Research on the Construction and Optimization of Agricultural Knowledge Graph Information Platform Based on Deep Learning

DOI

10.2991/978-2-38476-553-9_2How to use a DOI?

Keywords

Deep learning; Agriculture; Knowledge map; Information platform; System optimization

Abstract

The conventional agricultural knowledge graph information system employs a ternary triplet linking method based on entity associations to enable question-answering through entity browsing. However, this approach may result in imprecise answers due to unclear user intentions. To address this issue, an optimized agricultural knowledge graph information platform is designed using deep learning methods. First, the overall architecture of the platform is optimized, followed by the design of its major modules. By leveraging deep learning technology, the question-answering process for knowledge graph information is optimized to reduce complexity and enhance efficiency in identifying user intent. The optimization design of the agricultural knowledge graph information platform is accomplished through referential recognition, entity linking, and generating candidate paths for storing the knowledge graph.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xiao Gong
PY  - 2026
DA  - 2026/03/25
TI  - Research on the Construction and Optimization of Agricultural Knowledge Graph Information Platform Based on Deep Learning
BT  - Proceedings of the 2025 4th International Conference on Educational Science and Social Culture (ESSC 2025)
PB  - Atlantis Press
SP  - 4
EP  - 14
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-553-9_2
DO  - 10.2991/978-2-38476-553-9_2
ID  - Gong2026
ER  -