Research on knowledge graph-based aviation multi-modal data organization and discovery method
The data generated in the life cycle of aviation products shows the characteristics of multi-source and multi-modal.When constructing knowledge engineering for such data,the traditional text retrieval method based on keywords has become inadequate to meet the needs of researchers for knowledge acquisition in the process of scientific research.As a current method for knowledge representation in the specific domain of artificial intelligence,knowledge graph provides an adequate expression and standardized storage of the systematic and relational aspects between knowledge units and data.It served as an effective means of organizing domain-specific data and facilitating knowledge discovery services.Therefore,to reconstruct the operational thinking of researchers,it was proposed to use the knowledge graph as the knowledge expression model in the aviation domain,employing the standard knowledge unit as data carriers.Furthermore,the deep neural network was utilized as the feature encoder of multi-modal data,generating feature vectors for machine understanding and computation.By combining these two key technologies,a search and recommendation engine was built for multi-modal data.Building upon this technological foundation,a system architecture was designed and a knowledge discovery platform was implemented.This platform organized and indexed multi-modal data at the knowledge level,meeting the multi-modal knowledge retrieval needs of aviation researchers.
data organizationknowledge discoveryknowledge graphmulti-modalcross-modal retrieval
万方数据
维普
