Ontologies are not only becoming a widespread formalism to create the knowledge base of current intelligent and semantic systems, but they are also suitable for modeling context information in ubiquitous applications, which require expressive representation and reasoning languages. In this paper, we discuss different approaches for ontological context management, as well as a proposal to represent and exploit significance-based relations with standard and fuzzy ontologies.

Mobile applications can be greatly enhanced if they have information about the situation of the user. Situations may be inferred by analyzing several types of contextual information drawn from device sensors, such as location, motion, ambiance and proximity. To capture a richer understanding of users’ situations, we introduce an ontology describing the relations between background knowledge about the user and contexts inferred from sensor data. With the right combination of machine learning and semantic modeling, it is possible to create high-level interpretations of user behaviors and situations. However, the potential of understanding and interpreting behavior with such detailed granularity poses significant threats to personal privacy. We propose a framework to mitigate privacy risks by filtering sensitive data in a context-aware way, and maintain provenance of inferred situations as well as relations between existing contexts when sharing information with other parties.

This paper introduces 'just enough' principles and 'systems engineering' approach to the practice of ontology development to provide a minimal yet complete, lightweight, agile and integrated development process, supportive of stakeholder management and implementation independence.

For large scale enterprise implementations, a key problem, that has not been tackled much, is the ability to automatically map users’ requirements to reference process models. We present a tool called Process Model Requirements Gap Analyzer (ProcGap), which uses a combination of natural language processing, information retrieval and semantic reasoning to automatically match and map textual requirements to industry-specific process models. We present the results of mapping requirements from an industry project to an existing process model. We compare our approach to two previously implemented approaches and show that our approach outperforms them. In a case study, we also found that a user group with ProcGap had better performance than a user group that performed the same task manually.

Epistemic extensions of description logics (DLs) have been introduced several years ago in order to enhance expressivity and querying capabilities of these logics by knowledge base introspection. We argue that unintended effects occur when imposing the traditionally employed semantics on the very expressive DLs that underly the OWL 1 and OWL 2 standards. Consequently, we suggest a revised semantics that behaves more intuitively in these cases and coincides with the traditional semantics of less expressive DLs. Moreover, we introduce a way of answering epistemic queries to OWL knowledge bases by a reduction to standard OWL reasoning. We provide an implementation of our approach and present first evaluation results.

The research on low-complexity nonmonotonic description logics recently identified a fragment of EL with bottom, supporting defeasible inheritance with overriding, where reasoning can be carried out in polynomial time. We contribute to that framework by supporting more axiom schemata and all the concept constructors of EL++ without increasing asymptotic complexity. Moreover, we show that all the syntactic restrictions we adopt are necessary by proving several coNP-hardness results.

We investigate an extension of Description Logics (DL) with higher-order capabilities, based on Henkin-style semantics. Our study starts from the observation that the various possibilities of adding higher-order con- structs to a DL form a spectrum of increasing expres- sive power, including domain metamodeling, i.e., using concepts and roles as predicate arguments. We argue that higher-order features of this type are sufficiently rich and powerful for the modeling requirements aris- ing in many relevant situations, and therefore we carry out an investigation of the computational complexity of satisfiability and conjunctive query answering in DLs extended with such higher-order features. In particular, we show that adding domain metamodeling capabilities to SHIQ (the core of OWL 2) has no impact on the complexity of the various reasoning tasks. This is also true for DL-LiteR (the core of OWL 2 QL) under suit- able restrictions on the queries.

Ontology-based data access is a powerful form of extending database technology, where a classical extensional database (EDB) is enhanced by an ontology that generates new intensional knowledge which may contribute to answer a query. Recently, the Datalog+/- family of ontology languages was introduced; in Datalog+/-, rules are tuple-generating dependencies (TGDs), i.e., Datalog rules with the possibility of having existentially-quantified variables in the head. In this paper we introduce a novel Datalog+/- language, namely sticky sets of TGDs, which allows for a wide class of joins in the body, while enjoying at the same time a low query-answering complexity. We establish complexity results for answering conjunctive queries under sticky sets of TGDs, showing, in particular, that ontological conjunctive queries can be compiled into first-order and thus SQL queries over the given EDB instance. We also show some extensions of sticky sets of TGDs, and how functional dependencies and so-called negative constraints can be added to a sticky set of TGDs without increasing the complexity of query answering. Our language thus properly generalizes both classical database constraints and most widespread tractable description logics.

Nowadays web users have clearly expressed their wishes to receive and interact with personalized services directly. However, existing approaches, largely syntactic content-based, fail to provide robust, accurate and useful personalized services to its users. Towards such an issue, the semantic web provides technologies to annotate and match services’ descriptions with users’ features, interests and preferences, thus allowing for more efficient access to services and more generally information. The aim of our work, part of service personalization, is on automated instantiation of services which is crucial for advanced usability i.e., how to prepare and present services ready to be executed while limiting useless interactions with users? We introduce the constructive Description Logics reasoning join and couple it with concept abduction to i) identify useful parts of users profiles that satisfy services requirements and ii) compute the description required by a service to be executed but not provided by users profiles.

The OWL 2 profile OWL 2 QL, based on the DL-Lite family of description logics, is emerging as a major language for developing new ontologies and approximating the existing ones. Its main application is ontology-based data access, where ontologies are used to provide background knowledge for answering queries over data. We investigate the corresponding notion of query inseparability (or equivalence) for OWL 2 QL ontologies and show that deciding query inseparability is PSPACE-hard and in EXPTIME. We give polynomial time (incomplete) algorithms and demonstrate by experiments that they can be used for practical module extraction.