Martin Rezk

Ontop: Answering SPARQL queries over relational databases

We present Ontop, an open-source Ontology-Based Data Access (OBDA) system that allows for querying relational data sources through a conceptual representation of the domain of interest, provided in terms of an ontology, to which the data sources are mapped. Key features of Ontop are its solid theoretical foundations, a virtual approach to OBDA, which avoids materializing triples and is implemented through the query rewriting technique, extensive optimizations exploiting all elements of the OBDA architecture, its compliance to all relevant W3C recommendations (including SPARQL queries, R2RML mappings, and OWL2QL and RDFS ontologies), and its support for all major relational databases.

Optique: Zooming in on Big Data

Optique overcomes problems in current ontology-based data access systems pertaining to installation overhead, usability, scalability, and scope by integrating a user-oriented query interface, semi-automated managing methods, new query rewriting techniques, and temporal and streaming data processing in one platform.

Efficient SPARQL-to-SQL with R2RML mappings

Abstract Existing SPARQL-to-SQL translation techniques have limitations that reduce their robustness, efficiency and dependability. These limitations include the generation of inefficient or even incorrect SQL queries, lack of formal background, and poor implementations. Moreover, some of these techniques cannot be used over arbitrary DB schemas due to the lack of support for RDB to RDF mapping languages, such as R2RML. In this paper we present a technique (implemented in the -ontop- system) that tackles all these issues. We propose a formal approach for SPARQL-to-SQL translation that (i) generates efficient SQL by combining optimization techniques from the logic programming and SQL optimization fields; (ii) provides a well-defined specification of the SPARQL semantics used in the translation; and (iii) supports R2RML mappings over general relational schemas. We provide extensive benchmarks using the -ontop- system for Ontology Based Data Access (OBDA) and show that by using these techniques -ontop- is able to outperform well known SPARQL-to-SQL systems, as well as commercial triple stores, by several orders of magnitude.

The Ontop Framework for Ontology Based Data Access

Ontology Based Data Access (OBDA) [4] is a paradigm of accessing data trough a conceptual layer. Usually, the conceptual layer is expressed in the form of an RDF(S) [10] or OWL [15] ontology, and the data is stored in relational databases.

Ontology-based data integration in EPNet

Semantic technologies are rapidly changing the historical research. Over the last decades, an immense amount of new quantifiable data have been accumulated, and made available in interchangeable formats, in social sciences and humanities, opening up new possibilities for solving old questions and posing new ones. This paper introduces a framework that eases the access of scholars to historical and cultural data about food production and commercial trade system during the Roman Empire, distributed across different data sources. The proposed approach relies on the Ontology-Based Data Access (OBDA) paradigm, where the different datasets are virtually integrated by a conceptual layer (an ontology) that provides to the user a clear point of access and a unified and unambiguous conceptual view.

Rules and Ontology Based Data Access

In OBDA an ontology defines a high level global vocabulary for user queries, and such vocabulary is mapped to (typically relational) databases. Extending this paradigm with rules, e.g., expressed in SWRL or RIF, boosts the expressivity of the model and the reasoning ability to take into account features such as recursion and n-ary predicates. We consider evaluation of SPARQL queries under rules with linear recursion, which in principle is carried out by a 2-phase translation to SQL: (1) The SPARQL query, together with the RIF/SWRL rules, and the mappings is translated to a Datalog program, possibly with linear recursion; (2) The Datalog program is converted to SQL by using recursive common table expressions. Since a naive implementation of this translation generates inefficient SQL code, we propose several optimisations to make the approach scalable. We implement and evaluate the techniques presented here in the Ontop system. To the best of our knowledge, this results in the first system supporting all of the following W3C standards: the OWL 2 QL ontology language, R2RML mappings, SWRL rules with linear recursion, and SPARQL queries. The preliminary but encouraging experimental results on the NPD benchmark show that our approach is scalable, provided optimisations are applied.

Ontology-Based Integration of Cross-Linked Datasets

In this paper we tackle the problem of answering SPARQL queries over virtually integrated databases. We assume that the entity resolution problem has already been solved and explicit information is available about which records in the different databases refer to the same real world entity. Surprisingly, to the best of our knowledge, there has been no attempt to extend the standard Ontology-Based Data Access OBDA setting to take into account these DB links for SPARQL query-answering and consistency checking. This is partly because the OWL built-in owl:sameAs property, the most natural representation of links between data sets, is not included in OWLi¾?2i¾?QL, the de facto ontology language for OBDA. We formally treat several fundamental questions in this context: how links over database identifiers can be represented in terms of owl:sameAs statements, how to recover rewritability of SPARQL into SQL lost because of owl:sameAs statements, and how to check consistency. Moreover, we investigate how our solution can be made to scale up to large enterprise datasets. We have implemented the approach, and carried out an extensive set of experiments showing its scalability.

Transaction Logic with Partially Defined Actions

In this paper we develop a novel logic formalism,

How to Stay Ontop of Your Data: Databases, Ontologies and More

{\mathcal{T} \, \mathcal{R}^{PAD}}