Querying the Semantic Web with Preferences

Siberski, Wolf; Pan, Jeff Z.; Thaden, Uwe

doi:10.1007/11926078_44

Cited by 53 publications

(64 citation statements)

References 21 publications

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Order By: Relevance

“…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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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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“…[8] focused on extending SPARQL with support of expression of preference queries but it does not address the optimization of query processing. [13] formulated the problem of semantic web services selection using the notion of skyline query and proposed a solution for efficiently identifying the best match between requesters and providers.…”

Section: Related Workmentioning

confidence: 99%

“…An important class of querying paradigm for this purpose is preference queries, and in particular, skyline queries. Skyline queries are valuable for supporting multi-criteria decision making and have been extensively investigated in the context of relational databases [1] [12] but in a very limited way for Semantic Web [8].…”

Section: Introductionmentioning

confidence: 99%

“…However, there are much fewer efforts [11] [12][16] directed at evaluating skylines over multiple relations. A common strategy, which was also proposed in the context of preference queries on the Semantic Web [8], is to first join all necessary tables in a single table and then use a single table skyline algorithm to compute the skyline result, i.e. join-first-skyline-later (JFSL).…”

Section: Introductionmentioning

confidence: 99%

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Efficiently Evaluating Skyline Queries on RDF Databases

Chen

Gao

Anyanwu

2011

Lecture Notes in Computer Science

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Abstract. Skyline queries are a class of preference queries that compute the pareto-optimal tuples from a set of tuples and are valuable for multi-criteria decision making scenarios. While this problem has received significant attention in the context of single relational table, skyline queries over joins of multiple tables that are typical of storage models for RDF data has received much less attention. A naïve approach such as a join-first-skyline-later strategy splits the join and skyline computation phases which limit opportunities for optimization. Other existing techniques for multi-relational skyline queries assume storage and indexing techniques that are not typically used with RDF which would require a preprocessing step for data transformation. In this paper, we present an approach for optimizing skyline queries over RDF data stored using a vertically partitioned schema model. It is based on the concept of a "Header Point" which maintains a concise summary of the already visited regions of the data space. This summary allows some fraction of non-skyline tuples to be pruned from advancing to the skyline processing phase, thus reducing the overall cost of expensive dominance checks required in the skyline phase. We further present more aggressive pruning rules that result in the computation of near-complete skylines in significantly less time than the complete algorithm. A comprehensive performance evaluation of different algorithms is presented using datasets with different types of data distributions generated by a benchmark data generator.

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“…Conjunctive queries are also the formal core of SPARQL query language [11]. We thus use conjunctive queries as the basis of the hybrid query.…”

Section: Hybrid Query Capabilitymentioning

confidence: 99%

Semplore: An IR Approach to Scalable Hybrid Query of Semantic Web Data

et al. 2007

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Abstract. As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information. It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we breifly describe how Semplore is used for searching Wikipedia and an IBM customer's product information.

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Querying the Semantic Web with Preferences

Cited by 53 publications

References 21 publications

Efficient Execution of Top-K SPARQL Queries

Efficient Execution of Top-K SPARQL Queries

Efficiently Evaluating Skyline Queries on RDF Databases

Semplore: An IR Approach to Scalable Hybrid Query of Semantic Web Data

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