Abstract: In the field of smart city,with the deepening of information technology,many systems generate massive data.Semantic communication among these multi-source heterogeneous data has become one of the important problems to be solved in the deve-lopment of urban intelligent applications.Building knowledge graph is one of the common means to solve the semantic communication of data.After establishing ontology,the construction and evolution of graph entity model becomes the key technology to support various applications.Therefore,how to automatically extend the knowledge entities from constantly updated data sources becomes the primary problem of knowledge graph construction.Some existing knowledge entity generation tools cannot provide sufficient support for data import,and users need to carry out complex preprocessing of source data to convert it into the data format supported by the platform.As a result,the workload of preprocessing is heavy,and the data cannot be updated and increased rapidly.To deal with structured or semi-structured data,this paper proposes an ontology schema mapping-based incremental entity model construction and evolution approach of knowledge graph,which achieves the growth and evolution of instance model as data update.Based on the combination of machine recommendation and human-machine interaction,according to the characteristics of different data sources,the knowledge is extracted and correctly mapped to the concepts in the ontology model.The conti-nuous evolution of the entity model is supported by means of entity alignment and relationship complement.The approach is verified in the knowledge graph construction scenario of enterprise domain.By machine recommendation and prohibiting duplicate checking,efficient and accurate entity generation is realized,which proves the effectiveness of the approach.

Key words: Knowledge graph, Ontology, Schema, Human-machine interaction