Computing Relaxed Answers on RDF Databases

Huang, Hai; Liu, Chengfei; Zhou, Xiaofang

doi:10.1007/978-3-540-85481-4_14

Cited by 25 publications

(25 citation statements)

References 12 publications

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“…In this section we describe how to compute the relaxed and approximated answer of a SPARQL AR query by making use of a query rewriting algorithm, following a similar approach to [7,8,13]. In particular, given a query Q with the APPROX and/or RELAX operators, our goal is to incrementally build a set of queries {Q 0 , .…”

Section: Query Evaluationmentioning

confidence: 99%

“…A similarity measure technique which exploits the structure of the RDF dataset can be found in [5], where the authors navigate the RDF dataset as a graph in which every path is matched with respect to the query. Other techniques such as ontology driven similarity measures have been developed in [8,7,13]. These techniques use the RDFS ontology to retrieve extra answers and assign a score value to such answers.…”

Section: Introductionmentioning

confidence: 99%

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Flexible Querying for SPARQL

Calì

Frosini

Poulovassilis

et al. 2014

On the Move to Meaningful Internet Systems: OTM 2014 Conferences

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Abstract. Flexible querying techniques can be used to enhance users' access to heterogeneous data sets, such as Linked Open Data. This paper extends SPARQL 1.1 with approximation and relaxation operators that can be applied to regular expressions for querying property paths in order to find more answers than would be returned by the exact form of a user query. We specify the semantics of the extended language and we consider the complexity of query answering with the new operators, showing that both data and query complexity are not impacted by our extensions. We present a query evaluation algorithm that returns results incrementally according to their "distance" from the original query. We have implemented this algorithm and have conducted preliminary trials over the YAGO SPARQL endpoint and the Lehigh University Benchmark, showing promising performance for the language extensions.

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Section: Query Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flexible Querying for SPARQL

Calì

Frosini

Poulovassilis

et al. 2014

On the Move to Meaningful Internet Systems: OTM 2014 Conferences

View full text Add to dashboard Cite

show abstract

“…This ranking model fails to rank relaxed queries when there does not exist subsume relationships among them. In [14], Huang at al. proposed a method to rank the relaxed queries depending on their similarities to the user query.…”

Section: Related Workmentioning

confidence: 97%

“…The previous works [14,15] generalize user RDF queries through RDF(s) entailment to capture potential approximate answers. The ranking models proposed, however, are either non-quantifiable or imprecise.…”

Section: Related Workmentioning

confidence: 99%

Approximating query answering on RDF databases

2011

Self Cite

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Database users may be frustrated by no answers returned when they pose a query on the database. In this paper, we study the problem of relaxing queries on RDF databases in order to acquire approximate answers. We address two problems in efficient query relaxation. First, to ensure the quality of answers, we compute the similarities between relaxed queries with regard to the user query and use them to score the potential relevant answers. Second, for obtaining top-k answers, we develop two algorithms. One is based on the best-first strategy and relaxed queries are executed in the ranking order. The batch based algorithm executes the relaxed queries as a batch and avoids unnecessary execution cost. At last, we implement and experimentally evaluate our approaches.

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“…Rewriting rules are used on query patterns in [4] to perform both query refinement by including user preferences as well as query relaxation. Building on the work of [12], [11] develops a similarity measure for relaxed queries in an attempt to improve the relevance of answers. Similarity-based querying was also the focus of iSPARQL [15], where resources (rather than paths connecting them) are compared using similarity measures.…”

Section: Related Workmentioning

confidence: 99%

Combining Approximation and Relaxation in Semantic Web Path Queries

Poulovassilis

Wood

2010

Lecture Notes in Computer Science

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Abstract. We develop query relaxation techniques for regular path queries and combine them with query approximation in order to support flexible querying of RDF data when the user lacks knowledge of its full structure or where the structure is irregular. In such circumstances, it is helpful if the querying system can perform both approximate matching and relaxation of the user's query and can rank the answers according to how closely they match the original query. Our framework incorporates both standard notions of approximation based on edit distance and RDFS-based inference rules. The query language we adopt comprises conjunctions of regular path queries, thus including extensions proposed for SPARQL to allow for querying paths using regular expressions. We provide an incremental query evaluation algorithm which runs in polynomial time and returns answers to the user in ranked order.

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Computing Relaxed Answers on RDF Databases

Cited by 25 publications

References 12 publications

Flexible Querying for SPARQL

Flexible Querying for SPARQL

Approximating query answering on RDF databases

Combining Approximation and Relaxation in Semantic Web Path Queries

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