Cross-ontology attribute chain reasoning method based on cloud platform

Abstract

The invention discloses a cross-ontology attribute chain reasoning method based on a cloud platform. The method comprises the following steps that: (1) utilizing a semantic connection method obtain a mapping knowledge domain H of a uniform format; (2) utilizing the mapping knowledge domain to obtain an attribute chain reasoning network C; (3) replacing an object and an attribute with corresponding id, and forming a mapping knowledge domain H' and an attribute chain reasoning network C'; (4) applying a MapReduce frame, carrying out the concurrent reasoning of the attribute chain on the mapping knowledge domain H' according to the C', and revising and updating the C'; (5) storing a reasoning result in hdfs, and adding the reasoning result into the mapping knowledge domain H'; (6) circulating the (4) and the (5) until no new reasoning facts are generated; and (7) combining reasoning results generated by multiple iterations on the hdfs. By use of the method, cross-ontology mass knowledge concurrent reasoning can be supported, the method exhibits high expansibility, and the method has a good practical value for the application of large-scale semantic data reasoning.

Description

technical field [0001] The invention relates to computer semantic reasoning technology, in particular to a cloud platform-based cross-ontology attribute chain reasoning method. Background technique [0002] With the continuous development of the semantic web, the semantic web description language OWL based on the resource description framework has been widely used in ontology modeling and reasoning in various fields, including life sciences, media information, semantic spatiotemporal data, and social networks. And so on, the semantic data in various fields also showed explosive growth. Take the Linked Open Data project as an example. It proposes the concept of Linked data. Its purpose is to call on people to publish existing data as semantically linked data, so as to interconnect different data sources. So far it has contained more than 295 data sources and 31 billion triple records. [0003] There are many hidden complex relationships among these massive semantic data, an...

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Problems solved by technology

[0004] However, the existing semantic inference engines often lack good scalability and can only process small-scale ontology. With the continuous growth of OWL ontology data, the reasoning engine running in the above-mentioned stand-alone environment needs to load a large amount of ontology data. When performing OWL reasoning on large-scale cross-ontology data, there are problems such as memory overflow, insufficient computing performance and scalability, and traditional semantic reasoning machines have been difficult to handle such a large amount of semantic information.

On the other hand, some parallel reasoning techniques that have been proposed cannot effectively solve the problem of large-scale complex semantic reasoning.

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