Construction and Application of the Knowledge Graph in Endangered Plants

Haochuan Wei, Qianchi Zhang, Weixuan Gao, Xianghao Meng

Abstract: The current network information of endangered plants is scattered, making acquiring and reusing relevant knowledge difficult. The endangered plants' knowledge graph ePlantKG constructed in this paper can form a visual semantic information network. The data sources in this paper include China Rare and Endangered Plant Information Network, Baidu Encyclopedia, Wikipedia, and Kuaiming Encyclopedia. Firstly, we use a Python crawler to obtain network data and preprocess them. Then we use the obtained structured data combined with previously constructed plant ontology to define and build new ontologies. Next, through the rule-based method, we extract triples from semi-structured and unstructured data, fuse knowledge between heterogeneous data, and store them in the Neo4j database to form ePlantKG. Finally, we design and build a knowledge service platform to illustrate knowledge graphs and intelligent question answering. The intelligent question answering algorithm extracts features from the user's input text with TF-IDF and classifies questions with Naive Bayes. After realizing the similarity matching between entities and relations, we retrieve answers with the returned Cypher statement. The ePlantKG records 1926 species of endangered plants and 37860 species of common plants, and the image filling rate of endangered plants is more than 99 %. The platform implements several functions, e.g., graph display, entity recognition, and intelligent question answering. This paper realizes the information sharing and reuses on endangered plants, providing a method reference for applying knowledge graph in forestry intelligent question answering system and forestry big data analysis.

External IDs:dblp:conf/ACISicis/WeiZGM22