Context-Free Path Queries on RDF Graphs

Zhang, Xiaowang; Feng, Zhiyong; Wang, Xin; Rao, Guozheng; Wu, Wenrui

doi:10.1007/978-3-319-46523-4_38

Cited by 30 publications

(30 citation statements)

References 32 publications

(84 reference statements)

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“…Query 1 is based on the grammar for retrieving concepts on the same layer (presented in figure 6). For this query our algorithm demonstrates up to 1000 times better performance and provides identical results as compared to the presented in [22] for Q1.…”

Section: Ontology Queryingmentioning

confidence: 67%

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Context-free path querying with structural representation of result

Grigorev

Ragozina

2017

Proceedings of the 13th Central &Amp; Eastern European Software Engineering Conference in Russia

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Graph data model and graph databases are very popular in various areas such as bioinformatics, semantic web, and social networks. One specific problem in the area is a path querying with constraints formulated in terms of formal grammars. The query in this approach is written as grammar, and paths querying is graph parsing with respect to given grammar. There are several solutions to it, but how to provide structural representation of query result which is practical for answer processing and debugging is still an open problem. In this paper we propose a graph parsing technique which allows one to build such representation with respect to given grammar in polynomial time and space for arbitrary context-free grammar and graph. Proposed algorithm is based on generalized LL parsing algorithm, while previous solutions are based mostly on CYK or Earley algorithms, which reduces time complexity in some cases

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Section: Ontology Queryingmentioning

confidence: 67%

“…One of classical graph querying problems is a navigation queries for ontologies, and we apply our algorithm to this problem in order to estimate its practical value. We used dataset from paper [22]. Our algorithm is aimed to process graphs, so RDF files were converted to edge-labeled directed graph.…”

Section: Ontology Queryingmentioning

confidence: 99%

Context-free path querying with structural representation of result

Grigorev

Ragozina

2017

Proceedings of the 13th Central &Amp; Eastern European Software Engineering Conference in Russia

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“…Context Free Language & Graph Query: Parsing CFL on graphs and using it as query primitives has been studied in early theory work [40], [61], later used widely in programming analysis [41] and other domains such as bioinformatics [62] which requires high expressiveness language to constrain paths. Recently it is discussed as a graph query language [63] and SPARQL extension [64] in graph databases. In particular, CFLR is a general formulation of many program analysis tasks on graph representations of programs.…”

Section: Related Workmentioning

confidence: 99%

Understanding Data Science Lifecycle Provenance via Graph Segmentation and Summarization

Miao

Deshpande

2019

2019 IEEE 35th International Conference on Data Engineering (ICDE)

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Along with the prosperous data science activities, the importance of provenance during data science project lifecycle is recognized and discussed in recent data science systems research. Increasingly modern data science platforms today have nonintrusive and extensible provenance ingestion mechanisms to collect rich provenance and context information, handle modifications to the same file using distinguishable versions, and use graph data models (e.g., property graphs) and query languages (e.g., Cypher) to represent and manipulate the stored provenance/context information. Due to the schema-later nature of the metadata, multiple versions of the same files, and unfamiliar artifacts introduced by team members, the "provenance graph" is verbose and evolving, and hard to understand; using standard graph query model, it is difficult to compose queries and utilize this valuable information.In this paper, we propose two high-level graph query operators to address the verboseness and evolving nature of such provenance graphs. First, we introduce a graph segmentation operator, which queries the retrospective provenance between a set of source vertices and a set of destination vertices via flexible boundary criteria to help users get insight about the derivation relationships among those vertices. We show the semantics of such a query in terms of a context-free grammar, and develop efficient algorithms that run orders of magnitude faster than state-of-the-art. Second, we propose a graph summarization operator that combines similar segments together to query prospective provenance of the underlying project. The operator allows tuning the summary by ignoring vertex details and characterizing local structures, and ensures the provenance meaning using path constraints. We show the optimal summary problem is PSPACE-complete and develop effective approximation algorithms. The operators are implemented on top of a property graph backend. We evaluate our query methods extensively and show the effectiveness and efficiency of the proposed methods.

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“…There are a number of solutions [11,23,30] for context-free path query evaluation w.r.t. the relational query semantics, which employ such parsing algorithms as CYK [13,28] or Earley [10].…”

Section: Related Workmentioning

confidence: 99%

Context-free path querying by matrix multiplication

Azimov

Grigorev

2018

Proceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data Management Experiences &Amp; Systems (GRADES) and

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Graph data models are widely used in many areas, for example, bioinformatics, graph databases. In these areas, it is often required to process queries for large graphs. Some of the most common graph queries are navigational queries. The result of query evaluation is a set of implicit relations between nodes of the graph, i.e. paths in the graph. A natural way to specify these relations is by specifying paths using formal grammars over the alphabet of edge labels. An answer to a context-free path query in this approach is usually a set of triples (A, m, n) such that there is a path from the node m to the node n, whose labeling is derived from a non-terminal A of the given context-free grammar. This type of queries is evaluated using the relational query semantics. Another example of path query semantics is the single-path query semantics which requires presenting a single path from the node m to the node n, whose labeling is derived from a non-terminal A for all triples (A, m, n) evaluated using the relational query semantics. There is a number of algorithms for query evaluation which use these semantics but all of them perform poorly on large graphs. One of the most common technique for efficient big data processing is the use of a graphics processing unit (GPU) to perform computations, but these algorithms do not allow to use this technique efficiently. In this paper, we show how the contextfree path query evaluation using these query semantics can be reduced to the calculation of the matrix transitive closure. Also, we propose an algorithm for context-free path query evaluation which uses relational query semantics and is based on matrix operations that make it possible to speed up computations by using a GPU.

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Context-Free Path Queries on RDF Graphs

Cited by 30 publications

References 32 publications

Context-free path querying with structural representation of result

Context-free path querying with structural representation of result

Understanding Data Science Lifecycle Provenance via Graph Segmentation and Summarization

Context-free path querying by matrix multiplication

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