Preface: Semantic Web technologies for mobile and pervasive environments

Artificial Intelligence provides a rich set of methods and tools for implementing the Ambient Intelligence vision, i.e. to transform our environments into smart spaces assisting as with our everyday tasks in an intelligent, seamless and non-obtrusive way. Among them, Semantic Web technologies, such as RDF, ontology languages and others, can be used to address several of the challenges that come with this vision, mainly with respect to modelling, sharing and reasoning with context information. This thematic issue demonstrates their capabilities by presenting three different Semantic Web-based solutions for mobile and computing environments

Ontology-Based Data Access Using Rewriting, OWL 2 RL Systems and Repairing

Abstract. In previous work it has been shown how an OWL 2 DL on-tology O can be `repaired ' for an OWL 2 RL system ans|that is, how we can compute a set of axioms R that is independent from the data and such that ans that is generally incomplete for O becomes complete for all SPARQL queries when used with O [ R. However, the initial implementation and experiments were very preliminary and hence it is currently unclear whether the approach can be applied to large and com-plex ontologies. Moreover, the approach so far can only support instance queries. In the current paper we thoroughly investigate repairing as an approach to scalable (and complete) ontology-based data access. First, we present several non-trivial optimisations to the rst prototype. Sec-ond, we show how (arbitrary) conjunctive queries can be supported by integrating well-known query rewriting techniques with OWL 2 RL sys-tems via repairing. Third, we perform an extensive experimental evalua-tion obtaining encouraging results. In more detail, our results show that we can compute repairs even for very large real-world ontologies in a rea-sonable amount of time, that the performance overhead introduced by repairing is negligible in small to medium sized ontologies and noticeable but manageable in large and complex one, and that the hybrid reasoning approach can very eciently compute the correct answers for real-world challenging scenarios.

LARD: Large-scale Artificial Disfluency Generation

Disfluency detection is a critical task in real-time dialogue systems. However, despite its importance, it remains a relatively unexplored field, mainly due to the lack of appropriate datasets. At the same time, existing datasets suffer from various issues, including class imbalance issues, which can significantly affect the performance of the model on rare classes, as it is demonstrated in this paper. To this end, we propose LARD, a method for generating complex and realistic artificial disfluencies with little effort. The proposed method can handle three of the most common types of disfluencies: repetitions, replacements and restarts. In addition, we release a new large-scale dataset with disfluencies that can be used on four different tasks: disfluency detection, classification, extraction and correction. Experimental results on the LARD dataset demonstrate that the data produced by the proposed method can be effectively used for detecting and removing disfluencies, while also addressing limitations of existing datasets.Comment: Accepted at LREC 202

Repairing Ontologies for Incomplete Reasoners

Abstract. The need for scalable query answering often forces Semantic Web applications to use incomplete OWL 2 reasoners, which in some cases fail to derive all answers to a query. This is clearly undesirable, and in some applications may even be unacceptable. To address this problem, we investigate the problem of ‘repairing ’ an ontology T —that is, computing an ontology R such that a reasoner that is incomplete for T becomes complete when used with T ∪R. We identify conditions on T and the reasoner that make this possible, present a practical algorithm for computing R, and present a preliminary evaluation which shows that, in some realistic cases, repairs are feasible to compute, reasonable in size, and do not significantly affect reasoner performance.

Towards an ontology-driven adaptive dialogue framework

In this paper, we describe the principles and technologies that underpin the development of an adaptive dialogue manager framework, tailored to carrying out human-agent conversations in a natural, robust and exible manner. Our research focus is twofold. First, the investigation of dialogue strategies that can handle dynamically created user and system actions, while still enabling the agent to adapt its actions to various and possibly changing contexts. Second, the utilisation of rich semantic annotations for capturing background knowledge, as well as conversation topics and semantics of user utterances extracted through language analysis. The resulting annotations comprise the situational descriptions upon which reasoning takes place to recognise the conversation context and compile appropriate responses

Operational semantics of an extension of ODRL able to express obligations

Nowadays economy is every day more and more a digital economy where many human activities are performed by means of digital devices. Those digital activities produce and operate on a big amount of digital assets, as the data stored in datasets, documents, images, videos or audio files. Rationally, it is useless that digital assets are made public without the specification of constrains on their usage and access. Many formal languages for expressing licenses, policies, norms, agreements, and contracts have been proposed in literature. Among them, the Open Digital Rights Language (ODRL) is a quite general one. In this paper, we present an extension of the syntax of ODRL for expressing conditional obligations. We present also an operational semantics of this extension with the goal of being able to perform automatic reasoning on the dynamic evolution in time of obligations. The definition of such operational semantics will be based on the specification of the lifecycle of obligations and on the definition of the mechanisms for computing their state using automatic reasoning. In particular, for doing that we use as far as possible, W3C standards: RDF and RDF Schema for the specification of obligations, and the Apache Jena general purpose rule engine for efficiently deducing the state of obligations on the bases of the state of the interaction among agents