Although a number of initiatives provide personalized context-aware guidance for niche use-cases, a standard framework for context awareness remains lacking. This article explains how semantic technology has been exploited to generate a centralized repository of personal activity context. This data drives advanced features such as, personal situation recognition and customizable rules for the context-sensitive management of personal devices and data sharing. As a proof-of-concept, we demonstrate how an innovative context-aware system has successfully adopted such an infrastructure.

However, what often matters for experience is the user's place A semantic model of user-centered places, the Platys ontology enables the mapping Research in context-aware computing (Schilit, Adams, of positions to places. In the model, places and and Want 1994) aims to enable computing systems that activities can be represented at different levels of acquire and maintain context data and use it to adapt granularity using subsumption hierarchies. It originated with Weiser's vision of to determine a user's place at any given time. Place ubiquitous computing (Weiser 1999) where human recognition has been addressed with standard activities are enhanced with devices that are all around machine-learning classifiers as well as a semisupervised but unnoticeable to the user and that provide services expectation-maximization algorithm. The that adapt to the circumstances in which they are used. Location is an 1994; Schilit et al. 1993) are early works in contextaware essential part of place and therefore place recognition computing and dealt with tracking a user's location relies on location sensing. Since frequent location and using it to provide better services or sharing it sensing by a mobile device depletes power, we have with others.

Although a number of initiatives provide personalized context-aware guidance for niche use-cases, a standard framework for context awareness remains lacking. This article explains how semantic technology has been exploited to generate a centralized repository of personal activity context. This data drives advanced features such as, personal situation recognition and customizable rules for the context-sensitive management of personal devices and data sharing. As a proof-of-concept, we demonstrate how an innovative context-aware system has successfully adopted such an infrastructure.

SPARQL is the de facto language for querying RDF data, since its standardization in 2008. A new version, called SPARQL 1.1, was released in 2013, with the aim of enriching the 2008 language with reasoning capabilities to deal with RDFS and OWL vocabularies, and a mechanism to express navigation patterns through regular expressions. However, SPARQL 1.1 is not powerful enough for expressing some relevant navigation patterns, and it misses a general form of recursion. In this work, we focus on OWL 2 QL and we propose TriQ-Lite 1.0, a tractable rule-based formalism that supports the above functionalities, and thus it can be used for querying RDF data. Unlike existing composite approaches, our formalism has simple syntax and semantics in the same spirit as good old Datalog.

The last years have seen an exponential increase in the interest for the development of methods for combining probability with Description Logics (DLs). These methods are very useful to model real world domains, where incompleteness and uncertainty are common. This combination has become a fundamental component of the Semantic Web.Our work started with the development of a probabilistic semantics for DL, called DISPONTE, that applies the distribution semantics to DLs. Under DISPONTE we annotate axioms of a theory with a probability, that can be interpreted as the degree of our belief in the corresponding axiom, and we assume that each axiom is independent of the others. Several algorithms have been proposed for supporting the development of the Semantic Web. Efficient DL reasoners, such us Pellet, are able to extract implicit information from the modeled ontologies. Despite the availability of many DL reasoners, the number of probabilistic reasoners is quite small. We developed BUNDLE, a reasoner based on Pellet that allows to compute the probability of queries. BUNDLE, like most DL reasoners, exploits an imperative language for implementing its reasoning algorithm. Nonetheless, usually reasoning algorithms use non-deterministic operators for doing inference. One of the most used approaches for doing reasoning is the tableau algorithm which applies a set of consistency preserving expansion rules to an ABox, but some of these rules are non-deterministic.In order to manage this non-determinism, we developed the system TRILL which performs inference over DISPONTE DLs. It implements the tableau algorithm in the declarative Prolog language, whose search strategy is exploited for taking into account the non-determinism of the reasoning process. Moreover, we developed a second version of TRILL, called TRILL^P, which implements some optimizations for reducing the running time. The parameters of probabilistic KBs are difficult to set. It is thus necessary to develop systems which automatically learn this parameters starting from the information available in the KB. We presented EDGE that learns the parameters of a DISPONTE KB, and LEAP, that learn the structure together with the parameters of a DISPONTE KB. The main objective is to apply the developed algorithms to Big Data. Nonetheless, the size of the data requires the implementation of algorithms able to handle it. It is thus necessary to exploit approaches based on the parallelization and on cloud computing. Nowadays, we are working to improve EDGE and LEAP in order to parallelize them.

Static analysis is a core task in query optimization and knowledge base verification. We study static analysis techniques for SPARQL, the standard language for querying Semantic Web data. Specifically, we investigate the query containment problem and query-update independence analysis. We are interested in developing techniques through reductions to the validity problem in logic.

Modeling real world domains requires ever more frequently to represent uncertain information. The DISPONTE semantics for probabilistic description logics allows to annotate axioms of a knowledge base with a value that represents their probability. In this paper we discuss approaches for performing inference from probabilistic ontologies following the DISPONTE semantics. We present the algorithm BUNDLE for computing the probability of queries. BUNDLE exploits an underlying Description Logic reasoner, such as Pellet, in order to find explanations for a query. These are then encoded in a Binary Decision Diagram that is used for computing the probability of the query.

The standard way of answering queries over incomplete databases is to compute certain answers, defined as the intersection of query answers on all complete databases that the incomplete database represents. But is this universally accepted definition correct? We argue that this ``one-size-fits-all'' definition can often lead to counterintuitive or just plain wrong results, and propose an alternative framework for defining certain answers. We combine three previously used approaches, based on the semantics and representation systems, on ordering incomplete databases in terms of their informativeness, and on viewing databases as knowledge expressed in a logical language, to come up with a well justified and principled notion of certain answers. Using it, we show that for queries satisfying some natural conditions (like not losing information if a more informative input is given), computing certain answers is surprisingly easy, and avoids the complexity issues that have been associated with the classical definition.

Classic entrenchment-based contraction is not applicable to many useful logics, such as description logics. This is because the semantic construction refers to arbitrary disjunctions of formulas, while many logics do not fully support disjunction. In this paper, we present a new entrenchment-based contraction which does not rely on any logical connectives except conjunction. This contraction is applicable to all fragments of first-order logic that support conjunction. We provide a representation theorem for the contraction which shows that it satisfies all the AGM postulates except for the controversial Recovery Postulate, and is a natural generalisation of entrenchment-based contraction.

Materialisation precomputes all consequences of a set of facts and a datalog program so that queries can be evaluated directly (i.e., independently from the program). Rewriting optimises materialisation for datalog programs with equality by replacing all equal constants with a single representative; and incremental maintenance algorithms can efficiently update a materialisation for small changes in the input facts. Both techniques are critical to practical applicability of datalog systems; however, we are unaware of an approach that combines rewriting and incremental maintenance. In this paper we present the first such combination, and we show empirically that it can speed up updates by several orders of magnitude compared to using either rewriting or incremental maintenance in isolation.