We develop a framework for forgetting concepts and roles (aka uniform interpolation) in terminologies in the lightweight description logic EL extended with role inclusions and domain and range restrictions. Three different notions of forgetting, preserving, respectively, concept inclusions, concept instances, and answers to conjunctive queries, with corresponding languages for uniform interpolants are investigated. Experiments based on SNOMED CT (Systematised Nomenclature of

Abstract. We propose a general method and novel algorithmic techniques to facilitate the integration of independently developed ontologies using mappings. Our method and techniques aim at helping users understand and evaluate the semantic consequences of the integration, as well as to detect and fix potential errors. We also present ContentMap, a system that implements our approach, and a preliminary evaluation which suggests that our approach is both useful and feasible in practice. 1

We present a formal framework for (minimal) module extraction based on an abstract notion of inseparability w.r.t. a signature between ontologies. Two instances of this framework are discussed in detail for DL-Lite ontologies: concept inseparability, when ontologies imply the same complex concept inclusions over the signature, and query inseparability, when they give the same answers to existential queries for any instance data over the signature. We demonstrate that different types of corresponding minimal modules for these inseparability relations can be automatically extracted from large-scale DL-Lite ontologies by composing the tractable syntactic locality-based module extraction algorithm with intractable extraction algorithms using the multi-engine QBF solver AQME. The extracted minimal modules are compared with those obtained using non-logic-based approaches.

Abstract. Ontologies provide a conceptualization of a domain of interest. Nowadays, they are typically represented in terms of Description Logics (DLs), and are seen as the key technology used to describe the semantics of information at various sites. The idea of using ontologies as a conceptual view over data repositories is becoming more and more popular, but for it to become widespread in standard applications, it is fundamental that the conceptual layer through which the underlying data layer is accessed does not introduce a significant overhead in dealing with the data. Based on these observations, in recent years a family of DLs, called DL-Lite, has been proposed, which is specifically tailored to capture basic ontology and conceptual data modeling languages, while keeping low complexity of reasoning and of answering complex queries, in particular when the complexity is measured w.r.t. the size of the data. In this article, we present a detailed account of the major results that have been achieved for the DL-Lite family. Specifically, we concentrate on DL-LiteA,id, an expressive member of this family, present algorithms for reasoning and query answering over DL-LiteA,id ontologies,

Summary. Modularity of ontologies is currently an active research field, and many different notions of a module have been proposed. In this paper, we review the fundamental principles of modularity and identify formal properties that a robust notion of modularity should satisfy. We explore these properties in detail in the contexts of description logic and classical predicate logic and put them into the perspective of well-known concepts from logic and modular software specification such as interpolation, forgetting and uniform interpolation. We also discuss reasoning problems related to modularity. 1

Abstract. Forgetting is an important tool for reducing ontologies by eliminating some concepts and roles while preserving sound and complete reasoning. Attempts have previously been made to address the problem of forgetting in relatively simple description logics (DLs) such as DL-Lite and extended EL. The ontologies used in these attempts were mostly restricted to TBoxes rather than general knowledge bases (KBs). However, the issue of forgetting for general KBs in more expressive description logics, such as ALC and OWL DL, is largely unexplored. In particular, the problem of characterizing and computing forgetting for such logics is still open. In this paper, we first define semantic forgetting about concepts and roles in ALC ontologies and state several important properties of forgetting in this setting. We then define the result of forgetting for concept descriptions in ALC, state the properties of forgetting for concept descriptions, and present algorithms for computing the result of forgetting for concept descriptions. Unlike the case of DL-Lite, the result of forgetting for an ALC ontology does not exist in general, even for the special case of concept forgetting. This makes the problem of how to compute forgetting in ALC more challenging. We address this problem by defining a series of approximations to the result of forgetting for ALC ontologies and studying their properties and their application to reasoning tasks. We use the algorithms for computing forgetting for concept descriptions to compute these approximations. Our algorithms for computing approximations can be embedded into an ontology editor to enhance its ability to manage and reason in (large) ontologies. 1

OWL Ontologies are already being used in many application domains. In particular, OWL is extensively used in the clinical sciences; prominent examples of OWL ontologies are the National Cancer Institute (NCI) Thesaurus, SNOMED CT, the Gene Ontology (GO), the Foundational Model of Anatomy (FMA), and GALEN. These ontologies are large and complex; for example, SNOMED currently describes more than 350.000 concepts whereas NCI and GALEN describe around 50.000 concepts. Furthermore, these ontologies are in continuous evolution; for example the developers of NCI and GO perform approximately 350 additions of new entities and 25 deletions of obsolete entities each month [1]. Most realistic ontologies, including the ones just mentioned, are being developed collaboratively. The developers of an ontology can be geographically distributed and may contribute in different ways and to different extents. Maintaining such large ontologies in a collaborative way is a highly complex process, which involves tracking and managing the frequent changes to the ontology, reconciling conflicting views of the domain from different developers, minimising the introduction of errors (e.g., ensuring

There are various techniques for specifying a module of an ontology that covers all knowledge about a given set of terms. These differ with respect to the size of the module, the complexity of its computation, and certain robustness properties. In this paper, we survey existing logic-based approaches, focus on syntactic approximations, and compare different kinds of modules with respect to their properties. This is intended to give guidelines on how to choose “the right kind of module”.

We develop a formal framework for comparing different versions of DL-Lite ontologies. The main feature of our approach is that we take into account the vocabulary ( = signature) with respect to which one wants to compare ontologies. Five variants of difference and inseparability relations between ontologies are introduced and their respective applications for ontology development and maintenance discussed. These variants are obtained by generalising the notion of conservative extension from mathematical logic and by distinguishing between differences that can be observed among concept inclusions, answers to queries over ABoxes, by taking into account additional context ontologies, and by considering a model-theoretic, language-independent notion of difference. We compare these variants, study their meta-properties, determine the computational complexity of the corresponding reasoning tasks, and present decision algorithms. Moreover, we show that checking inseparability can be automated by means of encoding into QBF satisfiability and using off-the-shelf general purpose QBF solvers. Inseparability relations between ontologies are then used to develop a formal framework for (minimal) module extraction. We demonstrate that different types of minimal modules induced by these inseparability relations can be automatically extracted from real-world medium-size DL-Lite ontologies by composing the tractable syntactic locality-based module extraction algorithm with non-tractable extraction algorithms using the multi-engine QBF solver aqme. Finally, we explore the relationship between uniform interpolation (or forgetting) and inseparability between ontologies.

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