The aim of this thesis is the development of an infrastructure which is used for semantic retrieval of multimedia contents. Motivated by the needs of semantic search and retrieval of multimedia contents, operating directly on the MPEG-7 based annotations can be thought as a reasonable way for meeting these needs as MPEG-7 is a common standard providing a wide multimedia content description schema. However, it is clear that the MPEG-7 formalism is deficient about the semantics and reasoning support. From this perspective, additionally, we need to represent MPEG-7 descriptions in a new formalism in order to fill the gap about semantics and reasoning. Then, the semantic web and multimedia technologies intercept at this point of multimedia semantics. In this thesis, OWL Web Ontology Language, which is based on description logic has been utilized to model a connection between the ontology semantics and video metadata. Modeling the domain of the videos using ontologies and the MPEG-7 descriptions, and reasoning on the videos by the help of the logical formalism of these ontologies are the main objectives of the thesis.

Title: Reasoning in Description Logics Author: Mgr. Jaromír Malenko Department: Department of Theoretical Computer Science and Mathematical Logic, Faculty of Mathematics and Physics, Charles University in Prague Supervisor: Prof. RNDr. Petr Štěpánek, DrSc.; Department of Theoretical Computer Science and Mathematical Logic, Faculty of Mathematics and Physics, Charles University in Prague Keywords: Description logic, Reasoner, Cartesian product, Non-monotonic reasoning Abstract: We deal with several aspects of reasoning in Description Logics. First, since description logic (DL) is a subset of First Order Logic (FOL), we use a FOL reasoner to reason in DL. We implemented dl2fol, a DL reasoner that takes an ontology (a DL theory with rules), translates it into a FOL theory, passes this set of formulae to an underling FOL reasoner, and interprets the result in terms of given ontology. This is an effective method for reasoning with newly introduced language constructors. However, we observed longer running times and that satisfiability of some DL concepts wasn't proved due to FOL undecidability. Second, we extend two DLs by introducing new language construct: cartesian product (CP) of concepts and roles. This allows for expressing relationships, that are not expressible by other means in weaker DLs. We...

FROTA, Débora Farias. Uma lógica de descrição Default. 2011. 79 f. Dissertação (Mestrado em ciência da computação)- Universidade Federal do Ceará, Fortaleza-CE, 2011. / Submitted by Elineudson Ribeiro (elineudsonr@gmail.com) on 2016-07-11T13:21:13Z No. of bitstreams: 1 2011_dis_dffrota.pdf: 945021 bytes, checksum: 9adb958d87b14104dcd8db9fc4c4bd6f (MD5) / Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2016-07-15T13:25:42Z (GMT) No. of bitstreams: 1 2011_dis_dffrota.pdf: 945021 bytes, checksum: 9adb958d87b14104dcd8db9fc4c4bd6f (MD5) / Made available in DSpace on 2016-07-15T13:25:42Z (GMT). No. of bitstreams: 1 2011_dis_dffrota.pdf: 945021 bytes, checksum: 9adb958d87b14104dcd8db9fc4c4bd6f (MD5) Previous issue date: 2011 / Knowledge formalization and reasoning automatization are central within Arti cial Intelligence. First Order Logic has been traditionally used for such purposes. However, it is better suited to deal with complete knowledge in ideal circumstances. In real situations, in which the knowledge is partial, First Order Logic is not su cient. Nonmonotonic logics have been proposed to better cope with practical reasoning. A successful formalization of nonmonotonic reasoning is the Reiter's default logic which extends classical logic with default rules. Unfortunately, default logic is undecidable. In this work, we propose a description default logic expressible enough to formalize practical reasoning in knowledge bases. It has as its monotonic basis the ALC Description Logic. We add some restrictions to the application of defaults in order to obtain nice properties such as coherence and the elimination of anomalous extensions. We present the main algorithms used to build an extension with a step by step complexity analysis. / A formalização do conhecimento e a automatização do raciocínio são assuntos centrais de pesquisa da Inteligência Artificial. A Lógica de Primeira Ordem tem sido tradicionalmente utilizada para tais propósitos. No entanto, ela é mais adequada para lidar com conhecimento completo em circunstâncias ideais. Em situações reais, nas quais o conhecimento é parcial, a Lógica de Primeira Ordem não é suficiente. Lógicas não-monotônicas têm sido propostas para melhor lidar com o raciocínio prático. Uma formalização do raciocínio não-monotônico bem-sucedida é a Lógica Default de Reiter que estende a Lógica de Primeira Ordem com regras default. Infelizmente, a Lógica Default é indecidível. Nesta dissertação, propomos uma Lógica de Descrição Default expressiva o suficiente para formalizar o raciocínio prático sobre bases de conhecimento. Ela tem como base monotônica a Lógica de Descrição ALC. Adicionamos algumas restrições à aplicação dos defaults a fim de obter propriedades interessantes, tais como a coerência e a eliminação de extensões anômalas. Apresentamos os principais algoritmos usados para construir uma extensão com um passo-a-passo e suas análise de complexidade.

OLIVEIRA, Henrique Viana. Refinamento de consultas em lógicas de descrição utilizando teoria dos Rough sets. 2012. 111 f. Dissertação (Mestrado em ciência da computação)- Universidade Federal do Ceará, Fortaleza-CE, 2012. / Submitted by Elineudson Ribeiro (elineudsonr@gmail.com) on 2016-07-11T16:33:31Z No. of bitstreams: 1 2012_dis_hvoliveira.pdf: 789598 bytes, checksum: d75ef093adc56cc930f52c1e486ead5a (MD5) / Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2016-07-18T13:43:45Z (GMT) No. of bitstreams: 1 2012_dis_hvoliveira.pdf: 789598 bytes, checksum: d75ef093adc56cc930f52c1e486ead5a (MD5) / Made available in DSpace on 2016-07-18T13:43:45Z (GMT). No. of bitstreams: 1 2012_dis_hvoliveira.pdf: 789598 bytes, checksum: d75ef093adc56cc930f52c1e486ead5a (MD5) Previous issue date: 2012 / Query Refinement consists of methods that modify the terms of a consult aiming the change of its result obtained previously. Refinements can be done of several ways and different approaches can be applied to it. This work proposes to apply methods of Query Refinement based on Rough Set theory, using it as an alternative for the refinement problem. The proposed methods will be grounded in the languages of Description Logics, which are commonly used on problems involving knowledge bases or ontologies representation. Two extensions of Description Logics with the Rough Set theory are introduced in this dissertation. We will prove the complexity of satisfiability of these logics, as well as the complexities of the query refinement methods applied to these logics. Finally, we will show quality measures which will aid to choose the results of the refinements obtained. / Refinamento de consulta consiste de técnicas que modificam os termos de uma consulta com o objetivo de alterar os resultados obtidos inicialmente. Para a realização de tal fim, diversas abordagens podem ser aplicadas e diferentes tipos de refinamentos podem ser considerados. Este trabalho propõe aplicar a teoria dos Rough Sets como uma nova alternativa de solução para o problema. Através das noções presentes nessa teoria, iremos desenvolver técnicas que serão aplicadas nas linguagens de Lógicas de Descrição, que são comumente utilizadas em problemas de representação de bases de conhecimento ou ontologias. Além disso, introduziremos duas extensões de Lógicas de Descrição capazes de representar as operações da teoria dos Rough Sets. Provaremos os resultados de complexidade de decisão dessas duas lógicas, assim como os resultados de complexidade das técnicas de refinamentos desenvolvidas. Por fim, mostraremos métricas de qualidade que poderão ser usadas para melhorar o resultado dos refinamentos obtidos.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / Refinamento de consulta consiste de tÃcnicas que modificam os termos de uma consulta com o objetivo de alterar os resultados obtidos inicialmente. Para a realizaÃÃo de tal fim, diversas abordagens podem ser aplicadas e diferentes tipos de refinamentos podem ser considerados. Este trabalho propÃe aplicar a teoria dos Rough Sets como uma nova alternativa de soluÃÃo para o problema. AtravÃs das noÃÃes presentes nessa teoria, iremos desenvolver tÃcnicas que serÃo aplicadas nas linguagens de LÃgicas de DescriÃÃo, que sÃo comumente utilizadas em problemas de representaÃÃo de bases de conhecimento ou ontologias. AlÃm disso, introduziremos duas extensÃes de LÃgicas de DescriÃÃo capazes de representar as operaÃÃes da teoria dos Rough Sets. Provaremos os resultados de complexidade de decisÃo dessas duas lÃgicas, assim como os resultados de complexidade das tÃcnicas de refinamentos desenvolvidas. Por fim, mostraremos mÃtricas de qualidade que poderÃo ser usadas para melhorar o resultado dos refinamentos obtidos. / Query Refinement consists of methods that modify the terms of a consult aiming the change of its result obtained previously. Refinements can be done of several ways and different approaches can be applied to it. This work proposes to apply methods of Query Refinement based on Rough Set theory, using it as an alternative for the refinement problem. The proposed methods will be grounded in the languages of Description Logics, which are commonly used on problems involving knowledge bases or ontologies representation. Two extensions of Description Logics with the Rough Set theory are introduced in this dissertation. We will prove the complexity of satisfiability of these logics, as well as the complexities of the query refinement methods applied to these logics. Finally, we will show quality measures which will aid to choose the results of the refinements obtained.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / A formalizaÃÃo do conhecimento e a automatizaÃÃo do raciocÃnio sÃo assuntos centrais de pesquisa da InteligÃncia Articial. A LÃgica de Primeira Ordem tem sido tradicionalmente utilizada para tais propÃsitos. No entanto, ela à mais adequada para lidar com conhecimento completo em circunstÃncias ideais. Em situaÃÃes reais, nas quais o conhecimento à parcial, a LÃgica de Primeira Ordem nÃo à suciente. LÃgicas nÃo-monotÃnicas tÃm sido propostas para melhor lidar com o raciocÃnio prÃtico. Uma formalizaÃÃo do raciocÃnio nÃo-monotÃnico bem-sucedida à a LÃgica Default de Reiter que estende a LÃgica de Primeira Ordem com regras default. Infelizmente, a LÃgica Default à indecidÃvel. Nesta dissertaÃÃo, propomos uma LÃgica de DescriÃÃo Default expressiva o suciente para formalizar o raciocÃnio prÃtico sobre bases de conhecimento. Ela tem como base monotÃnica a LÃgica de DescriÃÃo ALC. Adicionamos algumas restriÃÃes à aplicaÃÃo dos defaults a m de obter propriedades interessantes, tais como a coerÃncia e a eliminaÃÃo de extensÃes anÃmalas. Apresentamos os principais algoritmos usados para construir uma extensÃo com um passo-a-passo e suas anÃlise de complexidade. / Knowledge formalization and reasoning automatization are central within Articial Intelligence. First Order Logic has been traditionally used for such purposes. However, it is better suited to deal with complete knowledge in ideal circumstances. In real situations, in which the knowledge is partial, First Order Logic is not sucient. Nonmonotonic logics have been proposed to better cope with practical reasoning. A successful formalization of nonmonotonic reasoning is the Reiter's default logic which extends classical logic with default rules. Unfortunately, default logic is undecidable. In this work, we propose a description default logic expressible enough to formalize practical reasoning in knowledge bases. It has as its monotonic basis the ALC Description Logic. We add some restrictions to the application of defaults in order to obtain nice properties such as coherence and the elimination of anomalous extensions. We present the main algorithms used to build an extension with a step by step complexity analysis.

Description Logic (abbrv. DL) belongs to the field of knowledge representation and reasoning. DL researchers have developed a large family of logic-based languages, so-called description logics (abbrv. DLs). These logics allow their users to explicitly represent knowledge as ontologies, which are finite sets of (human- and machine-readable) axioms, and provide them with automated inference services to derive implicit knowledge. The landscape of decidability and computational complexity of common reasoning tasks for various description logics has been explored in large parts: there is always a trade-off between expressibility and reasoning costs. It is therefore not surprising that DLs are nowadays applied in a large variety of domains: agriculture, astronomy, biology, defense, education, energy management, geography, geoscience, medicine, oceanography, and oil and gas. Furthermore, the most notable success of DLs is that these constitute the logical underpinning of the Web Ontology Language (abbrv. OWL) in the Semantic Web. Formal Concept Analysis (abbrv. FCA) is a subfield of lattice theory that allows to analyze data-sets that can be represented as formal contexts. Put simply, such a formal context binds a set of objects to a set of attributes by specifying which objects have which attributes. There are two major techniques that can be applied in various ways for purposes of conceptual clustering, data mining, machine learning, knowledge management, knowledge visualization, etc. On the one hand, it is possible to describe the hierarchical structure of such a data-set in form of a formal concept lattice. On the other hand, the theory of implications (dependencies between attributes) valid in a given formal context can be axiomatized in a sound and complete manner by the so-called canonical base, which furthermore contains a minimal number of implications w.r.t. the properties of soundness and completeness. In spite of the different notions used in FCA and in DLs, there has been a very fruitful interaction between these two research areas. My thesis continues this line of research and, more specifically, I will describe how methods from FCA can be used to support the automatic construction and extension of DL ontologies from data.

Access restrictions are essential in standard information systems and became an issue for ontologies in the following two aspects. Ontologies can represent explicit and implicit knowledge about an access policy. For this aspect we provided a methodology to represent and systematically complete role-based access control policies. Orthogonally, an ontology might be available for limited reading access. Independently of a specific ontology language or reasoner, we provided a lattice-based framework to assign labels to an ontology’s axioms and consequences. We looked at the problems to compute and repair one or multiple consequence labels and to assign a query-based access restriction. An empirical evaluation has shown that the algorithms perform well in practical scenarios with large-scale ontologies.

Golog is a powerful programming language for logic-based agents. The primitives of the language are actions whose preconditions and effects are defined in a Situation Calculus action theory using first-order logic. To describe possible courses of actions the programmer can freely combine imperative control structures with constructs for non-deterministic choice, leaving it to the system to resolve the non-determinism in a suitable manner. Golog has been successfully used for high-level decision making in the area of cognitive robotics. Obviously, it is important to verify certain properties of a Golog program before executing it on a physical robot. However, due to the high expressiveness of the language the verification problem is in general undecidable. In this thesis, we study the verification problem for Golog programs over actions defined in action languages based on Description Logics and explore the boundary between decidable and undecidable fragments.

A semantic framework to improve automated reasoning, retrieval, reuse and communication of engineering design knowledge is presented in this research. We consider design to be a process involving a sequence of decisions informed by the current state of information. As such, the information model developed is structured to reflect the conceptualizations of engineering design decisions with a particular emphasis on semantically capturing design rationale. Based on a description logic formalism, the information model was implemented using the Web Ontology Language (OWL), which provides a semantically rich and sufficiently broad specification of design decisions capable of supporting the application of any specific decision-making method. Through this approach knowledge reuse is achieved by communicating design rationale and facilitating semantic-based retrieval of knowledge. A case study is presented to illustrate three key features of the approach: 1) seamless integration of separate modular domain ontologies and instance knowledge related to engineering design that are needed to support decision making, 2) the explicit documentation of design rationale through design decisions, and 3) the application of an automated method for matching and retrieving stored knowledge. The automated retrieval method is implemented using the Semantic Web Rule Language (SWRL) and serves as an example of the type of reasoning services that can easily be achieved by formally and semantically representing design knowledge.