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2025 , Vol. 69 >Issue 23: 125 - 136

DOI: https://doi.org/10.13266/j.issn.0252-3116.2025.23.010

RESEARCH PAPERS

Research on the Model for Representing the Context of Data Semantic in Knowledge Graph Based on MDR

  • Mu Mengning ,
  • Yuan Man
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  • 1 School of Computer and Information Technology, Northeast Petroleum University, Daqing 163319;
    2 Sanya Institute of Offshore Oil & Gas, Northeast Petroleum University, Sanya 572025
Mu Mengning, Lecturer, PhD; Yuan Man, Professor, Doctoral Supervisor, Corresponding Author, E-mail: yuanman@nepu.edu.cn.

Received date: 2024-11-04

  Revised date: 2025-01-19

  Online published: 2025-11-20

Supported by

This work is supported by the 2024 Hainan Provincial Philosophy and Social Sciences Research Planning Project, titled “Research on Knowledge Integration and Interoperability Mechanisms in the Digital Transformation of Education in Hainan Province” (Grant No. HNSK(QN)24-53).

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Abstract

[Purpose/Significance] Analyzing the core components of knowledge graph data semantics can provide guidance for building sharable and interoperable knowledge graphs in vertical domains. [Method/Process] From the perspective of data-to-knowledge evolution, this paper proposed the “Semantic Meta-model-Ontology Model-Knowledge Graph” knowledge organization model (SOKM). First, on the connotation and extension of context defined by the theory of Semantic Value, and the data semantic standardization framework proposed by the ISO/ IEC 11179 (MDR) standard, it proposed the semantic metamodel (MSeMeta), which clarified the model structure for data semantic context composition method and standardized representation. Subsequently, the idea of punning was introduced to model context as both instances and classes, enabling machines to interpret the context of knowledge graph data semantics and achieve standardized representation of context within the ontology model. The final step involved validating the feasibility and rationality of SOKM by constructing a standardized knowledge graph and interoperability case in the geoscience. [Result/Conclusion] SOKM achieves semantic and representation standardization of data element at the ontology layer, while the proposed semantic meta-model integrating context plays a crucial role in advancing the semantic standardization of knowledge graphs.

Key words： data semantic standardization; context; semantic value theory; MDR; interoperability

Cite this article

Mu Mengning , Yuan Man . Research on the Model for Representing the Context of Data Semantic in Knowledge Graph Based on MDR[J]. Library and Information Service, 2025 , 69(23) : 125 -136 . DOI: 10.13266/j.issn.0252-3116.2025.23.010

References

[1] WANG C, HAZEN R M, CHENG Q, et al. The Deep-Time digital earth program: data-driven discovery in geosciences[J]. National science review, 2021, 8(9): nwab027.
[2] JANOWICZ K, HITZLER P, LI W, et al. Know, know where, know-where-graph: a densely connected, cross-domain knowledge graph and geo-enrichment service stack for applications in environmental intelligence[J]. AI magazine, 2022, 43(1): 30-39.
[3] MO F, CHAPLIN J C, SANDERSON D, et al. Semantic models and knowledge graphs as manufacturing system reconfiguration enablers[J]. Robotics and computer-integrated manufacturing, 2024, 86: 102625.
[4] XUE B, ZOU L. Knowledge graph quality management: a comprehensive survey[J]. IEEE transactions on knowledge and data engineering, 2022, 35(5): 4969-4988.
[5] YUAN J, LI H. Research on the standardization model of data semantics in the knowledge graph construction of oil & gas industry[J]. Computer standards & interfaces, 2023, 84: 103705.
[6] 刘慧琳, 牛力. 标准文件的知识图谱组织模式探究[J]. 档案学通讯, 2021(5): 58-65. (LIU H L, NIU L. Research on theorganization model of knowledge graph of standard documents [J]. Archives science bulletin, 2021(5): 58-65.)
[7] JIANG Y K, GAO X, SU W X, et al. Systematic knowledge management of construction safety standards based on knowledge graphs: a case study in China[J]. International journal of environmental research and public health, 2021, 18(20): 10692.
[8] 袁满, 褚冰, 陈萍. 知识图谱构建中的语义标准问题研究[J]. 情报理论与践, 2020, 43(3): 131-137. (YUAN M, CHU B, CHEN P. Research on issues of semantic standard in knowledge graph construction[J]. Information studies: theory & application, 2020, 43(3): 131-137.)
[9] MONTAGUE R. The proper treatment of quantification in ordinary English[C]//Approaches to natural language: proceedings of the 1970 stanford workshop on grammar and semantics. Dordrecht: Springer, 1973, 49: 221-242.
[10] FILLMORE C J. Frame semantics[J]. Cognitive linguistics: basic readings, 2006, 34: 373-400.
[11] PIERRA G. Context representation in domain ontologies and its use for semantic integration of data[M]//Lecture notes in computer science. Berlin: Springer, 2008: 174-211.
[12] SCIORE E, SIEGEL M, ROSNETHAL A. Using semantic values to facilitate interoperability among heterogeneous information systems[J]. ACM transactions on database systems, 1994, 19(2): 254-290.
[13] The International Organization for Standardization. Information technology — Metadata registries (MDR): ISO/IEC 11179-3:2023[S/OL]. [2025-02-20]. https://www.iso.org/standard/78915.html.
[14] The International Organization for Standardization. Information technology — Procedures for achieving metadata registry content consistency Part 6: Framework for generating ontologies: ISO/IEC TR 20943-6: 2013[S/OL]. [2025-02-20]. https://www.iso.org/standard/56030.html.
[15] 中国电子技术标准化研究院. 知识图谱标准化白皮书[EB/OL]. [2025-02-21]. http://www.cesi.cn/201909/5589.html. (China Electronics standardization institute. knowledge graph standardization white paper[EB/OL]. [2025-02-21]. http://www.cesi.cn/201909/5589.html.)
[16] IEEE Standards Association. Standard for technical requirements of standard-oriented knowledge graphs[EB/OL]. [2025-02-21]. https://sagroups.ieee.org/2959/wp-content/uploads/sites/388/2021/01/The-PAR-of-P2959.pdf.
[17] IEEE Standards Association. Standard for technical requirements and evaluation of knowledge graphs[EB/OL]. [2025-02-25]. https://standards.ieee.org/ieee/2807/7525/.
[18] IEEE Standards Association. Standard for technical requirements and evaluation of knowledge graphs[EB/OL]. [2025-02-25]. https://standards.ieee.org/project/2807_1.html.
[19] 中国电子技术标准化研究院. 关于征求《人工智能知识图谱知识交换协议（征求意见稿）》国家标准意见的函[EB/OL]. [2025-02-25]. http://www.cesi.cn/202405/10040.html. (China Electronics standardization institute. letter soliciting opinions on the national standard “artificial intelligence knowledge graph knowledge exchange protocol (draft for solicitation of comments)” [EB/OL]. http://www.cesi.cn/202405/10040.html.)
[20] XU H, ZHAO Y, HUANG H, et al. A comprehensive construction of the domain ontology for stratigraphy[J]. Geoscience frontiers, 2023, 14(5): 101461.
[21] XIAO X, LIN H, FANG C. WMO: an ontology for the semantic enrichment of wetland monitoring data[J]. International journal of digital earth, 2023, 16(1): 2189-2211.
[22] ZHU Y, SUN K, WANG S, et al. An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships[J]. Science China earth sciences, 2023, 66(11): 2563-2578.
[23] QIU Q, WANG B, MA K, et al. A practical approach to constructing a geological knowledge graph: a case study of mineral exploration data[J]. Journal of earth science, 2023, 34(5): 1374-1389.
[24] LI H, ZHU Q, ZHANG L, et al. Integrated representation of geospatial data, model, and knowledge for digital twin railway[J]. International journal of digital earth, 2022, 15(1): 1657-1675.
[25] KOMMINENI V K, KONIG-RIES B, SAMUEL S. From human experts to machines: An LLM supported approach to ontology and knowledge graph construction[EB/OL]. [2025-02-25]. https://arxiv.org/abs/2403.08345.
[26] AIT ABDELOUAHID R, DEBAUCHE O, MAHMOUDI S, et al. Literature review: clinical data interoperability models[J]. Information, 2023, 14(7): 364.
[27] 宋培彦, 刘稚楠, 刘冰. 基于ISO 25964的公共卫生领域术语语义映射机制研究[J]. 图书与情报, 2021(5): 31-38. (SONG P Y, LIU Z N, LIU B. Study on the semantic mapping mechanism of terms in public health based on ISO 25964[J]. Library & information, 2021(5): 31-38.)
[28] DUAN Y, SHAO L, HU G, et al. Specifying architecture of knowledge graph with data graph, information graph, knowledge graph and wisdom graph[C]//2017 IEEE 15th International conference on software engineering research, Management and applications. London: IEEE, 2017: 327-332.
[29] YUAN J, ZHAI K, LI H, et al. Research on the construction and mapping model of knowledgeorganization system driven by standards[J]. Computer standards & interfaces, 2025, 92: 103905.
[30] 袁满, 刘梦琪, 牟梦宁. 基于MDR的知识图谱语义关系及 表示 标准 化模 型研 究[J]. 情报 学报, 2023, 42(7): 832- 841. (YUAN M, LIU M Q, MU M N. Research on semantic relationship and representation standardization model of knowledge graph based on MDR[J]. Journal of the China Society for Scientific and Technical Information, 2023, 42(7): 832-841.)
[31] ABAD-NAVARRO F, MARTINEZ-COSTA C. A knowledge graph-based data harmonization framework for secondary data reuse[J]. Computer methods and programs in biomedicine, 2024, 243: 107918.
[32] TALUKDAR W, BISWAS A. Improving large language model (LLM) fidelity through context-aware grounding: a systematic approach to reliability and veracity[EB/OL]. [2025-02-25]. https://arxiv.org/abs/2408.04023.
[33] VAN DER PLUIJM B A, MARSHAK S. Earth structure: an introduction to structural geology and tectonics[M]. New York: W. W. Norton & Company, 2004.
[34] MENDIL I. A framework for explicit modelling of domain knowledge in state-based formal methods: the case of interactive critical systems[D]. Toulouse: Institute National Polytechnique De Toulouse, 2023.
[35] SEO Y D, KIM Y G, LEE E, et al. Design of a smart greenhouse system based on MAPE-K and ISO/IEC-11179[C]//2018 IEEE International conference on consumer electronics. Las Vegas: IEEE, 2018: 1-2.
[36] RAYCHEVA R, KOSTADINOV K, MITOVA E, et al. Landscape analysis of available European data sources amenable for machine learning and recommendations on usability for rare diseases screening[J]. Orphanet journal of rare diseases, 2024, 19(1): 147.
[37] KHADIR A C, ALIANE H, GUESSOUM A. Ontology learning: grand tour and challenges[J]. Computer science review, 2021, 39: 100339.
[38] SILVA H R S. Event modeling for supporting reasoning of consequences[D]. Porto Alegre, Rio Grande do Sul, Brazil: Universidade Federal Do Rio Grande Do Sul, 2024.
[39] SUCHANEK F M, ALAM M, BONALD T, et al. Yago 4.5: A large and clean knowledge base with a rich taxonomy[C]//Proceedings of the 47th international ACM SIGIR conference on research and development in information retrieval. New York: Association for Computing Machinery, 2024: 131-140.
[40] 牛凤桂, 张贝, 陈石. 大数据时代的地球科学知识图谱研究现状与展望[J]. 地震学报, 2024, 46(3): 353-376. (NIU F G, ZHANG B, CHEN S. Review and perspective of earth science knowledge graph in big data era[J]. Acta seismologica sinica, 2024, 46(3): 353-376.)
[41] VAN DISSEN R, SEEBECK H, LITCHFIELD N, et al. Development of the New Zealand community fault model- Version 1.0[C]//NZSEE 2021 Annual conference. Christchurch: NZSEE, 2021: 92.
[42] AKI K, RICHARDS P G. Quantitative seismology[M]. San Francisco: University Science Books, 2002.
[43] QU Y, PERRIN M, TORABI A, et al. GeoFault: a well-founded fault ontology for interoperability in geological modeling[J]. Computers & geosciences, 2024, 182: 105478.
[44] ZHANG R, INDULSKA M, SADIQ S. Discovering data quality problems: the case of repurposed data[J]. Business & Information systems engineering, 2019, 61: 575-593.
[45] LITCHFIELD N J, VAN DISSEN R, SUTHERLAND R, et al. A model of active faulting in New Zealand[J]. New Zealand journal of geology and geophysics, 2014, 57(1): 32-56.
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