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In this tutorial, we learn how to set up and exploit the virtual knowledge graph (VKG) approach to access data stored in relational legacy systems and to enrich such data with domain knowledge coming from different heterogeneous (biomedical) resources. The VKG approach is based on an ontology that describes a domain of interest in terms of a vocabulary familiar to the user and exposes a high-level conceptual view of the data. Users can access the data by exploiting the conceptual view, and in this way they do not need to be aware of low-level storage details. They can easily integrate ontologies coming from different sources and can obtain richer answers thanks to the interaction between data and domain knowledge.

Predictive analysis gradually gains importance in industry. For instance, service engineers at Siemens diagnostic centres unveil hidden knowledge in huge amounts of historical sensor data and use it to improve the predictive systems analysing live data. Currently, the analysis is usually done using data-dependent rules that are specific to individual sensors and equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers. One solution to this problem is to employ ontology-based data access (OBDA), which provides a conceptual view of data via an ontology. However, classical OBDA systems do not support access to temporal data and reasoning over it. To address this issue, we propose a framework for temporal OBDA. In this framework, we use extended mapping languages to extract information about temporal events in the RDF format, classical ontology and rule languages to reflect static information, as well as a temporal rule language to describe events. We also propose a SPARQL-based query language for retrieving temporal information and, finally, an architecture of system implementation extending the state-of-the-art OBDA platform Ontop.

OPTIONAL is a key feature in SPARQL for dealing with missing information. While this operator is used extensively, it is also known for its complexity, which can make efficient evaluation of queries with OPTIONAL challenging. We tackle this problem in the Ontology-Based Data Access (OBDA) setting, where the data is stored in a SQL relational database and exposed as a virtual RDF graph by means of an R2RML mapping. We start with a succinct translation of a SPARQL fragment into SQL. It fully respects bag semantics and three-valued logic and relies on the extensive use of the LEFT JOIN operator and COALESCE function. We then propose optimisation techniques for reducing the size and improving the structure of generated SQL queries. Our optimisations capture interactions between JOIN, LEFT JOIN, COALESCE and integrity constraints such as attribute nullability, uniqueness and foreign key constraints. Finally, we empirically verify effectiveness of our techniques on the BSBM OBDA benchmark.

In this paper we extend virtual OBDA to more expressive ontology languages than that of DL-LiteR, which is the logic underpinning the W3C ontology language OWL 2. We achieve this by relying on two well-known mechanisms, namely conservative rewriting and approximation, and compiling some of the domain semantics into OBDA mappings.

We study the extension of relational multiagent systems (RMASs), where agents manipulate full-fledged relational databases, with data types and facets equipped with domain-specific, rigid relations (such as total orders). Specifically, we focus on design-time verification of RMASs against rich first-order temporal properties expressed in a variant of first-order mu-calculus with quantification across states. We build on previous decidability results under the state-bounded assumption, i.e., in each single state only a bounded number of data objects is stored in the agent databases, while unboundedly many can be encountered over time. We recast this condition, showing decidability in presence of dense, linear orders, and facets defined on top of them. Our approach is based on the construction of a finite-state, sound and complete abstraction of the original system, in which dense linear orders are reformulated as non-rigid relations working on the active domain of the system only. We also show undecidability when including a data type equipped with the successor relation.

In recent years more and more often, we hear about Big Data and issues related to the management of these huge volume of data, and considering that there is no definitive solution for their storage, querying, analysis. This discipline is in great evolution, multidisciplinary and very complex to be dominated for the several aspects to be addressed: architectural, data structure, data management, data analytics, protection and security, computational as parallel and distributed processing, etc. To cope with these problems a basic survey of main tips, which would allow the developer to orient themselves in the choice of the best solution, for the development of an architecture for the management of Big Data, in each specific case could be of great support and help. In fact a large number of fields, from industry to scientific research, are inquiring information and hints about what can be obtained through the analysis of Big Data and how these infrastructures can be created, by using what, etc. And, therefore, more powerful solutions are needed to cope with increasing complexity and variety of problems, for their better management and exploitation of the open accessible, produced and integrated data. In this paper, starting from the analysis of the existing solutions, particularly interesting and well documented use cases, we identified a group of main differentiating features which can mainly influence the choice of the solution to be set up; then we looked at different types of existing solutions and products to see how they are handled the identified features. Lastly, the results obtained with the analysis of the desirable features for each main domain and then the identification of the most suitable and/or adopted products for the application domains. The work cannot be exhaustive, and in many cases we had to decide to include and to exclude aspects and tools. The obtained results can be regarded as a model and main guidelines for big data solution navigation.

Knowledge base exchange can be considered as a generalization of data exchange in which the aim is to exchange between a source and a target connected through mappings, not only explicit knowledge, i.e., data, but also implicit knowledge in the form of axioms. Such problem has been investigated recently using Description Logics (DLs) as representation formalism, thus assuming that the source and target KBs are given as a DL TBox+ABox, while the mappings have the form of DL TBox assertions. In this paper we are interested in the problem of representing a given source TBox by means of a target TBox that captures at best the intensional information in the source. In previous work, results on representability have been obtained for DL-LiteRDFS , a DL corresponding to the FOL fragment of RDFS. We extend these results to the positive fragment of DL-LiteR, in which, differently from DL-LiteRDFS , the assertions in the TBox and the mappings may introduce existentially implied individuals. For this we need to overcome the challenge that the chase, a key notion in data and knowledge base exchange, is not guaranteed anymore to be finite.

The study of node-selection query languages for (finite) trees has been a major topic in the recent research on query lan- guages for Web documents. On one hand, there has been an extensive study of XPath and its various extensions. On the other hand, query languages based on classical logics, such as first-order logic (FO) or monadic second-order logic (MSO), have been considered. Results in this area typically relate an Xpath-based language to a classical logic. What has yet to emerge is an XPath-related language that is expressive as MSO, and at the same time enjoys the computational proper- ties of XPath, which are linear query evaluation and exponen- tial query-containment test. In this paper we propose μXPath, which is the alternation-free fragment of XPath extended with fixpoint operators. Using two-way alternating automata, we show that this language does combine desired expressiveness and computational properties, placing it as an attractive can- didate as the definite query language for trees.

We study the problem of instance level (ABox) updates for Knowledge Bases (KBs) represented in Description Logics of the DL-Lite family. DL-Lite is at the basis of OWL 2 QL, one of the tractable fragments of OWL 2, the recently proposed revision of the Web Ontology Language. We examine known works on updates that follow the model-based approach and discuss their drawbacks. Specifically, the fact that model-based approaches intrinsically ignore the structural properties of KBs, leads to undesired properties of updates computed according to such semantics. Hence, we propose two novel formula-based approaches, and for each of them we develop a polynomial time algorithm to compute ABox updates for the Description Logic DL-LiteFR.