ODIN: A dataspace management system

ODIN is a system that supports the incremental pay-as-you-go integration of data sources into dataspaces and provides user-friendly querying mechanisms on top of them. We describe its main characteristics and underlying assumptions, including the user interactions required. Odin’s novelty lies in a largely automated bottom-up approach (i.e., driven by the sources at hand) that includes the user in the loop for disambiguation purposes. The on-site demonstration will feature an ongoing project with the World Health Organization (WHO). Online demo and videos: www.essi.upc.edu/dtim/odin/Peer ReviewedPostprint (published version

Ontology Based Data Access in Statoil

Ontology Based Data Access (OBDA) is a prominent approach to query databases which uses an ontology to expose data in a conceptually clear manner by abstracting away from the technical schema-level details of the underlying data. The ontology is ‘connected’ to the data via mappings that allow to automatically translate queries posed over the ontology into data-level queries that can be executed by the underlying database management system. Despite a lot of attention from the research community, there are still few instances of real world industrial use of OBDA systems. In this work we present data access challenges in the data-intensive petroleum company Statoil and our experience in addressing these challenges with OBDA technology. In particular, we have developed a deployment module to create ontologies and mappings from relational databases in a semi-automatic fashion; a query processing module to perform and optimise the process of translating ontological queries into data queries and their execution over either a single DB of federated DBs; and a query formulation module to support query construction for engineers with a limited IT background. Our modules have been integrated in one OBDA system, deployed at Statoil, integrated with Statoil’s infrastructure, and evaluated with Statoil’s engineers and data

On the containment of SPARQL queries under entailment regimes

Most description logics (DL) query languages allow instance retrieval from an ABox. However, SPARQL is a schema query language allowing access to the TBox (in addition to the ABox). Moreover, its entailment regimes enable to take into account knowledge inferred from knowledge bases in the query answering process. This provides a new perspective for the containment problem. In this paper, we study the containment of SPARQL queries over OWL EL axioms under entailment. OWL EL is the language used by many large scale ontologies and is based on EL++. The main contribution is a novel approach to rewriting queries using SPARQL property paths and the μ-calculus in order to reduce containment test under entailment into validity check in the μ-calculus

Incremental schema integration for data wrangling via knowledge graphs

Virtual data integration is the current approach to go for data wrangling in data-driven decision-making. In this paper, we focus on automating schema integration, which extracts a homogenised representation of the data source schemata and integrates them into a global schema to enable virtual data integration. Schema integration requires a set of well-known constructs: the data source schemata and wrappers, a global integrated schema and the mappings between them. Based on them, virtual data integration systems enable fast and on-demand data exploration via query rewriting. Unfortunately, the generation of such constructs is currently performed in a largely manual manner, hindering its feasibility in real scenarios. This becomes aggravated when dealing with heterogeneous and evolving data sources. To overcome these issues, we propose a fully-fledged semi-automatic and incremental approach grounded on knowledge graphs to generate the required schema integration constructs in four main steps: bootstrapping, schema matching, schema integration, and generation of system-specific constructs. We also present NextiaDI, a tool implementing our approach. Finally, a comprehensive evaluation is presented to scrutinize our approach.This work was partly supported by the DOGO4ML project, funded by the Spanish Ministerio de Ciencia e Innovación under project PID2020-117191RB-I00, and D3M project, funded by the Spanish Agencia Estatal de Investigación (AEI) under project PDC2021-121195-I00. Javier Flores is supported by contract 2020-DI-027 of the Industrial Doctorate Program of the Government of Catalonia and Consejo Nacional de Ciencia y Tecnología (CONACYT, Mexico). Sergi Nadal is partly supported by the Spanish Ministerio de Ciencia e Innovación, as well as the European Union – NextGenerationEU, under project FJC2020-045809-I.Peer ReviewedPostprint (published version

Provenance-aware knowledge representation: A survey of data models and contextualized knowledge graphs

Expressing machine-interpretable statements in the form of subject-predicate-object triples is a well-established practice for capturing semantics of structured data. However, the standard used for representing these triples, RDF, inherently lacks the mechanism to attach provenance data, which would be crucial to make automatically generated and/or processed data authoritative. This paper is a critical review of data models, annotation frameworks, knowledge organization systems, serialization syntaxes, and algebras that enable provenance-aware RDF statements. The various approaches are assessed in terms of standard compliance, formal semantics, tuple type, vocabulary term usage, blank nodes, provenance granularity, and scalability. This can be used to advance existing solutions and help implementers to select the most suitable approach (or a combination of approaches) for their applications. Moreover, the analysis of the mechanisms and their limitations highlighted in this paper can serve as the basis for novel approaches in RDF-powered applications with increasing provenance needs

Four Lessons in Versatility or How Query Languages Adapt to the Web

Exposing not only human-centered information, but machine-processable data on the Web is one of the commonalities of recent Web trends. It has enabled a new kind of applications and businesses where the data is used in ways not foreseen by the data providers. Yet this exposition has fractured the Web into islands of data, each in different Web formats: Some providers choose XML, others RDF, again others JSON or OWL, for their data, even in similar domains. This fracturing stifles innovation as application builders have to cope not only with one Web stack (e.g., XML technology) but with several ones, each of considerable complexity. With Xcerpt we have developed a rule- and pattern based query language that aims to give shield application builders from much of this complexity: In a single query language XML and RDF data can be accessed, processed, combined, and re-published. Though the need for combined access to XML and RDF data has been recognized in previous work (including the W3C’s GRDDL), our approach differs in four main aspects: (1) We provide a single language (rather than two separate or embedded languages), thus minimizing the conceptual overhead of dealing with disparate data formats. (2) Both the declarative (logic-based) and the operational semantics are unified in that they apply for querying XML and RDF in the same way. (3) We show that the resulting query language can be implemented reusing traditional database technology, if desirable. Nevertheless, we also give a unified evaluation approach based on interval labelings of graphs that is at least as fast as existing approaches for tree-shaped XML data, yet provides linear time and space querying also for many RDF graphs. We believe that Web query languages are the right tool for declarative data access in Web applications and that Xcerpt is a significant step towards a more convenient, yet highly efficient data access in a “Web of Data”

Integrating and querying linked datasets through ontological rules

The Web of Linked Open Data has developed from a few datasets in 2007 into a large data space containing billions of RDF triples published and stored in hundreds of independent datasets, so as to form the so called Linked Open Data Cloud. This information cloud, ranging over a wide set of data domains, poses a challenge when it comes to reconciling heterogeneous schemas or vocabularies adopted by data publishers. Motivated by this challenge, in this thesis was address the problem of integrating and querying multiple heterogeneous Linked Data sets through ontological rules. Firstly, we propose a formalisation of the notion of a peer-to-peer Linked Data integration system, where the mappings between peers comprise schema-level mappings and equality constraints between different IRIs; we call this formalism an RDF Peer System(RPS). We show that the semantics of the mappings preserve tractability of answering Basic Graph Pattern (BGP) SPARQL queries against the data stored in the RDF sources and the set of constraints given by the RPS mappings. Then, we address the problem of SPARQL query rewriting under RPSs and we show that it is not possible to rewrite an input BGP SPARQL query into a SPARQL 1.0 query under general RPSs, as the RPS peer mappings are not first-order-rewritable rules; this is a major drawback of general RPSs since data materialisation is required to exploit their full semantics. With the adoption of the more recent standard SPARQL 1.1 and its property paths we are able to extend the expressivity of the target language beyond first-order by including regular expressions in the body of the target SPARQL queries, that is, by expressing conjunctive two-way regular path queries (C2RPQs). Following this idea, in the second part of the thesis we step away from the language of RPSs to conduct a study on C2RPQ-rewritability under a broader ontology language. We define [ELHI`inh] (harmless linear ELHI), an ontology language that generalises both the DL-Lite[R] and linear ELH description logics. We prove the rewritability of instance queries (queries with a single atom in their body) under [ELHI`inh] knowledge bases with C2RPQs as the target language, presenting a query rewriting algorithm that makes use of non-deterministic finite-state automata. Following from that, we propose a query rewriting algorithm for answering conjunctive queries under [ELHI`inh] knowledge bases, with C2RPQs as the target language. Since C2RPQs can be straightforwardly expressed in SPARQL 1.1 by means of property paths, we believe that our approach is directly applicable to real-world querying settings. Lastly, we undertake a complexity analysis for query answering under [ELHI`inh]. We analyse the computational cost of query answering in terms of both data complexity (where the ontology and the query are fixed and the data alone is a variable input)and combined complexity (where query, ontology and data all constitute the variable input). We show that answering instance queries under [ELHI`inh] is NLogSpace-complete for data complexity and in PTime for combined complexity; we also show that answering CQs under [ELHI`inh] is NLogSpace-complete for data complexity and NP-complete for combined complexity

Storage, Querying and Inferencing for Semantic Web Languages

Harmelen, F.A.H. van [Promotor