A Multi-keyword Indexing Method Based on Locality Sensitive Hash on Graph

Abstract

The invention discloses a locality sensitive hashing based indexing method for multiple keywords on graphs and belongs to the technical field of graph data management. According to the method, the query on the multiple keywords on the graphs is supported by double-layer indexes; after a plurality of graphs are clustered in an n-gram space according to vertex keywords, an upper-layer bitmap and lower-layer locality sensitive hashing tables are constructed according to a clustered structure; the upper-layer bitmap realizes graph-to-cluster mapping according to n-gram (a character string formed by n continuous letters) of coarse particle size contained by the keywords; each cluster of a lower layer corresponds to a locality sensitive hashing table, and buckets of the hashing tables contain candidate graphs corresponding to n-gram of fine particle size. The method has the following advantages that query I / O and the number of the keywords are independent, the I / O times of the query on the multiple keywords are remarkably reduced, and the speed of query is increased; n-gram of different particle sizes is combined, so that the sensitivity of the indexes to spelling mistakes is effectively avoided, and the result of probability return expectation is increased.

Description

technical field [0001] The invention relates to a multi-keyword indexing method based on a local sensitive hash on a graph, which belongs to the technical field of graph data management. Background technique [0002] In recent years, fields such as the World Wide Web, social networks, biomedicine, and molecular structures of compounds have accumulated a large amount of associated and complex data, and their structures are usually abstracted as graphs. In order to achieve information acquisition and analysis, keyword query on the graph is a fundamental problem. At present, there are the following problems in the keyword query on the graph: (1) Due to the complex relationship between the graph data, multiple keywords are often involved in the query analysis. At present, the graph keyword index is mainly based on the inverted list and its variants. It is necessary to sequentially read the candidate graphs corresponding to each (pair) of keywords. I / O increases linearly with th...

AI Technical Summary

Problems solved by technology

At present, there are the following problems in the keyword query on the graph: (1) Due to the complex relationship between the graph data, multiple keywords are often involved in the query analysis

[0005] The above two aspects of work did not involve how to effectively support multi-keyword queries, reduce disk I / O, and did not involve the problem of being overly sensitive to keyword spelling errors when querying

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