64 research outputs found

    Optimizing Analytical Queries over Semantic Web Sources

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    Provenance for SPARQL queries

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    Determining trust of data available in the Semantic Web is fundamental for applications and users, in particular for linked open data obtained from SPARQL endpoints. There exist several proposals in the literature to annotate SPARQL query results with values from abstract models, adapting the seminal works on provenance for annotated relational databases. We provide an approach capable of providing provenance information for a large and significant fragment of SPARQL 1.1, including for the first time the major non-monotonic constructs under multiset semantics. The approach is based on the translation of SPARQL into relational queries over annotated relations with values of the most general m-semiring, and in this way also refuting a claim in the literature that the OPTIONAL construct of SPARQL cannot be captured appropriately with the known abstract models.Comment: 22 pages, extended version of the ISWC 2012 paper including proof

    Benchmarking RDF Storage Engines

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    In this deliverable, we present version V1.0 of SRBench, the first benchmark for Streaming RDF engines, designed in the context of Task 1.4 of PlanetData, completely based on real-world datasets. With the increasing problem of too much streaming data but not enough knowledge, researchers have set out for solutions in which Semantic Web technologies are adapted and extended for the publishing, sharing, analysing and understanding of such data. Various approaches are emerging. To help researchers and users to compare streaming RDF engines in a standardised application scenario, we propose SRBench, with which one can assess the abilities of a streaming RDF engine to cope with a broad range of use cases typically encountered in real-world scenarios. We offer a set of queries that cover the major aspects of streaming RDF engines, ranging from simple pattern matching queries to queries with complex reasoning tasks. To give a first baseline and illustrate the state of the art, we show results obtained from implementing SRBench using the SPARQLStream query-processing engine developed by UPM

    Analytical Queries on Vanilla RDF Graphs with a Guided Query Builder Approach

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    International audienceAs more and more data are available as RDF graphs, the availability of tools for data analytics beyond semantic search becomes a key issue of the Semantic Web. Previous work require the modelling of data cubes on top of RDF graphs. We propose an approach that directly answers analytical queries on unmodified (vanilla) RDF graphs by exploiting the computation features of SPARQL 1.1. We rely on the NF design pattern to design a query builder that completely hides SPARQL behind a verbalization in natural language; and that gives intermediate results and suggestions at each step. Our evaluations show that our approach covers a large range of use cases, scales well on large datasets, and is easier to use than writing SPARQL queries

    A General Framework for Representing, Reasoning and Querying with Annotated Semantic Web Data

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    We describe a generic framework for representing and reasoning with annotated Semantic Web data, a task becoming more important with the recent increased amount of inconsistent and non-reliable meta-data on the web. We formalise the annotated language, the corresponding deductive system and address the query answering problem. Previous contributions on specific RDF annotation domains are encompassed by our unified reasoning formalism as we show by instantiating it on (i) temporal, (ii) fuzzy, and (iii) provenance annotations. Moreover, we provide a generic method for combining multiple annotation domains allowing to represent, e.g. temporally-annotated fuzzy RDF. Furthermore, we address the development of a query language -- AnQL -- that is inspired by SPARQL, including several features of SPARQL 1.1 (subqueries, aggregates, assignment, solution modifiers) along with the formal definitions of their semantics

    Ontop: answering SPARQL queries over relational databases

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    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

    A SPARQL 1.1 Query Builder for the Data Analytics of Vanilla RDF Graphs

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    As more and more data are available as RDF graphs, the availability of tools for data analytics beyond semantic search becomes a key issue of the Semantic Web. Previous work has focused on adapting OLAP-like approaches and question answering by modelling RDF data cubes on top of RDF graphs. We propose a more direct – and more expressive – approach by guiding users in the incremental building of SPARQL 1.1 queries that combine several computation features (aggregations, expressions, bindings and filters), and by evaluating those queries on unmodified (vanilla) RDF graphs. We rely on the N<A>F design pattern to hide SPARQL behind a natural language interface, and to provide results and suggestions at every step. We have implemented our approach on top of Sparklis, and we report on three experiments to assess its expressivity, usability, and scalability

    Provenance : from long-term preservation to query federation and grid reasoning

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