Indexing data for efficient search capabilities is a core problem in many domains of computer science. As applications centered around semantic data sources become more common, the need for more sophisticated indexing and querying capabilities arises. In particular, the need to search for specific information in the presence of a terminology or ontology (i.e. a set of logic based rules that describe concepts and their relations) becomes of particular importance, as the information a user seeks may exists as an entailment of the explicit data by means of the terminology. This variant on traditional indexing and search problems forms the foundation of a range of possible technologies for semantic data. In this work, we propose an ordering language for specifying partial orders over semantic data items modeled as descriptions in a description logic. We then show how these orderings can be used as the basis of a search tree index for processing \emph{concept searches} in the presence of a terminology. We study in detail the properties of the orderings and the associated index structure, and also explore a relationship between ordering descriptions called \emph{order refinement}. A sound and complete procedure for deciding refinement is given. We also empirically evaluate a prototype implementation of our index structure, validating its potential efficacy in semantic query problems.

We consider the overlap between reasoning involved in <em>conjunctive query optimization</em> (CQO) and in tableaux-based approaches to reasoning about subsumption in <em>description logics</em> (DLs). In both cases, an underlying graph is created, searched and modified. This process is determined by a given <em>query</em> and <em>database schema</em> in the first case and by a given <em>description</em> and <em>terminology</em> in the second. The opportunities for overlap derive from an abundance of reductions of various schema languages to terminologies for common DL dialects, and from the fact that descriptions can in turn be viewed as queries that compute a single column. <br /><br /> Our main contributions are as follows. We present the design and implementation of a common graph utility that integrates the requirements for both CQO and DL reasoning. We then verify this model by also presenting the design and implementation for two drivers, one that implements a query optimizer for a conjunctive query language extended with descriptions, and one that implements a complete DL reasoner for a feature based DL dialect.

Indexing data for efficient search capabilities is a core problem in many domains of computer science. As applications centered around semantic data sources become more common, the need for more sophisticated indexing and querying capabilities arises. In particular, the need to search for specific information in the presence of a terminology or ontology (i.e. a set of logic based rules that describe concepts and their relations) becomes of particular importance, as the information a user seeks may exists as an entailment of the explicit data by means of the terminology. This variant on traditional indexing and search problems forms the foundation of a range of possible technologies for semantic data. In this work, we propose an ordering language for specifying partial orders over semantic data items modeled as descriptions in a description logic. We then show how these orderings can be used as the basis of a search tree index for processing \emph{concept searches} in the presence of a terminology. We study in detail the properties of the orderings and the associated index structure, and also explore a relationship between ordering descriptions called \emph{order refinement}. A sound and complete procedure for deciding refinement is given. We also empirically evaluate a prototype implementation of our index structure, validating its potential efficacy in semantic query problems.

Purpose: This PhD research examines how two different institutional logics of corporate governance have shaped the discourse on executive remuneration. Corporate Logic implies executives are intrinsically motivated and will act in the best interests of shareholders as long as their total remuneration is competitive and fair. On the other hand, Investor Logic implies executives are extrinsically motivated (opportunistic) and will only act in the best interests of shareholders if short- and long-term incentive schemes are designed appropriately. Approach: The research has an interpretive methodology and consists of three phases. First, the diffusion of both Logics is examined through a content analysis of a large sample of corporate governance codes of practice and corporate annual reports. Second, how both Logics are embedded in the remuneration principles and practices that are recommended by code issuers and adopted by companies is scrutinised using discourse analysis. Third, how both Logics have shaped the beliefs and decision-making of non-executive directors, executives, and others is studied using discourse analysis. Findings: Both Logics are embedded in the discourse on executive remuneration, although there has been a strengthening of Investor Logic over time. Both Logics co-exist as distinct from compete in the discourse because it has become taken-for-granted that executives should be remunerated comparably to other executives (Corporate Logic) and in line with shareholder returns (Investor Logic). Directors and others manage tension between Corporate Logic and Investor Logic by prioritising (or ordering) the Logics. Theoretical implications: The research shows how competitive and institutional pressures influence how remuneration decisions are made and reported. However, institutional change is complex because companies influence and are influenced by code issuers and others. Practical implications: As both Logics are embedded in the beliefs of companies, code issuers and others, executive remuneration practices have become unnecessarily complex and convoluted. The case for a simpler approach to executive remuneration is advanced.

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This dissertation explains the existence, sources, and variability of intra-organizational conflict in a hybrid organization. It assesses the usefulness of "structural" and "cultural" explanations of conflict and ultimately advances an alternative explanation for the presence of and variability in conflict in a hybrid organization. Homeowners' associations are used as a case for understanding the development of multiple institutional logics and the relationship between institutional pluralism and complexity and the presence of and variability in conflict in a hybrid organization. Drawing from quantitative and qualitative research conducted on homeowners' associations in the Greater-Boston area, including 250 surveys and 56 in-depth interviews with board members of homeowners' associations, I show how the American history and ideology of homeownership has generated two multiple, permanent, and functionally contradictory institutional logics--one based on the market and the other based on the community--in homeowners' associations. Using institutional theory and the concepts of institutional work and ambidexterity, I argue that organizational actor's responses to the presence of institutional pluralism and complexity, as evidenced in their perceptions and practices, determine whether a hybrid organization is subject to more or less conflict. My findings lead to three general conclusions. First, many homeowners' associations experience significant conflict. Second, structural and cultural explanations of conflict only partially explain the presence of conflict in homeowners' associations. They do not explain the variability of conflict in homeowners' associations. Third, and most significantly, the micro-actions of organizational actors matter in situations of institutional pluralism and complexity. I propose that organizational actors' responses to institutional pluralism and complexity explain variability in conflict; organizational actors either "don't do" or "do" logics. Organizational actors who "don't do" logics respond to institutional pluralism and complexity by eliminating and compartmentalizing logics. They perceive multiplicity as novel and problematic and enact disruptive practices to contest and separate logics. This results in more conflict. Organizational actors who "do" logics respond to institutional pluralism and complexity ambidextrously. They perceive multiplicity as routine, and even beneficial, and enact practices to maintain multiple institutional logics via context-specific and purposeful practices including adjustment, improvisation, and switching. This results in less conflict.

Este trabalho apresenta três principais contribuições: i. a proposta de uma nova lógica de descrição probabilística; ii. um novo algoritmo de inferência em primeira ordem a ser utilizado em terminologias representadas nessa lógica; e iii. aplicações práticas em problemas reais. A lógica aqui proposta, crALC (credal ALC), adiciona inclusões probabilísticas na popular lógica ALC combinando as terminologias com condições de aciclicidade, de Markov, e adotando uma semântica baseada em interpretações. Como os métodos de inferência exata tradicionalmente apresentam problemas de escalabilidade devido à presença de quantificadores (restrições universal e existencial), apresentamos um algoritmo de loopy propagation em primeira-ordem que se comporta bem para terminologias com domínios não triviais. Uma série de testes foi feita com o algoritmo proposto em comparação com algoritmos tradicionais da literatura; os resultados apresentados mostram uma clara vantagem em relação aos outros algoritmos. São apresentadas ainda duas aplicações da lógica e do algoritmo para resolver problemas reais da área de robótica móvel. Embora os problemas tratados sejam relativamente simples, eles constituem a base de muitos outros problemas da área, sendo um passo importante na representação de conhecimento de agentes/robôs autônomos e no raciocínio sobre esse conhecimento. / This work presents two major contributions: i. a new probabilistic description logic; ii. a new algorithm for inference in terminologies expressed in this logic; iii. practical applications in real tasks. The proposed logic, referred to as crALC (credal ALC), adds probabilistic inclusions to the popular logic ALC, combining the usual acyclicity and Markov conditions, and adopting interpretation-based semantics. As exact inference does not seem scalable due to the presence of quantifiers (existential and universal), we present a first-order loopy propagation algorithm that behaves appropriately for non-trivial domain sizes. A series of tests were done comparing the performance of the proposed algorithm against traditional ones; the presented results are favorable to the first-order algorithm. Two applications in the field of mobile robotics are presented, using the new probabilistic logic and the inference algorithm. Though the problems can be considered simple, they constitute the basis for many other tasks in mobile robotics, being a important step in knowledge representation and in reasoning about it.

The Web Ontology Language OWL is based on the highly expressive description logic SROIQ, which allows OWL ontology users to employ out-of-the-box reasoners to compute information that is not only explicitly asserted, but entailed by the ontology. Explanation facilities for entailments of OWL ontologies form an essential part of ontology development tools, as they support users in detecting and repairing errors in potentially large and highly complex ontologies, thus helping to ensure ontology quality. Justifications, minimal subsets of an ontology that are sufficient for an entailment to hold, are currently the prevalent form of explanation in OWL ontology development tools. They have been found to significantly reduce the time and effort required to debug erroneous entailments. A large number of entailments, however, have not only one but many justifications, which can make it considerably more challenging for a user to find a suitable repair for the entailment.In this thesis, we investigate the relationships between multiple justifications for both single and multiple entailments, with the goal of exploiting this justificatory structure in order to devise new coping strategies for multiple justifications. We describe various aspects of the justificatory structure of OWL ontologies, such as shared axiom cores and structural similarities. We introduce a model for measuring user effort in the debugging process and propose debugging strategies that exploit the justificatory structure in order to reduce user effort. Finally, an analysis of a large corpus of ontologies from the biomedical domain reveals that OWL ontologies used in practice frequently exhibit a rich justificatory structure.

Description logics are a family of logics that provide formalisms for representing and reasoning about knowledge, based on describing concepts, in a structured and formally well-understood way. They provide the logical foundation for the web ontology language (OWL), which increased awareness of them recently. The most popular techniques for decision procedures for description logics are tableau reasoning methods, which have a long tradition and are well established in automated reasoning. This thesis investigates the possibility of finding a general and optimised blocking mechanism for description logics with the finite model property. It suggests that, while the high branching factor for unrestricted blocking reduces its performance, suitable control of the application of the blocking rule can make the performance acceptable while preserving termination. This claim is supported by experiments that compare the performance of two sample controlled versions of unrestricted blocking. In order to show the generality and power of controlled versions of unrestricted blocking, it is shown how some of the mainstream and most successful standard blocking mechanisms can be approximated as restricted forms of unrestricted blocking. These approximations have the advantage of always being sound compared to their standard versions, which are known to be sound only for some logics. Here, a variation of unrestricted blocking which can ensure strong termination is also introduced. This is done through introducing a new rule that uses the inequality expressions introduced by the blocking rule. The weak termination property of unrestricted blocking is one of its weak points which by this variant of blocking can be addressed. The work presented in this thesis should be of value to people who are working on generalising different aspects of reasoning methods. As blocking plays a critical role in termination of tableau provers, exploration of different variations of unrestricted blocking introduced here may be also of interest for the artificial intelligence researcher.

A reutilização de conhecimento é uma tarefa chave para qualquer sistema computacional. Entretanto, o reúso indiscriminado desse conhecimento pode gerar resultados conflitantes com o objetivo de uso do conhecimento, levando sistemas a se comportarem de maneira imprevisível. Neste trabalho estudamos as consequências do reúso de conhecimento em ontologias baseadas em lógicas de descrição. Focamos principalmente nos problemas que podem ser causados pela fusão de ontologias. Investigamos e comparamos a capacidade das ferramentas de desenvolvimento de ontologias atuais de lidarem com esses problemas e como a teoria se desenvolveu para resolver os mesmos problemas. Realizamos a construção de um arcabouço lógico e de software, organizado na forma de um processo, que tem como objetivo auxiliar o projetista de ontologias a resolver conflitos advindos da fusão. O processo agrupa tarefas descritas normalmente na literatura em separado. Acreditamos que a união dessas abordagens leva a uma melhor solução de conflitos. Durante o desenvolvimento deste trabalho, concentramos nossos esforços principalmente no desenvolvimento de algoritmos para a construção de sub-ontologias maximais, onde os conflitos não ocorram, bem como a ordenação desses conjuntos segundo critérios comuns discutidos na literatura. Tais estratégias foram implementadas em software e testadas utilizando dados gerados automaticamente e dados reais. / Knowledge reuse is a key task during any system development. Nevertheless, careless knowledge reuse may generate conflicting outcomes regarding the system goal, leading such systems to unpredictable behaviour. With that in mind, during this research we studied the consequences of knowledge reuse in ontologies based on description logics. We focused mainly on conflicts arising from ontology merging. We investigated and compared the features developed for this purpose on ontology development tools and how the theory field proposed to deal with the same issues. We developed both a logical and a software framework grouped into a process that aims to help the ontology designer solve conflicts arising from ontology merging. The process groups common tasks that are normally described separately. We believe that the unification of these approaches should result in a better solution for the merging conflicts. We concentrated our efforts during this work on building algorithms for building maximal sub-ontologies where such conflicts are non-existent as well as means for ordering such sets according to a few relevance criteria commonly described at the literature. Such algorithms were implemented and tested against automatically generated and real data.