Rule-based Spark distributed elastic semantic flow reasoning method

A reasoning method and distributed technology, applied in the field of data processing, can solve problems such as further improvement in efficiency

Active Publication Date: 2021-07-30
CHONGQING JIAOTONG UNIVERSITY
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However, most of them are parallel reasoning methods based o

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  • Rule-based Spark distributed elastic semantic flow reasoning method
  • Rule-based Spark distributed elastic semantic flow reasoning method
  • Rule-based Spark distributed elastic semantic flow reasoning method

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Embodiment Construction

[0036] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0037] Such as figure 1 and figure 2 As shown, the present invention discloses a rule-based Spark distributed elastic semantic flow reasoning method, comprising:

[0038] S1. Obtain an RDF graph;

[0039] S2. Construct a bidirectional dictionary for the identifiers of the RDF graph;

[0040] S3. Dividing the RDF graph to obtain a schema graph model and an instance graph model;

[0041] S4. Using a bidirectional dictionary to design a corresponding key-value model based on the schema graph model and the instance graph model;

[0042] S5. The rule-based Spark distributed elastic semantic flow inference engine module reads the pattern data and instance data in the pattern graph model and the instance graph model, and executes the Spark job according to the optimized order of the RDFS rules.

[0043] The present invention utilizes the Spark Streaming stream...

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Abstract

The invention discloses a rule-based Spark distributed elastic semantic flow reasoning method. The method comprises the following steps: S1, obtaining an RDF graph; S2, constructing a bidirectional dictionary for the identifiers of the RDF graph; S3, dividing the RDF graph to obtain a mode graph model and an instance graph model; S4, designing a corresponding key value model based on the mode graph model and the instance graph model by using the bidirectional dictionary; and S5, reading mode data and instance data in the mode graph model and the instance graph model by a rule-based Spark distributed elastic semantic flow inference engine module, and executing the Spark operation according to the optimization sequence of the RDFS rule. Compared with the prior art, the distributed memory calculation model Spark is adopted, operation facing a distributed data set is abstracted into operation facing a local data set, the efficiency of iterative calculation in a big data processing environment is further improved, distributed memory RDFS reasoning is achieved, and the RDFS reasoning efficiency and expandability can be further improved.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a rule-based Spark distributed elastic semantic flow reasoning method. Background technique [0002] The rapid development of the Semantic Web has made it possible for people to obtain a large amount of knowledge graph RDF data. However, most traditional reasoners are designed based on a centralized architecture, which makes traditional reasoners perform poorly in terms of performance and scalability, which inevitably limits the possibility of processing web-scale data. [0003] Efficient RDF Schema (RDFS) reasoning and SPARQL query over large amounts of RDF data in a distributed environment is a key and challenging task in the field of Semantic Web. Currently, researchers have proposed several distributed query and reasoning methods for large-scale RDF data. However, most of them are parallel reasoning methods based on MapReduce, and the efficiency needs to be further ...

Claims

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Application Information

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IPC IPC(8): G06N5/04G06N5/02G06F16/36
CPCG06N5/04G06N5/022G06F16/367
Inventor 李韧张露伊杨建喜王桂平
Owner CHONGQING JIAOTONG UNIVERSITY
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