Semantic understanding based space keyword indexing method

Abstract

The present invention discloses a semantic understanding based space keyword indexing method. The method comprises the following steps: constructing an index structure of a space text object; initializing a priority queue in the index structure; reading out a first index node in the priority queue; reading the index structure to obtain a child node set of the first index node when the first index node is a non-leaf node; inserting the child node set into the priority queue according to a mode of realizing an ascending sequence with the lower bounding distance of a synthetic distance of a query point; accessing a semantic layer and a text layer corresponding to a leaf node to obtain a semantic candidate space text object set and a text candidate space text object set when the first index node is the leaf node; updating a result upper bound of the text candidate space text object set; and ending query when the result upper bound is less than a synthetic distance lower bound or the priority queue is empty. By adopting the semantic understanding based space keyword indexing method, space keywords can be indexed according to the semantic understanding of texts, so that indexing results are more accurate.

Description

technical field [0001] The invention relates to the technical field of information query, in particular to a spatial keyword indexing method based on semantic understanding. Background technique [0002] With the widespread use of mobile communication devices such as smart phones and the rapid development of mobile communication technology, the mobile Internet and its applications have shown a blowout development in recent years. Subsequently, various location-based services represented by Baidu Maps have become popular tools, profoundly changing people's work and lifestyle. In the location-based service system, spatial keyword query is a universal core technology, which returns the most relevant spatial object text to the query location and text description, and is used to retrieve the services and objects required by users in mobile applications. For example, a user can enter the keyword "hot pot" in Baidu Maps, and the search engine will consider the text and location in...

AI Technical Summary

Problems solved by technology

[0003] However, in terms of spatial keyword similarity, the existing technology for text measurement is only based on glyph evaluation, which cannot support the semantic understanding of text, so it is difficult to feed back the services and objects that users really need

For example, when a user enters the keyword "mocha coffee", the traditional spatial keyword engine cannot match it to the nearby "Starbucks coffee shop" because the text similarity based on glyphs is low

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