --- p.3 / Chapter 1.2.2 --- Ontologies with basic level concepts --- p.5 / Chapter 1.2.3 --- Context and Context Effect --- p.6 / Chapter 1.3 --- Contributions --- p.6 / Chapter 1.4 --- Structure of the Thesis --- p.8 / Chapter 2 --- Background Study --- p.10 / Chapter 2.1 --- Semantic Web --- p.10 / Chapter 2.2 --- Ontology --- p.12 / Chapter 2.3 --- Folksonomy --- p.14 / Chapter 2.4 --- Cognitive Psychology --- p.17 / Chapter 2.4.1 --- Category (Concept) --- p.17 / Chapter 2.4.2 --- Basic Level Categories (Concepts) --- p.17 / Chapter 2.4.3 --- Context and Context Effect --- p.20 / Chapter 2.5 --- F1 Evaluation Metric --- p.21 / Chapter 2.6 --- State of the Art --- p.23 / Chapter 2.6.1 --- Ontology Learning --- p.23 / Chapter 2.6.2 --- Semantics in Folksonomy --- p.26 / Chapter 3 --- Ontology Learning from Folksonomies --- p.28 / Chapter 3.1 --- Generating Ontologies with Basic Level Concepts from Folksonomies --- p.29 / Chapter 3.1.1 --- Modeling Instances and Concepts in Folksonomies --- p.29 / Chapter 3.1.2 --- The Metric of Basic Level Categories (Concepts) --- p.30 / Chapter 3.1.3 --- Basic Level Concepts Detection Algorithm --- p.31 / Chapter 3.1.4 --- Ontology Generation Algorithm --- p.34 / Chapter 3.2 --- Evaluation --- p.35 / Chapter 3.2.1 --- Data Set and Experiment Setup --- p.35 / Chapter 3.2.2 --- Quantitative Analysis --- p.36 / Chapter 3.2.3 --- Qualitative Analysis --- p.39 / Chapter 4 --- Context Effect on Ontology Learning from Folksonomies --- p.43 / Chapter 4.1 --- Context-aware Basic Level Concepts Detection --- p.44 / Chapter 4.1.1 --- Modeling Context in Folksonomies --- p.44 / Chapter 4.1.2 --- Context Effect on Category Utility --- p.45 / Chapter 4.1.3 --- Context-aware Basic Level Concepts Detection Algorithm --- p.46 / Chapter 4.2 --- Evaluation --- p.47 / Chapter 4.2.1 --- Data Set and Experiment Setup --- p.47 / Chapter 4.2.2 --- Result Analysis --- p.49 / Chapter 5 --- Potential Applications --- p.54 / Chapter 5.1 --- Categorization of Web Resources --- p.54 / Chapter 5.2 --- Applications of Ontologies --- p.55 / Chapter 6 --- Conclusion and Future Work --- p.57 / Chapter 6.1 --- Conclusion --- p.57 / Chapter 6.2 --- Future Work --- p.59 / Bibliography --- p.63

Orientador: Silvana Aparecida Borsetti Gregorio Vidotti / Coorientador: Juan Antonio Pastor-Sánchez / Banca: Silvana Drumon Monteiro / Banca: Ana Carolina Simionato Arakaki / Banca: Fernando Luiz Vechiato / Banca: José Eduardo Santarém Segundo / Resumo: Os avanços tecnológicos na sociedade têm possibilitado a inusitada geração e disponibilização de informação nos diversos âmbitos pelos múltiplos dispositivos e em diferentes formatos. A informação para ser acessada e usada pelos usuários nos ambientes digitais deverá previamente ser recuperada e encontrada. Diante disso, destaca-se que a Recuperação da Informação é amplamente discutida em múltiplos estudos desde a origem da Ciência da Informação e da Ciência da Computação, enquanto que a Findability torna-se foco de estudos nos últimos anos. Nesse contexto, com o intuito de esclarecer a relação entre a Recuperação da Informação e a Findability, e como esses processos acontecem nas imagens digitais - consideradas recursos imagéticos de natureza complexa pelas camadas de conteúdo que devem ser analisadas no processo de representação -, objetiva-se contribuir no aprimoramento da Recuperação e Findability com foco nas imagens digitais mediante o uso da polirrepresentação e das tecnologias da Web Semântica. Diante disto, a Ciência da Informação oferece subsídios que possibilitam trabalhos nessas temáticas com uma abordagem cientifica e tecnológica desde a integração dos diferentes conteúdos e informações dos recursos imagéticos e das necessidades informacionais do usuário. Para tanto, a metodologia desta pesquisa se caracteriza por ser de natureza básica que se tornou aplicada, quali-quantitativa e de tipo exploratória e descritiva, com um delineamento baseado no uso do método qua... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Technological advances in society have made possible the unusual generation and availability of information in the various scopes by multiple devices and in different formats. Information to be accessed and used by users in digital environments must first be retrieved and found. Therefore, it is important to highlight that Information Retrieval is widely discussed in multiple studies since the origin of Information Science and Computer Science, while Findability has become a focus of studies in recent years. In this context, in order to clarify the relationship between Information Retrieval and Findability, and how these processes take place in digital images - considered imagery resources of a complex nature by the content layers that must be analyzed in the representation process - aims to contribute to the enhancement of Retrieval and Findability focusing on digital images through the use of polyrepresentation and Semantic Web technologies. Faced with this, Information Science offers subsidies that enable work on these issues with a scientific and technological approach since the integration of different contents and information of the imagery resources and informational needs of the user. For this, the methodology of this research is characterized by being basic nature that has become applied, qualitative-quantitative and exploratory and descriptive, with a design based on the use of the quadripolar method using techniques such as bibliographic survey and document analysi... (Complete abstract click electronic access below) / Doutor

published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

In logic-based Knowledge Representation and Reasoning (KRR), ontologies are used to represent knowledge about a particular domain of interest in a precise way. The building blocks of ontologies include concepts, relations and objects. Those can be combined to form logical sentences which explicitly describe the domain. With this explicit knowledge one can perform reasoning to derive knowledge that is implicit in the ontology. Description Logics (DLs) are a group of knowledge representation languages with such capabilities that are suitable to represent ontologies. The process of building ontologies has been greatly simpli ed with the advent of graphical ontology editors such as SWOOP, Prote ge and OntoStudio. The result of this is that there are a growing number of ontology engineers attempting to build and develop ontologies. It is frequently the case that errors are introduced while constructing the ontology resulting in undesirable pieces of implicit knowledge that follows from the ontology. As such there is a need to extend current ontology editors with tool support to aid these ontology engineers in correctly designing and debugging their ontologies. Errors such as unsatis able concepts and inconsistent ontologies frequently occur during ontology construction. Ontology Debugging and Repair is concerned with helping the ontology developer to eliminate these errors from the ontology. Much emphasis, in current tools, has been placed on giving explanations as to why these errors occur in the ontology. Less emphasis has been placed on using this information to suggest e cient ways to eliminate the errors. Furthermore, these tools focus mainly on the errors of unsatis able concepts and inconsistent ontologies. In this dissertation we ll an important gap in the area by contributing an alternative approach to ontology debugging and repair for the more general error of a list of unwanted sentences. Errors such as unsatis able concepts and inconsistent ontologies can be represented as unwanted sentences in the ontology. Our approach not only considers the explanation of the unwanted sentences but also the identi cation of repair strategies to eliminate these unwanted sentences from the ontology. / Thesis (M.Sc.)-University of KwaZulu-Natal, Westville, 2010.

Thesis (M.S.)--State University of New York at Binghamton, Department of Computer Science, Thomas J. Watson School of Engineering and Applied Science, 2006. / Includes bibliographical references.

Thesis (M.S.)--State University of New York at Binghamton, Department of Computer Science, Thomas J. Watson School of Engineering and Applied Science, 2007. / Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 2008. / Vita. Includes bibliographical references (p. 128-132).

Zugl.: Barcelona, Universidad Politécnica de Cataluña., Diss., 2007 / Hergestellt on demand

Thesis (Ph.D.)--University of Florida, 2005. / Title from title page of source document. Document formatted into pages; contains 111 pages. Includes vita. Includes bibliographical references.