Type-based Semantic Optimization for Scalable RDF Graph Pattern Matching

Kim, HyeongSik; Ravindra, Padmashree; Anyanwu, Kemafor

doi:10.1145/3038912.3052655

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…Graph queries via structure similarity. Structure similarity based graph pattern match is one commonly used technology to support query over knowledge graph . Zheng et al proposed a instance‐driven mining algorithm to detect diverse structure patterns with equivalent semantic meanings of priori knowledge .…”

Section: Related Workmentioning

confidence: 99%

Semantic locality‐based approximate knowledge graph query

Wang

Yan

et al. 2019

Concurrency and Computation

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Summary RDF knowledge graphs (KG) usually contain billions of labeled entities, and how to obtain the desired results efficiently on RDF KG for given SPARQL queries have attracted increasing attentions recently. However, it is difficult for users to write a complex SPARQL query without full knowledge of the underlying KG schema due to the “schema‐free” feature of RDF KG. In this paper, we study the problem of acquiring semantic approximate results for a simple SPARQL query instead of the complex expression. The basic idea behind is to use the real knowledge semantics to extend the query intention of given simplified SPARQL query, and then a semantic based greedy search over is designed to return top‐k similar results. To obtain the knowledge semantics efficiently, we define type similarity to reorganize the original KG as a corpus with semantic locality and then a context aware text embedding model is adopted to achieve the semantic vectors of existing knowledge. Afterwards, an approximate query method over RDF KG is designed to obtain top‐k similar results based on the knowledge semantics above. Extensive experiments over DBpedia dataset and QALD‐4 benchmark confirm our solution's superiority on both effectiveness and efficiency.

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Section: Related Workmentioning

confidence: 99%

Semantic locality‐based approximate knowledge graph query

Wang

Yan

et al. 2019

Concurrency and Computation

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“…For our proof-of-concept prototype, we implemented by integrating Semstorm [27] as the graph pattern matching platform and Serpent [21,23] as the path query computation platform. Semstorm uses the below two main datastructures as query plan representation to hold the position information of the different triple patterns in the submitted query.…”

Section: Implementation Strategymentioning

confidence: 99%

“…Implementation Strategy: Our graph pattern matching platform, Semstorm [27] is an RDF processing platform that is targeted for Cloud-processing and uses Apache Hadoop/Tez execution environment. Semstorm's compiler builds on Jena's parser, using Jena's SSE to create a Tez [3] DAG as the physical query plan based on Semstorm's query algebra.…”

Section: Logical Query Plan Transformationmentioning

confidence: 99%

Evaluating Generalized Path Queries by Integrating Algebraic Path Problem Solving with Graph Pattern Matching

Bhattacharyya

Baldin

Xin

et al. 2019

Lecture Notes in Computer Science

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Path querying on Semantic Networks is gaining increased focus because of its broad applicability. Some graph databases offer support for variants of path queries e.g. shortest path. However, many applications have the need for the set version of various path problem i.e. finding paths between multiple source and multiple destination nodes (subject to different kinds of constraints). Further, the sets of source and destination nodes may be described declaratively as patterns, rather than given explicitly. Such queries lead to the requirement of integrating graph pattern matching with path problem solving. There are currently existing limitations in support of such queries (either inability to express some classes, incomplete results, inability to complete query evaluation unless graph patterns are extremely selective, etc). In this paper, we propose a framework for evaluating generalized path queries-gpqs that integrate an algebraic technique for solving path problems with SPARQL graph pattern matching. The integrated algebraic querying technique enables more scalable and efficient processing of gpqs, including the possibility of support for a broader range of path constraints. We present the approach and implementation strategy and compare performance and query expressiveness with a popular graph engine.

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“…Additionally, in [11,25,26] the authors study the structure of the data and provide structural summaries or representative schemas. None of these works is based on a given schema, and thus they require an extensive data scan.…”

Section: Related Workmentioning

confidence: 99%

Selectivity Estimation for SPARQL Triple Patterns with Shape Expressions

Abbas

Genevès

Roisin

et al. 2018

Lecture Notes in Computer Science

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ShEx (Shape Expressions) is a language for expressing constraints on RDF graphs. In this work we optimize the evaluation of conjunctive SPARQL queries, on RDF graphs, by taking advantage of ShEx constraints. Our optimization is based on computing and assigning ranks to query triple patterns, dictating their order of execution. We first define a set of well formed ShEx schemas, that possess interesting characteristics for SPARQL query optimization. We then define our optimization method by exploiting information extracted from a ShEx schema. We finally report on evaluation results performed showing the advantages of applying our optimization on the top of an existing state-of-the-art query evaluation system.

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Type-based Semantic Optimization for Scalable RDF Graph Pattern Matching

Cited by 7 publications

References 21 publications

Semantic locality‐based approximate knowledge graph query

Semantic locality‐based approximate knowledge graph query

Evaluating Generalized Path Queries by Integrating Algebraic Path Problem Solving with Graph Pattern Matching

Selectivity Estimation for SPARQL Triple Patterns with Shape Expressions

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