f-SPARQL: A Flexible Extension of SPARQL

Cheng, Jingwei; Ma, Zongmin; Li, Yan

doi:10.1007/978-3-642-15364-8_41

Cited by 40 publications

(40 citation statements)

References 6 publications

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“…Specifically, we extend the set-based SPARQL semantics to model degrees of membership of a mapping to the evaluation of a SPARQL expression. This is different from prior related work [9,16,32,36] which, in addition to providing a new fuzzy semantics for SPARQL, extend the language itself to represent fuzzy queries. In HARE, users do not need to be aware of vagueness, and continue to specify queries using SPARQL.…”

Section: Query Enginementioning

confidence: 79%

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Enhancing Answer Completeness of SPARQL Queries via Crowdsourcing

et al. 2017

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Linked Open Data initiatives have encouraged the publication of large RDF datasets into the Linking Open Data (LOD) cloud, including DBpedia, YAGO, and Geo-Names. Despite the size of LOD datasets and the development of (semi-)automatic methods to create and link LOD data, these datasets may be still incomplete, negatively affecting thus accuracy of Linked Data processing techniques. We acquire query answer completeness by capturing knowledge collected from the crowd, and propose a novel hybrid query processing engine that brings together machine and human computation to execute SPARQL queries. Our system, HARE, implements these hybrid query processing techniques. HARE encompasses several features: (1) a completeness model for RDF that exploits the characteristics of RDF in order to estimate the completeness of an RDF dataset; (2) a crowd knowledge base that captures crowd answers about missing values in the RDF dataset; (3) a query engine that combines on-the-fly crowd knowledge and estimates provided by the RDF completeness model, to decide upon the sub-queries of a SPARQL query that should be executed against the dataset or via crowd computing to enhance query answer completeness; and (4) a microtask manager that exploits the semantics encoded in the dataset RDF properties, to crowdsource SPARQL subqueries as microtasks and update the crowd knowledge base with the results from the crowd. Effectiveness and efficiency of HARE are empirically studied on a collection of 50 SPARQL queries against the DBpedia dataset. Experimental results clearly show that our solution accurately enhances answer completeness.

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Section: Query Enginementioning

confidence: 79%

“…This represents an upper bound of the number false positives produced by HARE. 9 We then measure the false discovery rate 9 Note that, in practice, there might be real missing values that can be considered false positives with the given definition. For example, consider the movie dbr:Beauty andthe Beast (2017 film).…”

Section: Hare Crowdsourcing Capabilitiesmentioning

confidence: 99%

Enhancing Answer Completeness of SPARQL Queries via Crowdsourcing

et al. 2017

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“…To the contrary, it is uncommon to have RDF stores including imprecise reasoning. However, there are particular instances such as f-SPARQL [10], a "flexible extension of SPARQL", that allows in the FILTER constraint, the occurrence of fuzzy terms and fuzzy operators (by using α-cut operation), as well as weights in fuzzy constraints to have different importance and efficiently compute the top-k answers.…”

Section: Related Workmentioning

confidence: 99%

An approach to improve semantics in Smart Spaces using reactive fuzzy rules

Rodrı́guez

Lilius

Cuéllar

et al. 2013

2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS)

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Human behaviour representation and modelling in Smart Spaces are crucial tasks in Ambient Intelligence environments. A problem found among existing technologies is that the environment sensor data is always provided as crisp events. However, to model behaviour in reactive Smart Spaces we need to identify user patterns, which are not always performed in the same way or happening at the same time/frequency. Hence, the handling of imprecision is essential to model human behaviour in a realistic way. In order to connect these two paradigms, we suggest a combined architecture to fill the gap between quantitative Smart Space architectures (which provide raw crisp events) and qualitative aspects such as fuzzy reasoning and learning algorithms, that extract intelligence from data. With this aim, we propose to use Semantic Web principles of independence and interoperability to build an integrated framework to take advantage of the benefits of both crisp and fuzzy models. Our contribution in this work is a proof of concept to demonstrate how a hybrid architecture, with a reactive rule-based subscription mechanism, can empower users to model everyday activities semantically or control what happens on their surroundings, among other advantages.

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“…Several works extend the standard SPARQL algebra to allow the definition of ranking predicates [10,20]. AnQL [17] is an extension of the SPARQL language and algebra able to address a wide variety of queries (including top-k ones) over annotated RDF graphs; our approach, on the other hand, requires no annotations, and can be applied to any state-of-the-art SPARQL engine.…”

Section: Related Workmentioning

confidence: 99%

Efficient Execution of Top-K SPARQL Queries

Magliacane

Bozzon

Valle

2012

Lecture Notes in Computer Science

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Abstract. Top-k queries, i.e. queries returning the top k results ordered by a user-defined scoring function, are an important category of queries. Order is an important property of data that can be exploited to speed up query processing. State-of-the-art SPARQL engines underuse order, and top-k queries are mostly managed with a materialize-then-sort processing scheme that computes all the matching solutions (e.g. thousands) even if only a limited number k (e.g. ten) are requested. The SPARQL-RANK algebra is an extended SPARQL algebra that treats order as a first class citizen, enabling efficient split-and-interleave processing schemes that can be adopted to improve the performance of top-k SPARQL queries. In this paper we propose an incremental execution model for SPARQL-RANK queries, we compare the performance of alternative physical operators, and we propose a rank-aware join algorithm optimized for native RDF stores. Experiments conducted with an open source implementation of a SPARQL-RANK query engine based on ARQ show that the evaluation of top-k queries can be sped up by orders of magnitude.

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f-SPARQL: A Flexible Extension of SPARQL

Cited by 40 publications

References 6 publications

Enhancing Answer Completeness of SPARQL Queries via Crowdsourcing

Enhancing Answer Completeness of SPARQL Queries via Crowdsourcing

An approach to improve semantics in Smart Spaces using reactive fuzzy rules

Efficient Execution of Top-K SPARQL Queries

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