Query rewriting and semantic annotation in semantic-based image retrieval under heterogeneous ontologies of big data

Abstract

In the era of big data, it is of great significance to retrieve the semantic features from images by big data technique. However, most semantic query models perform poorly in actual images, which are distributed heterogeneously. Image ontology mapping provides a solution to the problem. This paper applies the H-Match algorithm to find the mapping relationship between image ontologies in peer-to-peer (P2P) environment, and rewrite user queries for heterogeneous image ontologies. The H-Match algorithm was developed under the framework called Helios evolving interaction-based ontology knowledge sharing (Helios). The weights of semantic annotation were calculated by a novel method, involving word frequency, position and feedback. The research results have great application potentials in various fields.