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A Distributed Parallel Reasoning Method for RDF Data Combined with Rete Algorithm

A reasoning method and distributed technology, applied in the field of semantic web, can solve problems such as unable to meet the needs of massive data, unable to process data reasoning, reasoning is not efficient enough, etc., to achieve efficient reasoning, obvious effects, and far-reaching effects

Active Publication Date: 2017-12-08
FUZHOU UNIV
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Practical in a centralized environment cannot meet the needs of massive data, while reasoning in a distributed environment is not efficient enough, parallelization of reasoning
Although the Rete algorithm in a centralized environment can efficiently implement data reasoning, it cannot directly handle large-scale data reasoning in a centralized environment because it requires a large amount of memory when executing tasks in the alpha and beta stages.

Method used

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  • A Distributed Parallel Reasoning Method for RDF Data Combined with Rete Algorithm
  • A Distributed Parallel Reasoning Method for RDF Data Combined with Rete Algorithm
  • A Distributed Parallel Reasoning Method for RDF Data Combined with Rete Algorithm

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example

[0036]Instance triples refer to subject and object, which are usually not found in ontology files, and are specific instances: for example, instance triples (s2, p2, o2):

[0037] Subject S2: http: / / www.Department0.University0.edu / AssistantProfessor4 / Publication5

[0038] Predicate P2: http: / / swat.cse.lehigh.edu / onto / univ-bench.owl#publicationAuthor

[0039] Object O2: http: / / www.Department0.University0.edu / GraduateStudent41

example 3

[0040] The instance triplet is a specific instance, for example, the subject S2 of the instance triplet is Publication5; Publication5 is the specific instance of Publication in the pattern triplet

[0041] S12: Set the key (key) output by the Map stage as the rule name, and the value (value) is an instance triplet that satisfies the antecedent of the corresponding RDFS / OWL rule; if the antecedent satisfied by an instance triplet data is For the antecedents of multiple rules, different keys (keys) are used to redundantly store the instance triplet data, and the output of each value (value) includes the instance triplets in the antecedents that satisfy the corresponding RDFS / OWL rule; In this embodiment, the rule rule1:p rdfs:domain x & s p o=>s rdf:type x, if the input RDF triple data (s1,p1,o1) satisfies the condition "p rdfs:domain x", after After the Map stage, the output here is .

[0042] In order to ensure the correctness of the reasoning results, it is necessary to red...

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Abstract

The invention relates to an RDF data distributed parallel inference method combined with a Rete algorithm. Under the MapReduce algorithm framework, the Rete algorithm is combined for parallel inference. When the Rete algorithm is combined, RDF data are not simply partitioned, the function of an alpha network is completed in the Map stage, and the function of a beta network is completed in the Reduce stage. According to the RDF data distributed parallel inference method combined with the Rete algorithm, the one-time inference for all RDFS / OWL rules can be completed just by starting one MapReduce inference task, and the efficient inference for the mass RDF data can be achieved iteratively through multiple inference tasks.

Description

technical field [0001] The invention relates to the technical field of semantic web, in particular to a distributed parallel reasoning method for RDF data combined with Rete algorithm. Background technique [0002] The Sematic Web is an extension and extension of the World Wide Web. Currently, the ontology standards specified by the World Wide Web Consortium (W3C) mainly include RDF / RDFS and OWL. With the application of the Sematic Web, a large amount of semantic information has been generated. Due to the complexity and large-scale nature of the data, how to efficiently discover the hidden information in it through semantic information parallel reasoning is an urgent problem to be solved. At present, the centralized environment can no longer meet the needs of large-scale data; and the distributed environment can realize the reasoning of large-scale data. At present, there has been a lot of work on reasoning in a distributed environment. Such as fuzzy pD* reasoning, ALC lo...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30
CPCG06F16/24532G06F16/951
Inventor 汪璟玢郑翠春
Owner FUZHOU UNIV
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