Search method and search engine

Abstract

The invention provides a search method. The search method comprises the following steps of: S1, receiving a query command; S2, performing demand intention analysis on the query command based on a knowledge base, and defining the demand intention of the query command; S3, searching the query command carrying the demand intention in a database to obtain a search result; and S4, outputting the search result. Compared with the prior art, the search method has the advantages that: on the basis of the knowledge base, the query command input by a user is understood better; the intention of the query command is analyzed; the structure of the query command is analyzed to perform semantic content expansion on the query command so as to better guide a search engine to select quality resources to meet the search requirements on the user; therefore, the search efficiency of the user is improved and the network traffic is saved.

Description

Search method and search engine technical field The invention relates to search engine technology, in particular to a search method and a search engine for performing demand analysis and parsing of query instructions based on a knowledge base. Background technique With the rapid growth of information on the Internet, the network is flooded with more and more redundant information. For Internet users who search for the information they need on the Internet, facing these endless information is undoubtedly like finding a needle in a haystack. The emergence of search engines has undoubtedly brought great convenience to users' search needs to a certain extent. A search engine is a software system applied on the network. It collects and discovers information on the network with a certain strategy, and after processing and organizing the information, it provides users with information search services on the Internet. Usually, this kind of software system provides a web interface...

AI Technical Summary

Problems solved by technology

Existing search engines mostly understand the query commands input by users based on the characters of the query commands. For example, if the query command input by the user is "Nokia mobile phone", based on the existing search engines, the query commands can only be word-segmented into "Nokia" and "Mobile phone", and use the word segmentation result to search in the webpage database index, input the webpage Url whose text includes "Nokia" and "mobile phone", and form the search result, but this kind of search engine cannot understand the content of the user's query instruction Understanding at the semantic level, for example, if the user enters a query command of "Nokia mobile phone", it cannot understand this query command as "Nokia" as a brand in "mobile phone"; of course, it cannot understand the needs of the query command Intent, as well as the structure of the query instruction, cannot expand the semantic content of the query instruction, etc.

For query commands with diverse expression forms and demand intentions input by users, the existing character-based search engines can no longer better meet the needs of users, resulting in incomplete search, which requires multiple input of different query commands. It is possible to find the search results you need, the search efficiency is low, and the problem of wasting network resources

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