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Real-time query method and system for large-scale knowledge map under memory constraint

A knowledge map and large-scale technology, applied in the field of data processing, can solve the problems of wasting user time, computing power cannot keep up with the growth rate of knowledge map, and difficult query processing, so as to reduce constraints and save query time.

Active Publication Date: 2018-12-18
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The computing power of ordinary computing devices is far behind the growth rate of knowledge graphs, and it is becoming more and more difficult for ordinary users to query and process them
For example, freebase is about 380G. At present, the memory of ordinary users is about 8G. Ordinary PC users directly perform queries on it, which will generate a large number of I / O operations, which greatly wastes user time.

Method used

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  • Real-time query method and system for large-scale knowledge map under memory constraint

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Embodiment 1

[0056] see figure 1 , which is a flow chart of a real-time query method for a large-scale knowledge map provided under memory constraints according to Embodiment 1 of the present invention; figure 2 It is a schematic diagram according to the principle of the present invention. As shown in the figure, the real-time query method of a large-scale knowledge map provided by the embodiment of the present invention may include the following steps:

[0057] Step S101: Execute the step of establishing the inverted file hash list, that is, process and analyze the original knowledge graph to obtain the inverted file hash list. Due to the high vocabulary repetition rate in non-numerical large-scale knowledge graphs, the use of inverted files can quickly locate triples based on vocabulary. The purpose of hashing inverted files is to speed up vocabulary search and reduce file I / O operate.

[0058] Step S102: Execute the step of building a multi-level structure index, and build a multi-l...

Embodiment 2

[0064] On the basis of the real-time query method of a large-scale knowledge map under the condition of limited memory provided in Embodiment 1, the process of processing and analyzing the original knowledge map in step S101 to obtain the hash list of the inverted file can be specifically implemented in the following manner :

[0065] Step 1: Extract the tuple information in the form of vocabulary first and then offset in the original knowledge graph. The first vocabulary and then offset form refers to the form of (offset, vocabulary, ..., vocabulary), that is, the tuple in the form of (offset, vocabulary, ..., vocabulary) extracted from the original knowledge graph in step 1 information.

[0066] Step 2: Convert the extracted tuple information into a form of vocabulary first and then offset. The first vocabulary and then the offset form refers to the form of (vocabulary, offset, ..., offset), that is, the tuple information in the form of (offset, vocabulary, ..., vocabulary...

Embodiment 3

[0078] On the basis of the real-time query method of large-scale knowledge graph under the condition of limited memory provided in Embodiment 2, the process of constructing a multi-level structure index based on the original knowledge graph in step S102 can be specifically implemented in the following manner:

[0079] The present invention separates the ontology layer of the original knowledge map to obtain a preliminary structure discovery, and then constructs a multi-level index structure, including three parts: deep analysis of the knowledge map structure, establishment of a knowledge map storage node index, and establishment of an overall structure index.

[0080] (1) In-depth analysis of knowledge graph structure: Data classification, cleaning, and simplified data representation are performed on the preliminary structure discovery results of knowledge graph to obtain data classification and simplification results of knowledge graph; the data simplified representation is to ...

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Abstract

The invention relates to the technical field of data processing, and provides a real-time inquiry method and system of a large-scale knowledge map under the condition of limited memory. The method comprises the following steps: processing and analyzing the original knowledge map to obtain an inverted file hash list; constructing a multilevel structure index based on an original knowledge map; obtaining a target vocabulary by parsing the query sentence, and generating the result sub-graph by searching for the triple corresponding to the target vocabulary according to the inverted file hash listand the multi-level structure index. The invention greatly improves the knowledge map query ability of a single computer, and can provide a result set which meets both the user time requirement and the user precision requirement under the condition that the memory is extremely limited.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a real-time query method and system for a large-scale knowledge map under the condition of limited memory. Background technique [0002] The World Wide Web has formed a huge network from its birth to now, and its nodes are composed of web pages one by one, and the web pages are related to each other through hyperlinks. Based on the simple and open technology of the World Wide Web, modern search engine technology can search for relevant web pages in the huge network space. However, due to the development of the mobile Internet and the limited screen space of mobile devices, users expect search engines to obtain accurate results instead of searching one by one in the search results. Due to the accuracy requirements of users, the storage of web pages alone is no longer sufficient. [0003] In order to solve this requirement, XML (Extensible Markup Language), RDF (Resource ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06F17/27
CPCG06F40/284
Inventor 王宏志万晓珑高宏
Owner HARBIN INST OF TECH
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