SPARQL query rewriting for implementing data integration over linked data

There has been lately an increased activity of publishing structured data in RDF due to the activity of the Linked Data community 1. The presence on the Web of such a huge information cloud, ranging from academic to geographic to gene related information, poses a great challenge when it comes to reconcile heterogeneous schemas adopted by data publishers. For several years, the Semantic Web community has been developing algorithms for aligning data models (ontologies). Nevertheless, exploiting such ontology alignments for achieving data integration is still an under supported research topic. The semantics of ontology alignments, often defined over a logical frameworks, implies a reasoning step over huge amounts of data, that is often hard to implement and rarely scales on Web dimensions. This paper presents an algorithm for achieving RDF data mediation based on SPARQL query rewriting. The approach is based on the encoding of rewriting rules for RDF patterns that constitute part of the structure of a SPARQL query

SPARQL Query Recommendations by Example

In this demo paper, a SPARQL Query Recommendation Tool (called SQUIRE) based on query reformulation is presented. Based on three steps, Generalization, Specialization and Evaluation, SQUIRE implements the logic of reformulating a SPARQL query that is satisfiable w.r.t a source RDF dataset, into others that are satisfiable w.r.t a target RDF dataset. In contrast with existing approaches, SQUIRE aims at rec- ommending queries whose reformulations: i) reflect as much as possible the same intended meaning, structure, type of results and result size as the original query and ii) do not require to have a mapping between the two datasets. Based on a set of criteria to measure the similarity between the initial query and the recommended ones, SQUIRE demonstrates the feasibility of the underlying query reformulation process, ranks appropriately the recommended queries, and offers a valuable support for query recommendations over an unknown and unmapped target RDF dataset, not only assisting the user in learning the data model and content of an RDF dataset, but also supporting its use without requiring the user to have intrinsic knowledge of the data

Translating expressive ontology mappings into rewriting rules to implement query rewriting

The increasing amount of structured RDF data published by the Linked Data community poses a great challenge when it comes to reconcile heterogeneous schemas adopted by data publishers. For several years, the Semantic Web community has been developing algorithms for aligning data models (ontologies). Nevertheless, exploiting such ontology alignments for achieving data integration is still an under supported research topic. The semantics of ontology alignments, often defined over a logical framework, implies a reasoning step over huge amounts of data. This is often hard to implement and rarely scales on Web dimensions. This paper presents our approach for translating DL-like ontology alignments into graph patterns that can be used to implement ontological mediation in the form of SPARQL query rewriting and generation. This approach backs up a previous work for achieving SPARQL query rewriting where syntactical transformations of basic graph patterns are used. Supporting a rich ontology alignment language into our system is important for two reasons. Firstly the users can express rich alignments focusing on their semantic soundness; secondly more verbose correspondences of RDF patterns can be generated by the translation process providing a denotational semantics to the alignment language itself. The approach has been implemented into an open source Java API freely available to the community

FishMark: A Linked Data Application Benchmark

Abstract. FishBase is an important species data collection produced by the FishBase Information and Research Group Inc (FIN), a not-forprofit NGO with the aim of collecting comprehensive information (from the taxonomic to the ecological) about all the world’s finned fish species. FishBase is exposed as a MySQL backed website (supporting a range of canned, although complex queries) and serves over 33 million hits per month. FishDelish is a transformation of FishBase into LinkedData weighing in at 1.38 billion triples. We have ported a substantial number of FishBase SQL queries to FishDelish SPARQL query which form the basis of a new linked data application benchmark (using our derivative of the Berlin SPARQL Benchmark harness). We use this benchmarking framework to compare the performance of the native MySQL application, the Virtuoso RDF triple store, and the Quest OBDA system on a fishbase.org like application.

Peer-based query rewriting in SPARQL for semantic integration of linked data

In this proposal we address the problem of ontology-based SPARQL query answering over distributed Linked Data sources, where the ontology is given by conjunctive mappings between the source schemas in a peer-to-peer fashion and by equality constraints between constants. In our setting, the data is not materialised in a single datastore: it is accessed in a distributed environment through SPARQL endpoints. We aim to achieve query answering by generating the perfect rewriting of the original query and then processing the rewritten query over distributed SPARQL endpoints. We identify a subset of ontology constraints that enjoy the first-order rewritability property and we perform preliminary empirical evaluation taking into account such restricted constraints only. For future work, we aim to tackle the query answering problem in the general case

Improving lifecycle query in integrated toolchains using linked data and MQTT-based data warehousing

The development of increasingly complex IoT systems requires large engineering environments. These environments generally consist of tools from different vendors and are not necessarily integrated well with each other. In order to automate various analyses, queries across resources from multiple tools have to be executed in parallel to the engineering activities. In this paper, we identify the necessary requirements on such a query capability and evaluate different architectures according to these requirements. We propose an improved lifecycle query architecture, which builds upon the existing Tracked Resource Set (TRS) protocol, and complements it with the MQTT messaging protocol in order to allow the data in the warehouse to be kept updated in real-time. As part of the case study focusing on the development of an IoT automated warehouse, this architecture was implemented for a toolchain integrated using RESTful microservices and linked data.Comment: 12 pages, worksho

KGRAM Versatile Inference and Query Engine for the Web of Linked Data

International audienceQuerying and linking distributed and heterogeneous databases is increasingly needed, as plentiful data resources are published over the Web. This work describes the design of a versatile query system named KGRAM that supports (i) multiple query languages among which the SPARQL 1.1 standard, (ii) federation of multiple heterogeneous and distributed data sources, and (iii) adaptability to various data manipulation use cases. KGRAM provides abstractions for both the query language and the data model, thus delivering unifying reasoning mechanisms. It is implemented as a modular software suite to ease architecting and deploying dedicated data manipulation platforms. Its design integrates optimization concerns to deliver high query performance. Both KGRAM's software versatility and performance are evaluated