Towards a Multi-way Similarity Join Operator

Galkin, Mikhail; Vidal, María-Esther; Auer, Sören

doi:10.1007/978-3-319-67162-8_26

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Galvin et al [10] propose a multiway similarity join operator for RDF; however, the notion of similarity considered is based on semantic similarity that tries to match different terms referring to the same real-world entity. Closer to this work lies iSPARQL [20], which extends SPARQL with IMPRECISE clauses that can include similarity joins on individual attributes.…”

Section: Similarity In Databasesmentioning

confidence: 99%

Similarity joins and clustering for SPARQL

Ferrada,

Bustos,

Hogan

2024

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The SPARQL standard provides operators to retrieve exact matches on data, such as graph patterns, filters and grouping. This work proposes and evaluates two new algebraic operators for SPARQL 1.1 that return similarity-based results instead of exact results. First, a similarity join operator is presented, which brings together similar mappings from two sets of solution mappings. Second, a clustering solution modifier is introduced, which instead of grouping solution mappings according to exact values, brings them together by using similarity criteria. For both cases, a variety of algorithms are proposed and analysed, and use-case queries that showcase the relevance and usefulness of the novel operators are presented. For similarity joins, experimental results are provided by comparing different physical operators over a set of real world queries, as well as comparing our implementation to the closest work found in the literature, DBSimJoin, a PostgreSQL extension that supports similarity joins. For clustering, synthetic queries are designed in order to measure the performance of the different algorithms implemented.

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Section: Similarity In Databasesmentioning

confidence: 99%

Similarity joins and clustering for SPARQL

Ferrada,

Bustos,

Hogan

2024

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Extending SPARQL with Similarity Joins

Ferrada

Bustos

Hogan

2020

Lecture Notes in Computer Science

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Worst-Case-Optimal Similarity Joins on Graph Databases

Arroyuelo,

Bustos,

Gómez-Brandón

et al. 2024

Proc. ACM Manag. Data

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We extend the concept of worst-case optimal equijoins in graph databases to the case where some nodes are required to be within the k-nearest neighbors (kNN) of others under some similarity function. We model the problem by superimposing the database graph with the kNN graph and show that a variant of Leapfrog TrieJoin (LTJ) implemented over a compact data structure called the Ring can be seamlessly extended to integrate similarity clauses with the equijoins in the LTJ query process, retaining worst-case optimality in many relevant cases. Our experiments on a benchmark that combines Wikidata and IMGpedia show that our enhanced LTJ algorithm outperforms by a considerable margin a baseline that first applies classic LTJ and then completes the query by applying the similarity predicates. The difference is more pronounced on queries where the similarity clauses are more densely connected to the query, becoming of an order of magnitude in some cases.

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Towards a Multi-way Similarity Join Operator

Cited by 3 publications

References 15 publications

Similarity joins and clustering for SPARQL

Similarity joins and clustering for SPARQL

Extending SPARQL with Similarity Joins

Worst-Case-Optimal Similarity Joins on Graph Databases

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