Managing data through the lens of an ontology

Ontology-based data management aims at managing data through the lens of an ontology, that is, a conceptual representation of the domain of interest in the underlying information system. This new paradigm provides several interesting features, many of which have already been proved effective in managing complex information systems. This article introduces the notion of ontology-based data management, illustrating the main ideas underlying the paradigm, and pointing out the importance of knowledge representation and automated reasoning for addressing the technical challenges it introduces

On the evolution of the instance level of DL-lite knowledge bases

Recent papers address the issue of updating the instance level of knowledge bases expressed in Description Logic following a model-based approach. One of the outcomes of these papers is that the result of updating a knowledge base K is generally not expressible in the Description Logic used to express K. In this paper we introduce a formula-based approach to this problem, by revisiting some research work on formula-based updates developed in the '80s, in particular the WIDTIO (When In Doubt, Throw It Out) approach. We show that our operator enjoys desirable properties, including that both insertions and deletions according to such operator can be expressed in the DL used for the original KB. Also, we present polynomial time algorithms for the evolution of the instance level knowledge bases expressed in the most expressive Description Logics of the DL-lite family

Epistemic Integrity Constraints for Ontology-Based Data Management

Ontology-based data management (OBDM) is a powerful knowledge-oriented paradigm for managing data spread over multiple heterogeneous sources. In OBDM, the data sources of an information system are handled through the reconciled view provided by an ontology, i.e., the conceptualization of the underlying domain of interest expressed in some formal language. In any information systems where the basic knowledge resides in data sources, it is of paramount importance to specify the acceptable states of such information. Usually, this is done via integrity constraints, i.e., requirements that the data must satisfy formally expressed in some specific language. However, while the semantics of integrity constraints are clear in the context of databases, the presence of inferred information, typical of OBDM systems, considerably complicates the matter. In this paper, we establish a novel framework for integrity constraints in the OBDM scenarios, based on the notion of knowledge state of the information system. For integrity constraints in this framework, we define a language based on epistemic logic, and study decidability and complexity of both checking satisfaction and performing different forms of static analysis on them

Non-Monotonic Ontology-based Abstractions of Data Services

In Ontology-Based Data Access (OBDA), a domain ontology is linked to the data sources of an organization in order to query, integrate and manage data through the concepts and relations of the domain of interest, thus abstracting from the structure and the implementation details of the data layer. While the great majority of contributions in OBDA in the last decade have been concerned with the issue of computing the answers of queries expressed over the ontology, recent papers address a different problem, namely the one of providing suitable abstractions of data services, i.e., characterizing or explaining the semantics of queries over the sources in terms of queries over the domain ontology. Current works on this subject are based on expressing abstractions in terms of unions of conjunctive queries (UCQs). In this paper we advocate the use of a non-monotonic language for this task. As a first contribution, we present a simple extension of UCQs with nonmonotonic features, and show that non-monotonicity provides more expressive power in characterizing the semantics of data services. A second contribution is to prove that, similarly to the case of monotonic abstractions, depending on the expressive power of the languages used to specify the various components of the OBDA system, there are cases where neither perfect nor approximated abstractions exist for a given data service. As a third contribution, we single out interesting special cases where the existence of abstractions is guaranteed, and we present algorithms for computing such abstractions in these cases

Using Ontologies for Semantic Data Integration

While big data analytics is considered as one of the most important paths to competitive advantage of today’s enterprises, data scientists spend a comparatively large amount of time in the data preparation and data integration phase of a big data project. This shows that data integration is still a major challenge in IT applications. Over the past two decades, the idea of using semantics for data integration has become increasingly crucial, and has received much attention in the AI, database, web, and data mining communities. Here, we focus on a specific paradigm for semantic data integration, called Ontology-Based Data Access (OBDA). The goal of this paper is to provide an overview of OBDA, pointing out both the techniques that are at the basis of the paradigm, and the main challenges that remain to be addressed

A Higher-Order Semantics for Metaquerying in OWL 2 QL

Inspired by recent work on higher-order Description Logics, we propose HOS, a new semantics for OWL 2 QL ontologies. We then consider SPARQL queries which are legal under the direct semantics entailment regime, we extend them with logical union, existential variables, and unrestricted use of variables so as to express meaningful meta-level queries. We show that both satisfiability checking and answering instance queries with metavariables have the same ABox complexity as under direct semantic

Inconsistency-tolerant Query Answering in Ontology-based Data Access

Ontology-based data access (OBDA) is receiving great attention as a new paradigm for managing information systems through semantic technologies. According to this paradigm, a Description Logic ontology provides an abstract and formal representation of the domain of interest to the information system, and is used as a sophisticated schema for accessing the data and formulating queries over them. In this paper, we address the problem of dealing with inconsistencies in OBDA. Our general goal is both to study DL semantical frameworks that are inconsistency-tolerant, and to devise techniques for answering unions of conjunctive queries under such inconsistency-tolerant semantics. Our work is inspired by the approaches to consistent query answering in databases, which are based on the idea of living with inconsistencies in the database, but trying to obtain only consistent information during query answering, by relying on the notion of database repair. We first adapt the notion of database repair to our context, and show that, according to such a notion, inconsistency-tolerant query answering is intractable, even for very simple DLs. Therefore, we propose a different repair-based semantics, with the goal of reaching a good compromise between the expressive power of the semantics and the computational complexity of inconsistency-tolerant query answering. Indeed, we show that query answering under the new semantics is first-order rewritable in OBDA, even if the ontology is expressed in one of the most expressive members of the DL-Lite family

Semantic technology for open data publishing

After years of focus on technologies for big data storing and processing, many observers are pointing out that making sense of big data cannot be done without suitable tools for conceptualizing, preparing, and integrating data (see http://www.dbta.com/). Research in the last years has shown that taking into account the semantics of data is crucial for devising powerful data integration solutions. In this work we focus on a specific paradigm for semantic data integration, named "Ontology-Based Data Access" (OBDA), proposed in [1-4]. According to such paradigm, the client of the information system is freed from being aware of how data and processes are structured in concrete resources (databases, software programs, services, etc.), and interacts with the system by expressing her queries and goals in terms of a conceptual representation of the domain of interest, called ontology. More precisely, a system realizing the vision of OBDA is constituted by three components: The ontology, whose goal is to provide a formal, clean and high level representation of the domain of interest, and constitutes the component with which the clients of the system (both humans and software programs) interact. fiedata source layer, representing the existing data sources in the information system, which are managed by the processes and services operating on their data. e mapping between the two layers, which is an explicit representation of the relationship between the data sources and the ontology, and is used to translate the operations on the ontology (e.g., query answering) in terms of concrete actions on the data sources.