Semantic-based multi-modal spatio-temporal data association method

Abstract

The invention discloses a semantic-based multi-modal spatio-temporal data association method, and belongs to the technical field of geographic space information systems. According to the method, the constructed ontology model is used for carrying out feature-semantic mapping and conceptualized semantic expression on the data, complex semantic information of the multi-modal spatio-temporal data iseffectively described in a unified format, and the problem that a computer is difficult to understand semantic content information of the data is solved; meanwhile, based on ontology semantic expression of the spatio-temporal data, time association, space association and content object semantic association between the spatio-temporal data are established through three factors of time, space and objects contained in data content, and association between the multi-modal spatio-temporal data is effectively carried out. The method is mainly used for cross-modal retrieval of multi-modal spatio-temporal data, an accurate and comprehensive data link association processing result is provided for a user, and then the cross-modal retrieval performance is improved.

Description

technical field [0001] The invention belongs to the technical field of geographic space information systems, in particular to a semantic-based multi-mode spatio-temporal data association model. Background technique [0002] With the rapid development of Internet technology, the content of spatio-temporal data is increasingly rich, the source is more and more extensive, and the storage format is diversified. Different data are often related to each other. The research on the association query of massive spatio-temporal data is becoming more and more important. Spatiotemporal data is mixed data with spatiotemporal characteristics, diverse types, and complex modalities, including geographic videos, images, vector maps, trajectory data, and positioning data, etc. These data not only contain rich temporal, spatial, and semantic information, but also have multidimensional , multi-scale, multi-temporal characteristics, and multi-modal characteristics. The multi-modal characteristic...

AI Technical Summary

Problems solved by technology

[0008] The above data semantic association methods are mainly aimed at ordinary text, image and video data, etc., only considering the characteristics of data content, and cannot be fully applied to multi-modal spatio-temporal data with time, space and rich content semantics

Ontologies can effectively express and model the semantic information of data, but the research on using ontology to build cross-media knowledge graphs has just begun, and there is little research on ontology models for establishing semantic associations for multi-modal spatio-temporal data.

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