Construction of Multi-modal Knowledge Graph for Logging Field
In order to solve the problems of heterogeneous data sources,difficult complementarity and fusion between data,which cannot be well applied to risk assessment,interpretation evaluation and decision-making knowledge provision,a multi-modal knowledge graph construction method for logging field is proposed.From the perspective of logging,the proposed method classifies knowledge into general knowledge,regional knowledge and auxiliary knowledge in a top-down way.By combining multi-modal data such as Chinese text,pictures,audio and video in logging interpretation process,the entity attribute relationship is deeply mined,and the ontology layer of logging domain is built,and based on CasRel entity relation joint extraction,cosine similarity multi-modal knowledge fusion and TransR multi-modal representation learning technology,the multi-modal knowledge graph in logging field is constructed.The practical verification of Daqing test service branch shows that the proposed multi-modal knowledge graph can effectively enhance the integration,interconnection and sharing of logging knowledge.
well loggingknowledge graphmulti-modalknowledge fusionknowledge representation
国家科技期刊平台
NSTL
万方数据
