多模态知识图谱在农业中的研究进展

doi:10.19788/j.issn.2096-6369.220320

Abstract

Abstract:

Incorporating entities of multiple modalities and their semantic relationships on the basis of traditional knowledge graph, multimodal knowledge graph provides important information in the form of text, image and sound. It plays an important role in eliminating ambiguity and supplementing visual knowledge. In recent years, under the background of the rapid development of agricultural informatization and intelligence, knowledge graph technology has attracted extensive attention. In this article, the concepts of knowledge graph and multimodality are introduced in detail. Meanwhile, technical methods such as multimodal representation learning are elaborated from the perspective of graph construction. For the applications of multimodal knowledge graph in agriculture, we focus on the research of agricultural intelligent question answering system, plant diseases and pests’ identification, agricultural product recommendation and so on. At the same time, the challenges in construction and development of agricultural multimodal knowledge graphs are prospected and analyzed.

Key words: multi-model, knowledge graph, question answering, pest identification, agriculture informatization

CLC Number:

Q939

Jiayun Chen, Xiangying Xu, Yonglong Zhang, Ye Zhou, Hongjiang Wang, Changwei Tan. Research Progress of Multimodal Knowledge Graph in Agriculture[J].Journal of Agricultural Big Data, 2022, 4(3): 126-134.

Figures/Tables 3

References 52

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Research Progress of Multimodal Knowledge Graph in Agriculture

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