Fuzzy XPath Queries in XQuery

Almendros-Jiménez, Jesús Manuel; Luna, Alejandro; Moreno, Ginés

doi:10.1007/978-3-662-45563-0_27

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Recently, a fuzzy approach has been proposed to extend the XPath query language with the aim of providing mechanisms to assign priorities to queries and to rank query answers [2]. These techniques are based on fuzzy extensions of the Boolean operators.…”

Section: Related Workmentioning

confidence: 99%

“…In order to apply relaxation to queries, the extended reduction of an ontology K is required [13]. Given an ontology K, its extended reduction extRed(K) is computed as follows: (i) compute cl(K); (ii) apply the rules of Figure 2 in reverse until no more rules can be applied (after applying this step the ontology generated is unique); (iii) apply rules 1 and 3 of Figure 1 in reverse until no more rules can be applied 2 .…”

Section: Query Relaxationmentioning

confidence: 99%

“…Henceforth, we assume that K = extRed(K), which allows direct relaxations to be applied to queries (see below), corresponding to the 'smallest' relaxation steps. This is necessary for associating an unambiguous cost to queries, so that query an- 2 In order to generate a unique extended reduction we alter step (iii) of the procedure in [13] We thank one of the reviewers for pointing out that, without such an extra condition, the extended reduction may not be unique. swers can then be returned to users incrementally in order of increasing cost.…”

Section: Query Relaxationmentioning

confidence: 99%

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Flexible query processing for SPARQL

Frosini

Calì

Poulovassilis

et al. 2017

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Flexible querying techniques can enhance users' access to complex, heterogeneous datasets in settings such as Linked Data, where the user may not always know how a query should be formulated in order to retrieve the desired answers. This paper presents query processing algorithms for a fragment of SPARQL 1.1 incorporating regular path queries (property path queries), extended with query approximation and relaxation operators. Our flexible query processing approach is based on query rewriting and returns answers incrementally according to their "distance" from the exact form of the query. We formally show the soundness, completeness and termination properties of our query rewriting algorithm. We also present empirical results that show promising query processing performance for the extended language.

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Section: Related Workmentioning

confidence: 99%

Section: Query Relaxationmentioning

confidence: 99%

Section: Query Relaxationmentioning

confidence: 99%

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Flexible query processing for SPARQL

Frosini

Calì

Poulovassilis

et al. 2017

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“…Proposals for lexible querying of semi-structured data have included relaxing queries by removing conditions from XPath expressions [3], 'widening' queries by using knowledge from an ontology, thesaurus or schema [33,44,59], and fuzzy XPath query evaluation [2]. For lexible querying of RDF data, SPARQL's OPTIONAL clause [31] provides some lexibility by returning query answers that may fail to match speciied triple patterns of the query.…”

Section: Related Work 21 Flexible Queryingmentioning

confidence: 99%

Optimisation Techniques for Flexible SPARQL Queries

et al. 2022

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RDF datasets can be queried using the SPARQL language but are often irregularly structured and incomplete, which may make precise query formulation hard for users. The SPARQL AR language extends SPARQL 1.1 with two operators — APPROX and RELAX — so as to allow flexible querying over property paths. These operators encapsulate different dimensions of query flexibility, namely approximation and generalisation, and they allow users to query complex, heterogeneous knowledge graphs without needing to know precisely how the data is structured. Earlier work has described the syntax, semantics and complexity of SPARQL AR , has demonstrated its practical feasibility, but has also highlighted the need for improving the speed of query evaluation. In the present paper, we focus on the design of two optimisation techniques targeted at speeding up the execution of SPARQL AR queries and on their empirical evaluation on three knowledge graphs: LUBM, DBpedia and YAGO. We show that applying these optimisations can result in substantial improvements in the execution times of longer-running queries (sometimes by one or more orders of magnitude) without incurring significant performance penalties for fast queries.

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Applications of Flexible Querying to Graph Data

Poulovassilis

2018

Data-Centric Systems and Applications

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Graph data models provide flexibility and extensibility that makes them well-suited to modelling data that may be irregular, complex, and evolving in structure and content. However, a consequence of this is that users may not be familiar with the full structure of the data, which itself may be changing over time, making it hard for users to formulate queries that precisely match the data graph and meet their information seeking requirements. There is a need therefore for flexible querying systems over graph data that can automatically make changes to the user's query so as to find additional or different answers, and so help the user to retrieve information of relevance to them. This chapter describes recent work in this area, looking at a variety of graph query languages, applications, flexible querying techniques and implementations.A fragment of this global ontology is shown in Figure 1 (omitting the AccessNo i and AccessNo properties, and the domain and range information of PepSeq and Aligns).Consider now the following query posed over the global ontology by a user who

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Fuzzy XPath Queries in XQuery

Cited by 6 publications

References 16 publications

Flexible query processing for SPARQL

Flexible query processing for SPARQL

Optimisation Techniques for Flexible SPARQL Queries

Applications of Flexible Querying to Graph Data

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