AgriKG: An Agricultural Knowledge Graph and Its Applications
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In-KG applications include graph refinement (e.g., completion and error detection), triple/entity classification, and entity resolution. Out-of-KG applications include relation extraction, question answering, and recommendation, which are related to specific AIoT scenarios, such as network forensics analysis in smart security [224], smart assistants in smart healthcare and agriculture [39], [225],
“InKG applications include graph refinement (e.g., completion and error detection), triple/entity classification, and entity resolution. Out-of-KG applications include relation extraction, question answering, and recommendation, which are related to specific AIoT scenarios, such as network forensics analysis in smart security [224], smart assistants in smart healthcare and agriculture [39], [225],”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [225]
An agricultural knowledge graph, namely AgriKG, is automatically constructed by leveraging NLP and deep learning techniques.
“To this end, an agricultural knowledge graph, namely AgriKG [225], is automatically constructed by leveraging NLP and deep learning techniques.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [225]
Cites
Enriching Word Vectors with Subword Information
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00051 · Cited by 9,645 · see citing works
Distant supervision for relation extraction without labeled data
Mintz, M. D., Bills, S., Snow, R., & Jurafsky, D. (2009) · Annual Meeting of the Association for Computational Linguistics · doi:10.3115/1690219.1690287 · Cited by 2,799 · see citing works
Neural Relation Extraction with Selective Attention over Instances
Lin, Y., Shen, S., Liu, Z., Luan, H., & Sun, M. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1200 · Cited by 1,052 · see citing works
Constraint-Based Question Answering with Knowledge Graph
Bao, J., Duan, N., Yan, Z., Zhou, M., & Zhao, T. (2016) · International Conference on Computational Linguistics · Cited by 179 · see citing works
Punctuation as Implicit Annotations for Chinese Word Segmentation
Li, Z., & Sun, M. (2009) · Computational Linguistics · doi:10.1162/coli.2009.35.4.35403 · Cited by 133 · see citing works
S-MART: Novel Tree-based Structured Learning Algorithms Applied to Tweet Entity Linking
Yang, Y., & Chang, M.-W. (2015) · doi:10.3115/v1/p15-1049 · Cited by 110 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774 · see citing works
- Authors
- Chen, Y., Kuang, J., Cheng, D., Zheng, J., Gao, M., & Zhou, A.
- Affiliations
- East China Normal University (CN)
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Gao, Ming
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Volume
- 11448
- Pages
- 533-537
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2941274872
- Cited by
- 70
- References
- 6
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 15 · 2023: 14 · 2022: 9 · 2021: 9 · 2020: 4 · 2019: 1
- FWCI
- 2.39
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- Lecture notes in computer science
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- entity linking, unstructured text, knowledge graph construction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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