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RDF data distributed semantic parallel reasoning method

A reasoning method and distributed technology, applied in the field of Semantic Web, can solve the problems of too many MapReduce tasks to start, inefficient inference, inability to meet the needs of massive data, etc.

Active Publication Date: 2016-08-31
FUZHOU UNIV
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Problems solved by technology

[0004] The centralized environment cannot meet the needs of massive data, while the reasoning in the distributed environment is not efficient enough, and the parallelization of reasoning
At present, although the distributed reasoning engine can realize data parallel reasoning, the number of MapReduce tasks is large and time-consuming, and there are too many redundant calculations and useless data, which makes it unable to be efficient when the amount of data increases. And correctly implement the reasoning of RDFS / OWL rules

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  • RDF data distributed semantic parallel reasoning method
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  • RDF data distributed semantic parallel reasoning method

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Embodiment Construction

[0096]The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0097] This embodiment provides a distributed semantic parallel reasoning method for RDF data, such as figure 1 As shown, it specifically includes the following steps:

[0098] Step S1: Load the pattern triplet, construct the TRM, and construct the connection variable information that may be connected in each rule according to the RDFS / OWL rules;

[0099] Step S2: Generate a rule labeling model according to the TRM and connection variable information;

[0100] Step S3: Divide connection variables into univariate and multivariate forms, divide RDFS / OWL rules into 5 types according to the type of TRM and connection variables, and design different reasoning schemes respectively;

[0101] Step S4: To Flag_Rule m = 1 executes parallel reasoning of RDFS / OWL rules and outputs intermediate results;

[0102] Step S5: delete repeated triplets in the intermedia...

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Abstract

The invention relates to an RDF data distributed semantic parallel reasoning method. The method comprises the steps of firstly, according to an ontology file and RDFS / OWL rules, constructing a transitive closure relation matrix (TRM for short) and link variable information, and generating rule marks; and secondly, classifying the RDFS / OWL rules according to the types of link variables, designing different reasoning schemes separately, and finishing reasoning of the RDFS / OWL rules in parallel in combination with a MapReduce computing framework. A living example triple is filtered through the link variable information and the rule marks, so that the transmission loss of a large amount of useless triple data in a distributed system can be reduced. Through constructing the TRM, the iterative frequency of reasoning can be reduced and the efficiency of reasoning can be improved. Finally, repeated triple data is deleted in real time according to a reasoning result to further improve the efficiency of subsequent iterative reasoning. Through the method, the reasoning of the RDFS / OWL rules can be efficiently and correctly realized under the condition that the data volume is increased.

Description

technical field [0001] The invention relates to the technical field of semantic web, in particular to a distributed semantic parallel reasoning method for RDF data. Background technique [0002] The RDF and OWL standards in the Semantic World Wide Web have been widely used in various fields, such as general knowledge (DBpedia), medical life sciences (LODD) and bioinformatics (UniProt). As of September 2014, the total amount of data has reached 65 billion triples. With the rapid growth of data in the Semantic Web, the centralized environment is not suitable for reasoning on large-scale data due to memory limitations; research on RDFS / OWL distributed parallel reasoning is a relatively new field at present. Such as fuzzy pD* reasoning, RDFS parallel reasoning, distributed rule matching system, distributed reasoning engine WebPIE, distributed reasoning engine YARM. These methods of reasoning are not efficient enough. Most of these reasoning solutions combine the MapReduce com...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06F17/27
CPCG06F16/24564G06F40/30
Inventor 汪璟玢叶怡新郑翠春
Owner FUZHOU UNIV
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