Building high-quality merged ontologies from multiple sources with requirements customization

Ontologies are the prime way of organizing data in the Semantic Web. Often, it is necessary to combine several, independently developed ontologies to obtain a knowledge graph fully representing a domain of interest. Existing approaches scale rather poorly to the merging of multiple ontologies due to using a binary merge strategy. Thus, we aim to investigate the extent to which the n-ary strategy can solve the scalability problem. This thesis contributes to the following important aspects: 1. Our n-ary merge strategy takes as input a set of source ontologies and their mappings and generates a merged ontology. For efficient processing, rather than successively merging complete ontologies pairwise, we group related concepts across ontologies into partitions and merge first within and then across those partitions. 2. We take a step towards parameterizable merge methods. We have identified a set of Generic Merge Requirements (GMRs) that merged ontologies might be expected to meet. We have investigated and developed compatibilities of the GMRs by a graph-based method. 3. When multiple ontologies are merged, inconsistencies can occur due to different world views encoded in the source ontologies To this end, we propose a novel Subjective Logic-based method to handling the inconsistency occurring while merging ontologies. We apply this logic to rank and estimate the trustworthiness of conflicting axioms that cause inconsistencies within a merged ontology. 4. To assess the quality of the merged ontologies systematically, we provide a comprehensive set of criteria in an evaluation framework. The proposed criteria cover a variety of characteristics of each individual aspect of the merged ontology in structural, functional, and usability dimensions. 5. The final contribution of this research is the development of the CoMerger tool that implements all aforementioned aspects accessible via a unified interface

Tools for enterprises collaboration in virtual enterprises

Virtual Enterprise (VE) is an organizational collaboration concept which provides a competitive edge in the globalized business environment. The life cycle of a VE consists of four stages i.e. opportunity identification (Pre-Creation), partner selection (Creation), operation and dissolution. The success of VEs depends upon the efficient execution of their VE-lifecycles along with knowledge enhancement for the partner enterprises to facilitate the future formation of efficient VEs. This research aims to study the different issues which occur in the VE lifecycle and provides a platform for the formation of high performance enterprises and VEs. In the pre-creation stage, enterprises look for suitable partners to create their VE and to exploit a market opportunity. This phase requires explicit and implicit information extraction from enterprise data bases (ECOS-ontology) for the identification of suitable partners. A description logic (DL) based query system is developed to extract explicit and implicit information and to identify potential partners for the creation of the VE. In the creation phase, the identified partners are analysed using different risks paradigms and a cooperative game theoretic approach is used to develop a revenue sharing mechanism based on enterprises inputs and risk minimization for optimal partner selection. In the operation phases, interoperability remains a key issue for seamless transfer of knowledge information and data. DL-based ontology mapping is applied in this research to provide interoperability in the VE between enterprises with different domains of expertise. In the dissolution stage, knowledge acquired in the VE lifecycle needs to be disseminated among the enterprises to enhance their competitiveness. A DL-based ontology merging approach is provided to accommodate new knowledge with existing data bases with logical consistency. Finally, the proposed methodologies are validated using the case study. The results obtained in the case study illustrate the applicability and effectiveness of proposed methodologies in each stage of the VE life cycle

Open biomedical pluralism : formalising knowledge about breast cancer phenotypes

We demonstrate a heterogeneity of representation types for breast cancer phenotypes and stress that the characterisation of a tumour phenotype often includes parameters that go beyond the representation of a corresponding empirically observed tumour, thus reflecting significant functional features of the phenotypes as well as epistemic interests that drive the modes of representation. Accordingly, the represented features of cancer phenotypes function as epistemic vehicles aiding various classifications, explanations, and predictions. In order to clarify how the plurality of epistemic motivations can be integrated on a formal level, we give a distinction between six categories of human agents as individuals and groups focused around particular epistemic interests. We analyse the corresponding impact of these groups and individuals on representation types, mapping and reasoning scenarios. Respecting the plurality of representations, related formalisms, expressivities and aims, as they are found across diverse scientific communities, we argue for a pluralistic ontology integration. Moreover, we discuss and illustrate to what extent such a pluralistic integration is supported by the distributed ontology language DOL, a meta-language for heterogeneous ontology representation that is currently under standardisation as ISO WD 17347 within the OntoIOp (Ontology Integration and Interoperability) activity of ISO/TC 37/SC 3. We particularly illustrate how DOL supports representations of parthood on various levels of logical expressivity, mapping of terms, merging of ontologies, as well as non-monotonic extensions based on circumscription allowing a transparent formal modelling of the normal/abnormal distinction in phenotypes

Semantic Keyword-based Search on Heterogeneous Information Systems

En los últimos años, con la difusión y el uso de Internet, el volumen de información disponible para los usuarios ha crecido exponencialmente. Además, la posibilidad de acceder a dicha información se ha visto impulsada por los niveles de conectividad de los que disfrutamos actualmente gracias al uso de los móviles de nueva generación y las redes inalámbricas (e.g., 3G, Wi-Fi). Sin embargo, con los métodos de acceso actuales, este exceso de información es tan perjudicial como la falta de la misma, ya que el usuario no tiene tiempo de procesarla en su totalidad. Por otro lado, esta información está detrás de sistemas de información de naturaleza muy heterogénea (e.g., buscadores Web, fuentes de Linked Data, etc.), y el usuario tiene que conocerlos para poder explotar al máximo sus capacidades. Esta diversidad se hace más patente si consideramos cualquier servicio de información como potencial fuente de información para el usuario (e.g., servicios basados en la localización, bases de datos exportadas mediante Servicios Web, etc.). Dado este nivel de heterogeneidad, la integración de estos sistemas se debe hacer externamente, ocultando su complejidad al usuario y dotándole de mecanismos para que pueda expresar sus consultas de forma sencilla. En este sentido, el uso de interfaces basados en palabras clave (keywords) se ha popularizado gracias a su sencillez y a su adopción por parte de los buscadores Web más usados. Sin embargo, esa sencillez que es su mayor virtud también es su mayor defecto, ya que genera problemas de ambigüedad en las consultas. Las consultas expresadas como conjuntos de palabras clave son inherentemente ambiguas al ser una proyección de la verdadera pregunta que el usuario quiere hacer. En la presente tesis, abordamos el problema de integrar sistemas de información heterogéneos bajo una búsqueda guiada por la semántica de las palabras clave; y presentamos QueryGen, un prototipo de nuestra solución. En esta búsqueda semántica abogamos por establecer la consulta que el usuario tenía en mente cuando escribió sus palabras clave, en un lenguaje de consulta formal para evitar posibles ambigüedades. La integración de los sistemas subyacentes se realiza a través de la definición de sus lenguajes de consulta y de sus modelos de ejecución. En particular, nuestro sistema: - Descubre el significado de las palabras clave consultando un conjunto dinámico de ontologías, y desambigua dichas palabras teniendo en cuenta su contexto (el resto de palabras clave), ya que cada una de las palabras tiene influencia sobre el significado del resto de la entrada. Durante este proceso, los significados que son suficientemente similares son fusionados y el sistema propone aquellos más probables dada la entrada del usuario. La información semántica obtenida en el proceso es integrada y utilizada en fases posteriores para obtener la correcta interpretación del conjunto de palabras clave. - Un mismo conjunto de palabras pueden representar diversas consultas aún cuando se conoce su significado individual. Por ello, una vez establecidos los significados de cada palabra y para obtener la consulta exacta del usuario, nuestro sistema encuentra todas las preguntas posibles utilizando las palabras clave. Esta traducción de palabras clave a preguntas se realiza empleando lenguajes de consulta formales para evitar las posibles ambigüedades y expresar la consulta de manera precisa. Nuestro sistema evita la generación de preguntas semánticamente incorrectas o duplicadas con la ayuda de un razonador basado en Lógicas Descriptivas (Description Logics). En este proceso, nuestro sistema es capaz de reaccionar ante entradas insuficientes (e.g., palabras omitidas) mediante la adición de términos virtuales, que representan internamente palabras que el usuario tenía en mente pero omitió cuando escribió su consulta. - Por último, tras la validación por parte del usuario de su consulta, nuestro sistema accede a los sistemas de información registrados que pueden responderla y recupera la respuesta de acuerdo a la semántica de la consulta. Para ello, nuestro sistema implementa una arquitectura modular permite añadir nuevos sistemas al vuelo siempre que se proporcione su especificación (lenguajes de consulta soportados, modelos y formatos de datos, etc.). Por otro lado, el trabajar con sistemas de información heterogéneos, en particular sistemas relacionados con la Computación Móvil, ha permitido que las contribuciones de esta tesis no se limiten al campo de la búsqueda semántica. A este respecto, se ha estudiado el ámbito de la semántica de las consultas basadas en la localización, y especialmente, la influencia de la semántica de las localizaciones en el procesado e interpretación de las mismas. En particular, se proponen dos modelos ontológicos para modelar y capturar la relaciones semánticas de las localizaciones y ampliar la expresividad de las consultas basadas en la localización. Durante el desarrollo de esta tesis, situada entre el ámbito de la Web Semántica y el de la Computación Móvil, se ha abierto una nueva línea de investigación acerca del modelado de conocimiento volátil, y se ha estudiado la posibilidad de utilizar razonadores basados en Lógicas Descriptivas en dispositivos basados en Android. Por último, nuestro trabajo en el ámbito de las búsquedas semánticas a partir de palabras clave ha sido extendido al ámbito de los agentes conversacionales, haciéndoles capaces de explotar distintas fuentes de datos semánticos actualmente disponibles bajo los principios del Linked Data

A framework for analyzing changes in health care lexicons and nomenclatures

Ontologies play a crucial role in current web-based biomedical applications for capturing contextual knowledge in the domain of life sciences. Many of the so-called bio-ontologies and controlled vocabularies are known to be seriously defective from both terminological and ontological perspectives, and do not sufficiently comply with the standards to be considered formai ontologies. Therefore, they are continuously evolving in order to fix the problems and provide valid knowledge. Moreover, many problems in ontology evolution often originate from incomplete knowledge about the given domain. As our knowledge improves, the related definitions in the ontologies will be altered. This problem is inadequately addressed by available tools and algorithms, mostly due to the lack of suitable knowledge representation formalisms to deal with temporal abstract notations, and the overreliance on human factors. Also most of the current approaches have been focused on changes within the internal structure of ontologies, and interactions with other existing ontologies have been widely neglected. In this research, alter revealing and classifying some of the common alterations in a number of popular biomedical ontologies, we present a novel agent-based framework, RLR (Represent, Legitimate, and Reproduce), to semi-automatically manage the evolution of bio-ontologies, with emphasis on the FungalWeb Ontology, with minimal human intervention. RLR assists and guides ontology engineers through the change management process in general, and aids in tracking and representing the changes, particularly through the use of category theory. Category theory has been used as a mathematical vehicle for modeling changes in ontologies and representing agents' interactions, independent of any specific choice of ontology language or particular implementation. We have also employed rule-based hierarchical graph transformation techniques to propose a more specific semantics for analyzing ontological changes and transformations between different versions of an ontology, as well as tracking the effects of a change in different levels of abstractions. Thus, the RLR framework enables one to manage changes in ontologies, not as standalone artifacts in isolation, but in contact with other ontologies in an openly distributed semantic web environment. The emphasis upon the generality and abstractness makes RLR more feasible in the multi-disciplinary domain of biomedical Ontology change management

Strategies for Managing Linked Enterprise Data

Data, information and knowledge become key assets of our 21st century economy. As a result, data and knowledge management become key tasks with regard to sustainable development and business success. Often, knowledge is not explicitly represented residing in the minds of people or scattered among a variety of data sources. Knowledge is inherently associated with semantics that conveys its meaning to a human or machine agent. The Linked Data concept facilitates the semantic integration of heterogeneous data sources. However, we still lack an effective knowledge integration strategy applicable to enterprise scenarios, which balances between large amounts of data stored in legacy information systems and data lakes as well as tailored domain specific ontologies that formally describe real-world concepts. In this thesis we investigate strategies for managing linked enterprise data analyzing how actionable knowledge can be derived from enterprise data leveraging knowledge graphs. Actionable knowledge provides valuable insights, supports decision makers with clear interpretable arguments, and keeps its inference processes explainable. The benefits of employing actionable knowledge and its coherent management strategy span from a holistic semantic representation layer of enterprise data, i.e., representing numerous data sources as one, consistent, and integrated knowledge source, to unified interaction mechanisms with other systems that are able to effectively and efficiently leverage such an actionable knowledge. Several challenges have to be addressed on different conceptual levels pursuing this goal, i.e., means for representing knowledge, semantic data integration of raw data sources and subsequent knowledge extraction, communication interfaces, and implementation. In order to tackle those challenges we present the concept of Enterprise Knowledge Graphs (EKGs), describe their characteristics and advantages compared to existing approaches. We study each challenge with regard to using EKGs and demonstrate their efficiency. In particular, EKGs are able to reduce the semantic data integration effort when processing large-scale heterogeneous datasets. Then, having built a consistent logical integration layer with heterogeneity behind the scenes, EKGs unify query processing and enable effective communication interfaces for other enterprise systems. The achieved results allow us to conclude that strategies for managing linked enterprise data based on EKGs exhibit reasonable performance, comply with enterprise requirements, and ensure integrated data and knowledge management throughout its life cycle

Ontology mapping: a logic-based approach with applications in selected domains

In advent of the Semantic Web and recent standardization efforts, Ontology has quickly become a popular and core semantic technology. Ontology is seen as a solution provider to knowledge based systems. It facilitates tasks such as knowledge sharing, reuse and intelligent processing by computer agents. A key problem addressed by Ontology is the semantic interoperability problem. Interoperability in general is a common problem in different domain applications and semantic interoperability is the hardest and an ongoing research problem. It is required for systems to exchange knowledge and having the meaning of the knowledge accurately and automatically interpreted by the receiving systems. The innovation is to allow knowledge to be consumed and used accurately in a way that is not foreseen by the original creator. While Ontology promotes semantic interoperability across systems by unifying their knowledge bases through consensual understanding, common engineering and processing practices, it does not solve the semantic interoperability problem at the global level. As individuals are increasingly empowered with tools, ontologies will eventually be created more easily and rapidly at a near individual scale. Global semantic interoperability between heterogeneous ontologies created by small groups of individuals will then be required. Ontology mapping is a mechanism for providing semantic bridges between ontologies. While ontology mapping promotes semantic interoperability across ontologies, it is seen as the solution provider to the global semantic interoperability problem. However, there is no single ontology mapping solution that caters for all problem scenarios. Different applications would require different mapping techniques. In this thesis, we analyze the relations between ontology, semantic interoperability and ontology mapping, and promote an ontology-based semantic interoperability solution. We propose a novel ontology mapping approach namely, OntoMogic. It is based on first order logic and model theory. OntoMogic supports approximate mapping and produces structures (approximate entity correspondence) that represent alignment results between concepts. OntoMogic has been implemented as a coherent system and is applied in different application scenarios. We present case studies in the network configuration, security intrusion detection and IT governance & compliance management domain. The full process of ontology engineering to mapping has been demonstrated to promote ontology-based semantic interoperability

Ontological View-driven Semantic Integration in Open Environments

In an open computing environment, such as the World Wide Web or an enterprise Intranet, various information systems are expected to work together to support information exchange, processing, and integration. However, information systems are usually built by different people, at different times, to fulfil different requirements and goals. Consequently, in the absence of an architectural framework for information integration geared toward semantic integration, there are widely varying viewpoints and assumptions regarding what is essentially the same subject. Therefore, communication among the components supporting various applications is not possible without at least some translation. This problem, however, is much more than a simple agreement on tags or mappings between roughly equivalent sets of tags in related standards. Industry-wide initiatives and academic studies have shown that complex representation issues can arise. To deal with these issues, a deep understanding and appropriate treatment of semantic integration is needed. Ontology is an important and widely accepted approach for semantic integration. However, usually there are no explicit ontologies with information systems. Rather, the associated semantics are implied within the supporting information model. It reflects a specific view of the conceptualization that is implicitly defining an ontological view. This research proposes to adopt ontological views to facilitate semantic integration for information systems in open environments. It proposes a theoretical foundation of ontological views, practical assumptions, and related solutions for research issues. The proposed solutions mainly focus on three aspects: the architecture of a semantic integration enabled environment, ontological view modeling and representation, and semantic equivalence relationship discovery. The solutions are applied to the collaborative intelligence project for the collaborative promotion / advertisement domain. Various quality aspects of the solutions are evaluated and future directions of the research are discussed

ONTOLOGY MAPPING: TOWARDS SEMANTIC INTEROPERABILITY IN DISTRIBUTED AND HETEROGENEOUS ENVIRONMENTS

The World Wide Web (WWW) now is widely used as a universal medium for information exchange. Semantic interoperability among different information systems in the WWW is limited due to information heterogeneity, and the non semantic nature of HTML and URLs. Ontologies have been suggested as a way to solve the problem of information heterogeneity by providing formal, explicit definitions of data and reasoning ability over related concepts. Given that no universal ontology exists for the WWW, work has focused on finding semantic correspondences between similar elements of different ontologies, i.e., ontology mapping. Ontology mapping can be done either by hand or using automated tools. Manual mapping becomes impractical as the size and complexity of ontologies increases. Full or semi-automated mapping approaches have been examined by several research studies. Previous full or semi-automated mapping approaches include analyzing linguistic information of elements in ontologies, treating ontologies as structural graphs, applying heuristic rules and machine learning techniques, and using probabilistic and reasoning methods etc. In this paper, two generic ontology mapping approaches are proposed. One is the PRIOR+ approach, which utilizes both information retrieval and artificial intelligence techniques in the context of ontology mapping. The other is the non-instance learning based approach, which experimentally explores machine learning algorithms to solve ontology mapping problem without requesting any instance. The results of the PRIOR+ on different tests at OAEI ontology matching campaign 2007 are encouraging. The non-instance learning based approach has shown potential for solving ontology mapping problem on OAEI benchmark tests

Doctor of Philosophy

dissertationExchanging patient specific information across heterogeneous information systems is a critical but increasingly complex and expensive challenge. Lacking a universal unique identifier for healthcare, patient records must be linked using combinations of identity attributes such as name, date of birth, and sex. A state's birth certificate registry contains demographic information that is potentially very valuable for identity resolution, but its use for that purpose presents numerous problems. The objectives of this research were to: (1) assess the frequency, extent, reasons, and types of changes on birth certificates; (2) develop and evaluate an ontology describing information used in identity resolution; and (3) use a logical framework to model identity transactions and assess the impact of policy decisions in a cross jurisdictional master person index. To understand birth certificate changes, we obtained de identifified datasets from the Utah birth certifificate registry, including history and reasons for changes from 2000 to 2012. We conducted cohort analyses, examining the number, reason, and extent of changes over time, and cross sectional analyses to assess patterns of changes. We evaluated an ontological approach to overcome heterogeneity between systems exchanging identity information and demonstrated the use of two existing ontologies, the Simple Event Model (SEM) and the Clinical Element Model (CEM), to capture an individual's identity history. We used Discrete Event Calculus to model identity events iv across domains and over time. Models were used to develop contextual rules for releasing minimal information from birth certificate registries for sensitive cases such as adoptions. Our findings demonstrate that the mutability of birth certificates makes them a valuable resource for identity resolution, provided that changes can be captured and modeled in a usable form. An ontology can effectively model identity attributes and the events that cause them to change over time, as well as to overcome syntactic and semantic heterogeneity. Finally, we show that dynamic, contextual rules can be used to govern the flow of identity information between systems, allowing entities to link records in the most difficult cases, avoid costly human review, and avoid the threats to privacy that come from such review