Abstract
Water affair data mainly consists of structured data and unstructured data, and the storage methods of data are diverse and heterogeneous. To meet the needs of water affair information integration, a method of constructing a knowledge graph using a combination of water affair structured and unstructured data is proposed. To meet the needs of a water information search, an information recommendation system for constructing a water affair knowledge graph is proposed. In this paper, the edit distance algorithm and latent Dirichlet allocation (LDA) algorithm are used to construct a water affair structured data and unstructured data combination knowledge graph, and this graph is validated based on the semantic distance algorithm. Finally, this paper uses the recall rate, accuracy rate, and F comprehensive results to compare the algorithms. The evaluation results of the edit distance algorithm and the LDA algorithm exceed 90%, which is greater than the comparison algorithm, thus confirming the validity and accuracy of the construction of a water affair knowledge graph. Furthermore, a set of water affair verification sets is used to verify the recommendation method, which proves the effectiveness of the recommended method.
Highlights
With the development of water affair information, water affair data have the problems of multisource heterogeneity and a large quantity
This section uses the top-level knowledge graph of water affairs in Section 2.1.1 and the water affair structured monitoring data and unstructured text in Section 3.2.1 to complete the construction of a water affair knowledge graph
This paper evaluates the edit distance algorithm in water structured data mapping and the latent Dirichlet allocation (LDA) text classification algorithm in water text mapping
Summary
With the development of water affair information, water affair data have the problems of multisource heterogeneity and a large quantity. A knowledge graph is essentially a semantic network; it is a graph-based data structure that consists of nodes and edges. In contrast to the previous ontology [2], which focused on the upper and lower relations, the knowledge graph focuses on the semantic relationship. To put it when the semantic relationship is merged into the ontology, a knowledge graph is formed. The pattern layer of the knowledge graph is similar to the relationship and structure between concepts in the ontology. The entity relationship network formed by putting all of the data of the data layer and the structural relationship of the pattern layer together is the knowledge graph
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