Exploration of Knowledge Graphs via Online Aggregation

Kalinsky, Oren; Hogan, Aidan; Mishali, Oren; Etsion, Yoav; Kimelfeld, Benny

doi:10.1109/icde53745.2022.00247

Cited by 3 publications

(2 citation statements)

References 51 publications

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“…Worst-case optimal multi-way algorithms [26] have recently gained a lot of attention (e.g., [3,27,28,29]) and have demonstrated high performance in evaluating graph pattern queries [27,29,30]. Such algorithms satisfy the AGM bound [31] and their runtime matches the worst-case size of the result of the input query [23,27].…”

Section: Worst-case Optimal Multi-way Join Algorithmsmentioning

confidence: 99%

Native Execution of GraphQL Queries over RDF Graphs Using Multi-Way Joins

Karalis,

Bigerl,

Ngonga Ngomo

2023

Knowledge Graphs: Semantics, Machine Learning, and Languages

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Purpose: The query language GraphQL has gained significant traction in recent years. In particular, it has recently gained the attention of the semantic web and graph database communities and is now often used as a means to query knowledge graphs. Most of the storage solutions that support GraphQL rely on a translation layer to map the said language to another query language that they support natively, for example SPARQL. Methodology: Our main innovation is a multi-way left-join algorithm inspired by worst-case optimal multi-way join algorithms. This novel algorithm enables the native execution of GraphQL queries over RDF knowledge graphs. We evaluate our approach in two settings using the LinGBM benchmark generator. Findings: The experimental results suggest that our solution outperforms the state-of-the-art graph storage solution for GraphQL with respect to both query runtimes and scalability. Value: Our solution is implemented in an open-sourced triple store, and is intended to advance the development of representation-agnostic storage solutions for knowledge graphs.

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Section: Worst-case Optimal Multi-way Join Algorithmsmentioning

confidence: 99%

Native Execution of GraphQL Queries over RDF Graphs Using Multi-Way Joins

Karalis,

Bigerl,

Ngonga Ngomo

2023

Knowledge Graphs: Semantics, Machine Learning, and Languages

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“…For G the graph in Figure 17, (B, G)) time writing the results in the worst case, such algorithms can be considered worst-case optimal (wco) [143]. Though such algorithms were initially proposed in a relational setting [143,195], they were later applied for processing joins over RDF graphs [95,81,137]. Note that traditional pairwise join algorithms are not wco.…”

Section: Worst Case Optimal Joinsmentioning

confidence: 99%

A survey of RDF stores & SPARQL engines for querying knowledge graphs

et al. 2021

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Recent years have seen the growing adoption of non-relational data models for representing diverse, incomplete data. Among these, the RDF graphbased data model has seen ever-broadening adoption, particularly on the Web. This adoption has prompted the standardization of the SPARQL query language for RDF, as well as the development of a variety of local and distributed engines for processing queries over RDF graphs. These engines implement a diverse range of specialized techniques for storage, indexing, and query processing. A number of benchmarks, based on both synthetic and real-world data, have also emerged to allow for contrasting the performance of different query engines, often at large scale. This survey paper draws together these developments, providing a comprehensive review of the techniques, engines and benchmarks for querying RDF knowledge graphs.

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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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Exploration of Knowledge Graphs via Online Aggregation

Cited by 3 publications

References 51 publications

Native Execution of GraphQL Queries over RDF Graphs Using Multi-Way Joins

Native Execution of GraphQL Queries over RDF Graphs Using Multi-Way Joins

A survey of RDF stores & SPARQL engines for querying knowledge graphs

Worst-Case-Optimal Similarity Joins on Graph Databases

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