AgriKG: An Agricultural Knowledge Graph and Its Applications

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11448)


Recently, with the development of information and intelligent technology, agricultural production and management have been significantly boosted. But it still faces considerable challenges on how to effectively integrate large amounts of fragmented information for downstream applications. To this end, in this paper, we propose an agricultural knowledge graph, namely AgriKG, to automatically integrate the massive agricultural data from internet. By applying the NLP and deep learning techniques, AgriKG can automatically recognize agricultural entities from unstructured text, and link them to form a knowledge graph. Moreover, we illustrate typical scenarios of our AgriKG and validate it by real-world applications, such as agricultural entity retrieval, and agricultural question answering, etc.



This work has been supported by the National Key Research and Development Program of China under grant 2016YFB1000905, and the National Natural Science Foundation of China under Grant No. U1811264, 61877018, 61672234, and 61502236. It has been also supported by the Shanghai Agriculture Applied Technology Development Program, China (Grant No. T20170303).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Data Science and EngineeringEast China Normal UniversityShanghaiChina
  2. 2.Key Laboratory of Advanced Theory and Application in Statistics and Data Science - MOEShanghaiChina
  3. 3.Shanghai Jiao Tong UniversityShanghaiChina
  4. 4.Keydriver IncShanghaiChina

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