Ontology-based data access (OBDA) augments classical query answering in databases by including domain knowledge provided by an ontology. An ontology captures the terminology of an application domain and describes domain knowledge in a machine-processable way. Formal ontology languages additionally provide semantics to these specifications. Systems for OBDA thus may apply logical reasoning to answer queries; they use the ontological knowledge to infer new information, which is only implicitly given in the data. Moreover, they usually employ the open-world assumption, which means that knowledge not stated explicitly in the data or inferred is neither assumed to be true nor false. Classical OBDA regards the knowledge however only w.r.t. a single moment, which means that information about time is not used for reasoning and hence lost; in particular, the queries generally cannot express temporal aspects. We investigate temporal query languages that allow to access temporal data through classical ontologies. In particular, we study the computational complexity of temporal query answering regarding ontologies written in lightweight description logics, which are known to allow for efficient reasoning in the atemporal setting and are successfully applied in practice. Furthermore, we present a so-called rewritability result for ontology-based temporal query answering, which suggests ways for implementation. Our results may thus guide the choice of a query language for temporal OBDA in data-intensive applications that require fast processing, such as context recognition.

Building and mantaining large-scale ontologies is an error-prone task. It is thus not uncommon to find unwanted or unexpected consequences that follow implicitely from the restrictions in the ontology. To understand and correct these consequences, it is helpful to find the specific portions of the ontology that are responsible for them. Axiom-pinpointing is the task of finding minimal subontologies that entail a given consequence, also called MinAs. In this work we look at the task of computing all the MinAs by means of modified decision procedures. We first show that tableaux- and automata-based decision procedures can be transformed into pinpointing algorithms that output a (compact) representation of the set of all MinAs. We then explore the complexity of the problem.

Description Logics (DLs) are a family of logic-based knowledge representation (KR) formalisms designed to represent and reason about static conceptual knowledge in a semantically well-understood way. On the other hand, standard action formalisms are KR formalisms based on classical logic designed to model and reason about dynamic systems. The largest part of the present work is dedicated to integrating DLs with action formalisms, with the main goal of obtaining decidable action formalisms with an expressiveness significantly beyond propositional. To this end, we offer DL-tailored solutions to the frame and ramification problem. One of the main technical results is that standard reasoning problems about actions (executability and projection), as well as the plan existence problem are decidable if one restricts the logic for describing action pre- and post-conditions and the state of the world to decidable Description Logics. A smaller part of the work is related to decidable extensions of Description Logics with concrete datatypes, most importantly with those allowing to refer to the notions of space and time.

Computing the least common subsumer (lcs) is an inference task that can be used to support the \bottom-up' construction of knowledge bases for KR systems based on description logics. Previous work on how to compute the lcs has concentrated on description logics that allow for universal value restrictions, but not for existential restrictions. The main new contribution of this paper is the treatment of description logics with existential restrictions. More precisely, we show that, for the description logic ALE (which allows for conjunction, universal value restrictions, existential restrictions, negation of atomic concepts, as well as the top and the bottom concept), the lcs always exists and can efiectively be computed. Our approach for computing the lcs is based on an appropriate representation of concept descriptions by certain trees, and a characterization of subsumption by homomorphisms between these trees. The lcs operation then corresponds to the product operation on trees. / An abridged version of this technical report is published in the Proceedings of IJCAI'99.

Unification of concept terms is a new kind of inference problem for Description Logics, which extends the equivalence problem by allowing to replace certain concept names by concept terms before testing for equivalence. We show that this inference problem is of interest for applications, and present first decidability and complexity results for a small concept description language. / This revised version of LTCS-Report 97-02 provides a stronger complexity result in Section 6. An abridged version will appear in Proc. ECAI'98 .

We live in an era of data and information, where an immeasurable amount of discoveries, findings, events, news, and transactions are generated every second. Governments, companies, or individuals have to employ and process all that data for knowledge-based decision-making (i.e. a decision-making process that uses predetermined criteria to measure and ensure the optimal outcome for a specific topic), which then prompt them to view the knowledge as valuable resource. In this knowledge-based view, the capability to create and utilize knowledge is the key source of an organization or individual’s competitive advantage. This dynamic nature of knowledge leads us to the study of belief revision (or belief change), an area which emerged from work in philosophy and then impacted further developments in computer science and artificial intelligence. In belief revision area, the AGM postulates by Alchourrón, Gärdenfors, and Makinson continue to represent a cornerstone in research related to belief change. Katsuno and Mendelzon (K&M) adopted the AGM postulates for changing belief bases and characterized AGM belief base revision in propositional logic over finite signatures. In this thesis, two research directions are considered. In the first, by considering the semantic point of view, we generalize K&M’s approach to the setting of (multiple) base revision in arbitrary Tarskian logics, covering all logics with a classical model-theoretic semantics and hence a wide variety of logics used in knowledge representation and beyond. Our generic formulation applies to various notions of “base”, such as belief sets, arbitrary or finite sets of sentences, or single sentences. The core result is a representation theorem showing a two-way correspondence between AGM base revision operators and certain “assignments”: functions mapping belief bases to total — yet not transitive — “preference” relations between interpretations. Alongside, we present a companion result for the case when the AGM postulate of syntax-independence is abandoned. We also provide a characterization of all logics for which our result can be strengthened to assignments producing transitive preference relations (as in K&M’s original work), giving rise to two more representation theorems for such logics, according to syntax dependence vs. independence. The second research direction in this thesis explores two approaches for revising description logic knowledge bases under fixed-domain semantics, namely model-based approach and individual-based approach. In this logical setting, models of the knowledge bases can be enumerated and can be computed to produce the revision result, semantically. We show a characterization of the AGM revision operator for this logic and present a concrete model-based revision approach via distance between interpretations. In addition, by weakening the KB based on certain domain elements, a novel individual-based revision operator is provided as an alternative approach.

Cette thèse aborde l'aide du pronostic basée sur l'image et les ontologies médicales, en utilisant la représentation des connaissances et le raisonnement pour les très grandes images microscopiques. Une application médicale particulière dans laquelle une assistance de type pronostic est nécessaire est la graduation du cancer du sein. Même si cela est considéré comme un outil d'évaluation essentiel dans la pratique de pathologie moderne, les principaux problèmes posés par la procédure manuelle de pronostic sont : la nécessité des connaissances, attention et temps. D'autre part, le manque de représentation sémantique formelle standardisée pour aider l'indexation et la classification de la terminologie, ainsi que l'utilisation d'un mécanisme d'inférence pour assister la graduation représentent des problématiques clé du domaine. Dans ce sens, cette étude propose une représentation formelle qualitative pour la graduation du cancer du sein ainsi qu'une ontologie d'application Breast Cancer Grading Ontology (BCGO) pour décrire les connaissances d'une manière cohérente. Une autre question que nous adressons en proposant l'ontologie est le fossé sémantique entre les concepts sémantiques de haut niveau et les caractéristiques de l'image de bas niveau. En plus, nous proposons un soutien de théorie spatiale pour la représentation des relations spatiales entre les concepts spécifiques à la graduation du cancer du sein. L'ontologie BCGO est intégré dans une plateforme microscopique cognitif virtuelle MICO pour l'exploration visuelle, l'indexation et l'extraction sémantique de l'image microscopique.

As Lógicas de Descrição são usadas como base para a linguagem OWL, padrão para representação de ontologias na web. No entanto, conhecimento não é estático e, com tal dinamismo, o estudo de revisão de crenças e sua correta e adequada aplicação tornam-se muito importantes. Neste trabalho, pretendemos desenvolver uma ferramenta para revisão de ontologias como um plug-in para o Protégé, o editor de ontologias mais utilizado atualmente. / Description Logics are the basis for OWL language, which is the standard to represent ontologies on the web. However, knowledge is usually not satic and its dynamics brings the importance of belief revision and its correct and proper application. In this work, we intend to develop a tool for revising ontologies as a plug-in for Protégé, the most used ontology editor nowadays.

A area de revisão de crenças estuda como agentes racionais mudam suas crencas ao receberem novas informações. O marco da area de revisão de crenças foi a publicacão do trabalho de Alchourron, Gardenfors e Makinson. Nesse trabalho conhecido como paradigma AGM foram denidos criterios de racionalidade para tipos de mudanca de crencas. Desde então, a área de revisão de crenças foi influenciada por diversas disciplinas como filosoa, computacão e direito. Paralelamente ao desenvolvimento da area de revisão de crenças, os últimos 20 anos foram marcados por um grande avanço no estudo das logicas de descrição. Tal avanço, impulsionado pelo desenvolvimento da web-semântica, levou a adoção de linguagens inspiradas em logicas de descrição (OWL) como padrão para se representar ontologias na web. Nessa tese tratamos do problema de aplicar a teoria da revisão de crenças a lógicas não clássicas e especialmente a logicas de descric~ao. Trabalhos recentes mostraram que o paradigma AGM e incompatvel com diversas logicas de descricão. Estendemos esses resultados mostrando outras lógicas que não são compatíveis com o paradigma AGM. Propomos formas de aplicar a teoria de revisão tanto em bases quanto em conjuntos de crencas a essas logicas. Alem disso, usamos algoritmos conhecidos da área de depuração de ontologias para implementar operações em bases de crenças. / Belief revision theory studies how rational agents change their beliefs after receiving new information. The most in uential work in this area is the paper of Alchourron, Gardenfors and Makinson. In this work, known as AGM paradigm rationality criteria for belief change were dened. Since then, the eld has been in uenced by many areas like philosophy, computer science and law. Parallel to the development of belief revision eld, in the past 20 years there was a huge grow in the study of description logics. The climax of this development was the adoption of OWL (a language based on description logics) as the standard language to represent ontologies on the web. In this work we deal with the problem of applying belief revision in to non-classical logics, specially description logics. Recent works showed that the AGM paradigm is not compliant with several description logics. We have extended this work by showing that other logics are not compliant with AGM paradigm. Furthermore, we propose alternative ways to apply belief revision techniques to these logics. Finally, we show that well known algorithms from the area of ontology debugging eld can be used to implement the proposed constructions.

In computer science, an ontology is a machine-processable representation of knowledge about some domain. Ontologies are encoded in ontology languages, such as the Web Ontology Language (OWL) based on Description Logics (DLs). An ontology is a set of logical statements, called axioms. Some axioms make universal statements, e.g. all fathers are men, while others record data, i.e. facts about specific individuals, e.g. Bob is a father. A set of universal statements is called TBox, as it encodes terminology, i.e. schema-level conceptual relationships, and a set of facts is called ABox, as it encodes instance-level assertions. Ontologies are extensively developed and widely used in domains such as biology and medicine. Manual engineering of a TBox is a difficult task that includes modelling conceptual relationships of the domain and encoding those relationships in the ontology language, e.g. OWL. Hence, it requires the knowledge of domain experts and skills of ontology engineers combined together. In order to assist engineering of TBoxes and potentially automate it, acquisition (or induction) of axioms from data has attracted research attention and is usually called Ontology Learning (OL). This thesis investigates the problem of OL from general principles. We formulate it as General Terminology Induction that aims at acquiring general, expressive TBox axioms (called general terminology) from data. The thesis addresses and investigates in depth two main questions: how to rigorously evaluate the quality of general TBox axioms and how to efficiently construct them. We design an approach for General Terminology Induction and implement it in an algorithm called DL-Miner. We extensively evaluate DL-Miner, compare it with other approaches, and run case studies together with domain experts to gain insight into its potential applications. The thesis should be of interest to ontology developers seeking automated means to facilitate building or enriching ontologies. In addition, as our experiments show, DL-Miner can deliver valuable insights into the data, i.e. can be useful for data analysis and debugging.