关键词: 食品贮藏, 知识图谱, 特征融合, 多元关系表示, 知识融合

Abstract: Food storage is the key link in people’s pursuit of high-quality diet and the focus of building a resource-saving society. However, there are problems of information overload, lack of systematic representation and difficulty with querying in the huge amount of multi-source heterogeneous food data, which restrict the research and development of relevant practitioners and the effectiveness of information access for the general population. Based on the characteristics of structurized semantic information expressed in the knowledge graph, this paper integrates it with food storage and designs a complete framework for the construction of knowledge graph in the field of food storage, which provides new perspectives and intelligent methods for food research and application. Firstly, according to the characteristics of multi-source and heterogeneous food storage data and ontology construction method, this paper proposes the expression model of multi-entity relationship, which makes up for the deficiency of knowledge representation of triples, and completes the schema layer construction. Secondly, this paper focuses on the unstructured knowledge by using an improved named entity recognition method that combines multiple features to complete the entity extraction, and uses the multi-classification model and the proposed multi-relationship extraction algorithm to complete the relationship extraction. Knowledge fusion is completed by using the methods of dictionary-based and similarity matching. Finally, the Neo4j is applied to the database stores. The rationality and effectiveness of the entire framework are verified by experiments. Moreover, visual analysis and semantic search are completed on the successfully constructed knowledge graph.

Key words: food storage, knowledge graph, feature fusion, multi-relation representation, knowledge fusion