Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11): 255-264.doi: 10.11896/jsjkx.231100079

• Artificial Intelligence • Previous Articles     Next Articles

Knowledge Annotation Platform-based Knowledge Graph Construction and Application for Water Conservancy Hub Projects

ZHANG Junhui1,2, ZAN Hongying1, OU Jiale1, YAN Ziyue1, ZHANG Kunli1   

  1. 1 College of Computer and Artificial Intelligence,Zhengzhou University,Zhengzhou 450001,China
    2 Yellow River Engineering Consulting Co.,Ltd,Zhengzhou 450003,China
  • Received:2023-11-13 Revised:2024-04-28 Online:2024-11-15 Published:2024-11-06
  • About author:ZHANG Junhui,born in 1986,Ph.D,senior engineer,is a member of CCF(No.07619G).Her main research intere-sts include natural language processing and so on.
    ZAN Hongying,born in 1966,Ph.D,professor,Ph.D supervisor,is a member of CCF(No.08671S).Her main research interests include machine translation,Q&A,abstract and machine learning.
  • Supported by:
    Major Program of the National Social Science Foundation of China(21&ZD338).

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Abstract: The generation of a significant volume of heterogeneous data in water resources has facilitated the creation and utilization of domain knowledge graphs,but it has led to discrepancies in the construction processes of these graphs.To address the complexities involved in building water resources knowledge graphs,an efficient approach based on a knowledge annotation platform is proposed.Taking the intelligent application of knowledge in Xiaolangdi Water Conservancy Hub project as an example,using the engineering data of the hub,the proposed method is applied to construct a water conservancy hub project knowledge graph(WCHP-KG) in the field of water conservancy.Firstly,focusing on the Xiaolangdi Water Conservancy Hub project,a construction for conceptual classification and relationship description is established based on industry terminology and existing voca-bularies,forming the pattern layer of WCHP-KG.Through BiLSTM-CRF and sequence labeling models,under the guidance of water conservancy experts,a knowledge annotation platform is used to semi-automatically annotate and manually proofread unstructured texts,achieving knowledge fusion and constructing the data layer of WCHP-KG.Results indicate that WCHP-KG co-vers 43 water conservancy entities and 110 entity relationships.Through practical validation,the proposed WCHP-KG provides a solid structured knowledge base for applications related to the Xiaolangdi Water Conservancy Hub project,and provides a reliable reference for engineering decision-making and management,validating the efficacy of the proposed construction method.In the future,WCHP-KG will be further expanded and the construction process will be improved to meet the needs of more application scenarios and fields.

Key words: Heterogeneous data, Domain knowledge graph, Knowledge graph construction, Water conservancy hub, Knowledge annotation platform

CLC Number: 

  • TP391
[1] HANG T T,FENG J,LU J M.Knowledge Graph Construction Techniques:Classification,Investigation,and Future Directions[J].Computer Science,2021,48(2):175-189.
[2] WANG M,WANG H F,LI B H,et al.Overview of Key Technologies for the New Generation Knowledge Graph[J].Compu-ter Research and Development,2022,59(9):1947-1965.
[3] FENG J,XU X,LU J M.Construction and Application of Water Conservancy Information Knowledge Graph[J].Computer and Modernization,2019(9):35-40.
[4] WANG L.The application of knowledge graph technology in thejoint scheduling of water conservancy projects in the Beijiang River Basin[J].Heilongjiang Water Conservancy Technology,2021,49(12):187-190.
[5] CAI Y,XIE W J,CHENG Y L,et al.A review of key technology research on a national water conservancy map[J].Journal of Water Resources,2020,51(6):685-694.
[6] DUAN H,HAN K,ZHAO H L,et al.Research on the construction of a comprehensive knowledge graph for water conservancy[J].Journal of Water Resources,2021,52(8):948-958.
[7] WANG L,SUN W J.Comparative Study on Simulation of Rainfall Characteristics between HEC-HMS and Vflo Based on DEM Data:A Case Study of Miyun District,Beijing[J].Journal of Environmental Science,2019,39(10):3559-3565.
[8] XIAO J Q,ZHOU X,PAN Y,et al.Application of GA-BP optimized TS fuzzy neural network water quality monitoring and evaluation system prediction model－taking the Taihu Lake as an example[J].Journal of Southwest University(Natural Science Edition),2019,41(12):110-119.
[9] LIU M,FU B L,HE H C,et al.Water surface monitoring and water quality parameter inversion of the Li River based on multi temporal active and passive remote sensing(2016-2020)[J].Lake Science,2021,33(3):687-705.
[10] HUILIN S Z W.Overview on the advance of the research onnamed entity recognition[J].Data Analysis and Knowledge Discovery,2010,26(6):42-47.
[11] LIU X,ZHANG S,WEI F,et al.Recognizing named entities in tweets[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies.2011:359-367.
[12] XU K,ZHOU Z,HAO T,et al.A bidirectional LSTM and conditional random fields approach to medical named entity recognition[C]//Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017.Springer International Publishing,2018:355-365.
[13] JIA Y,MA X.Attention in character-based BiLSTM-CRF forChinese named entity recognition[C]//Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence.2019:1-4.
[14] KUMAR S.A survey of deep learning methods for relation extraction[J].arXiv:1705.03645,2017.
[15] ZENG D,LIU K,LAI S,et al.Relation classification via convolutional deep neural network[C]//Proceedings of COLING 2014,the 25th International Conference on Computational Linguistics:Technical Papers.2014:2335-2344.
[16] SOCHER R,HUVAL B,MANNING C D,et al.Semantic compositionality through recursive matrix-vector spaces[C]//Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning.2012:1201-1211.
[17] XU Y,MOU L,LI G,et al.Classifying relations via long short term memory networks along shortest dependency paths[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.2015:1785-1794.
[18] ZENG D,LIU K,CHEN Y,et al.Distant supervision for relation extraction via piecewise convolutional neural networks[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.2015:1753-1762.
[19] SURDEANU M,TIBSHIRANI J,NALLAPATI R,et al.Multi-instance multi-label learning for relation extraction[C]//Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning.2012:455-465.
[20] HE H,SUN X.A unified model for cross-domain and semi-su-pervised named entity recognition in chinese social media[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2017.
[21] VASHISHTH S,JOSHI R,PRAYAGA S S,et al.RESIDE:Improving Distantly-Supervised Neural Relation Extraction using Side Information[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.2018:1257-1266.
[22] MIWA M,BANSAL M.End-to-End Relation Extraction usingLSTMs on Sequences and Tree Structures[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers).Association for Computational Linguistics,2016.
[23] CHEN S Y,LU D D,CHENG H M.Knowledge Graph Analysis of Water Resources Research in China Based on Technology Text Mining[J].Hydrology,2019,39(2):61-66.
[24] JIN J L,CHEN P F,CHEN M L,et al.Bibliometric analysis of water resource carrying capacity research based on knowledge graph[J].Water Resources Protection,2019,35(6):14-24.
[25] MAO W S,ZHAO H L,JIANG Y Z,et al.Construction and application of a knowledge graph for domestic water ecological environment research based on bibliometrics[J].Journal of Water Resources,2019,50(11):1400-1416.
[26] LI Z Q,HU H.In recent years,research progress on water-sa-ving irrigation technology in China－a scientific knowledge graph analysis based on bibliometrics[J].Water Saving Irrigation,2015(8):104-109.
[27] LIU X J,YANG X,FU H L.The Development Trend and Research Hotspot Analysis of Regenerated Water Research:A Graph Quantification Study Based on CiteSpace[J].Resources and Environment in Arid Areas,2019(4):68-75.
[28] WANG X L,XUE X P,SUN R F.A Tracing Method for SuddenWater Pollution Events Based on Particle Swarm Optimization and Knowledge Graph[J].Hydroelectric Power,2020,46(2):17-21.
[29] WANG J,TANG J,YANG M,et al.Improving OCR-basedimage captioning by incorporating geometrical relationship[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:1306-1315.
[30] CHEN W,ZHA H,CHEN Z,et al.HybridQA:A Dataset of Multi-Hop Question Answering over Tabular and Textual Data[C]//Findings of the Association for Computational Linguistics:EMNLP 2020.2020:1026-1036.
[31] ZHU F,LEI W,HUANG Y,et al.TAT-QA:A Question An-swering Benchmark on a Hybrid of Tabular and Textual Content in Finance[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing(Volume 1:Long Papers).2021:3277-3287.
[32] ZHANG K L,ZHAO X,GUAN T F,et al.Construction and ap-plication of entity and relationship labeling platform for medical texts[J].J.Chin.Inf.Process,2020,34(6):36-44.
[33] HOU M W,WEI R,LU L,et al.A review of knowledge graph research and its application in the medical field[J].Computer Research and Development,2018,55(12):2587-2599.
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