Distributed keyword query method based on RDF graph

Abstract

The invention designs a distributed keyword query method based on an RDF graph, and belongs to the field of information retrieval. The method comprises the steps of firstly, converting the RDF data graph into an RDF sentence graph; secondly, by utilizing a conditional depth-first algorithm and a simulated annealing algorithm, segmenting the RDF sentence graph according to two most basic principlesincluding minimum of an edge cut set and data balance between sub-graphs after segmentation; and finally, refining the segmented RDF sentence graph into the RDF data graph, obtaining a vertex cut setof the RDF graph, and by utilizing a reverse search algorithm and a Hadoop distributed computing framework, realizing efficient and fast query of keywords. Under the condition of guaranteeing the atomicity and semantic integrity of RDF data, the limitation of a traditional algorithm on the segmentation efficiency of a large-scale data set is effectively eliminated, and the query efficiency of thekeywords is greatly improved.

Description

technical field [0001] The invention relates to a distributed keyword query method based on an RDF graph, belonging to the field of information retrieval. Background technique [0002] Graph is a ubiquitous data structure widely used in various fields. Keyword query based on RDF graph structure is a current research hotspot, which allows users to obtain efficient query results without using complex structured query language. Most of the current query algorithms are implemented in a centralized environment, that is, keyword queries can only be processed on a single computer. In fact, as the scale of RDF graphs continues to expand, it is very time-consuming to perform keyword queries on a single machine. Therefore, graph processing and storage in a distributed environment has very important theoretical value and practical significance. [0003] At present, the commonly used keyword query technology is to represent RDF data as a directed graph with labels, the vertices in the...

AI Technical Summary

Problems solved by technology

However, the number of vertices in each subgraph divided by this method is the same (or similar), but the number of edges associated with each vertex is different, resulting in data imbalance between subgraphs

At the same time, with the continuous expansion of the graph scale, the limitations of traditional algorithms (such as KL, DFEP, VSEP) on the graph scale make these algorithms unable to meet the demand of explosive data growth.

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