SOR: An optimized semantic ontology retrieval algorithm for heterogeneous multimedia big data

Semantic information can express the search intentions of users, and this approach has become an important tool in the field of information retrieval. To support semantic-based multimedia retrieval in big data environment, this paper presents an optimized algorithm called semantic ontology retrieval (SOR), which uses big data processing tools to store and retrieve ontologies from heterogeneous multimedia data. First, the background of semantic extraction and ontology representation for multimedia big data are addressed. Second, the methodology of SOR, including the model definition and retrieval algorithm, is proposed. Third, for parallel processing SOR in distributed nodes, a MapReduce-based retrieval framework is presented. Finally, to achieve high retrieval precision and good user experience, a user feedback scheme is designed. The experimental results illustrate that SOR is suitable for semantic-based retrieval for heterogeneous multimedia big data. (C) 2017 Elsevier B.V. All rights reserved.