1,721,103 research outputs found
Challenges, approaches and solutions in data integration for research and innovation
No full textIn order to be implemented by policy makers, science, technology, and innovation (science, technology, and innovation (STI)) policies and indicator building need data. Whenever we need data, we need a method for data management, and in the era of big data big data, a crucial role is played by data integration big dataintegration. Therefore, STI policies and indicator development need data integration. Two main approaches to data integration exist, namely procedural and declarative. In this chapter, we follow the latter approach and focus our attention on the ontology-based data integration (ontology-based dataintegration (OBDI)) paradigm. The main principles of OBDI are: (i)Leave the data where they are.(ii)Build a conceptual specification of the domain of interest (ontology), in terms of knowledge structures.(iii)Map such knowledge structures to concrete data sources.(iv)Express all services over the abstract representation.(v)Automatically translate knowledge services to data services. We introduce the main challenges of data integration for research and innovation (researchand innovation (R&I)) and show that reasoning over an ontology connected to data may be very helpful for the study of R&I. We also provide examples by using Sapientia, an ontology specifically defined for multidimensional research assessment
Editorial: Special Issue on Quality Aspects of Data Preparation
Full text linkThis Special Issue of the Journal of Data and Information Quality (JDIQ) contains novel theoretical and methodological contributions as well as state-of-the-art reviews and research perspectives on quality aspects of data preparation. In this editorial, we summarize the scope of the issue and briefly describe its content
Exploiting ontologies for explaining data sources semantics
No full textWe study the problem of associating formal semantic descriptions to data services. We base our proposal on the Ontology-Based Data Access paradigm, where a domain ontology is used to provide a semantic layer mapped to the data sources of an organization. The basic idea is to explain the semantics of a data service in terms of a query over the ontology. We illustrate a formal framework for this problem, based on the notion of source-to-ontology rewriting, which comes in three variants, called sound, complete and perfect, respectively. We present a thorough complexity analysis of two computational problems, namely verification (checking whether a query is a rewriting of a given data service), and computation (computing a rewriting of a data service)
Metamodeling and metaquerying in OWL 2 QL
Full text linkOWL 2 QL is a standard profile of the OWL 2 ontology language, specifically tailored to Ontology-Based Data Management. Inspired by recent work on higher-order Description Logics, in this paper we present a new semantics for OWL 2 QL ontologies, called Metamodeling Semantics (MS), and show that, in contrast to the official Direct Semantics (DS) for OWL 2, it allows exploiting the metamodeling capabilities natively offered by the OWL 2 punning. We then extend unions of conjunctive queries with both metavariables, and the possibility of using TBox atoms, with the purpose of expressing meaningful metalevel queries. We first show that under MS both satisfiability checking and answering queries including only ABox atoms, have the same complexity as under DS. Second, we investigate the problem of answering general metaqueries, and single out a new source of complexity coming from the combined presence of a specific type of incompleteness in the ontology, and of TBox axioms among the query atoms. Then we focus on a specific class of ontologies, called TBox-complete, where there is no incompleteness in the TBox axioms, and show that general metaquery answering in this case has again the same complexity as under DS. Finally, we move to general ontologies and show that answering general metaqueries is coNP-complete with respect to ontology complexity, Π2p-complete with respect to combined complexity, and remains AC0 with respect to ABox complexity
The notion of Abstraction in Ontology-based Data Management
Full text linkWe study a novel reasoning task in Ontology-based Data Management (OBDM), called Abstraction, which aims at associating formal semantic descriptions to data services. In OBDM a domain ontology is used to provide a semantic layer mapped to the data sources of an organization. The basic idea of the work presented in this paper is to explain the semantics of a data service in terms of a query over the ontology. We illustrate a formal framework for this problem, based on three different notions of abstraction, called sound, complete, and perfect, respectively. We present a thorough complexity analysis of two computational problems, namely verification (checking whether a query is an abstraction of a given data service), and computation (computing an abstraction of a given data service)
Conceptual language for statistical data modeling
No full textWe describe a new language for statistical data modeling. The language offers a general framework for the representation of elementary and summary data, and has three main characteristics: (i) the types of modeling primitives it provides are particularly suited for representing objects from a statistical point of view; (ii) it includes a rich set of structuring mechanisms for both elementary and summary data, which are given a formal semantics by means of logic; (iii) it is equipped with specialized inference procedures, allowing to perform different kinds of checks on the representation. The language is intended to be used during the specification phase of a statistical database, which we consider a knowledge-driven activity, where the availability of both powerful structuring mechanisms and suitable reasoning techniques constitute a valuable tool to the designer. The main focus of this paper is on the formal foundation of our approach. We describe the syntax and the semantics of the language, and we discuss its use in statistical data modeling. Also, we describe the basis for devising inference techniques for our language. Such techniques are based on an interesting correspondence between the language and propositional dynamic logic
INCOD: A System for Interactive Conceptual Data Base Design
No full textA description is given of the general structure of a computer aided system for Interactive Conceptual Design (INCOD) of Data Bases. First of all, motivations are given for a system as INCOD that allows the incremental definition of a conceptual schema of a Data Base according both to a top-down and a bottom-up strategy. Afterward, the general features, functions and architecture of the system are described
- …
