Structural extraction method and system of positional information based on semantic position model

Abstract

The invention discloses a structural extraction method and system of positional information based on a semantic position model. The method includes establishing a positional basic concept ontology, a positional physical concept ontology based on the semantic position model and filling ontology instance; classifying the positional basic concept ontology and the positional physical concept ontology to establish corresponding positional structure concept ontologies; automatically mapping the location structuring concept ontologies to generate rule classes, each rule class corresponds to one positional physical concept ontology and is inheriting and greedy; instantiating all the rule classes to generate rule objects, and to finally generate rule sets; classifying the rule sets, re-classifying them into a plurality of rule groups to form a rule retrieval dependency graph; further establishing a reverse retrival between the concept ontology and the rules of each rule group; matching positional descriptive graphs by using a rule matching algorithm to match and finish structuring extraction of positional information.

Description

technical field [0001] The invention belongs to the technical field of natural language location extraction, in particular to a method and system for structured extraction of location information based on a semantic location model. Background technique [0002] Natural language location extraction technology has always been one of the key research issues in the field of natural language understanding. Its main content is the identification of location names and location relationships. There are two main methods: one is based on regular expression rules, and the other is One is the Geographical information retrieval method. [0003] The method based on regular expression rules needs to establish a corpus and construction rules of geographic named entities or place names, and uses rule matching to identify them. It has strict requirements on concept construction rules and can improve the accuracy of the extraction results, but its shortcomings are also obvious. First, because...

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Problems solved by technology

The position analysis and matching work for Chinese ubiquitous information was carried out relatively late. Because the grammatical structure of Chinese is obviously different from that of English, researchers generally need to use pre-segmentation to deal with it, and word segmentation itself is also difficult due to ambiguity. will introduce bugs that can cause further problems in post-processing

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