A Formal Language for the Analysis of Graph Models and its Software Implementation
DOI:
https://doi.org/10.18372/1990-5548.85.20427Keywords:
graph models, language for analysis of graph models, semantic networks, syntactical analysis, message transition, rumor spreading processAbstract
The purpose of this paper is to develop a specialized language for processing graph data and its software implementation. The proposed solution ensures versatility, usability, and efficiency, enabling the execution of both basic graph operations and more complex procedures. The tool supports classical graph algorithms, including shortest path search, graph traversal, and minimum spanning tree construction, as well as applications in modeling and analyzing message transition processes and processing graph-based representations of textual data. A common drawback of many existing graph analysis tools—often implemented as libraries of general-purpose programming languages—is their limited usability. This limitation arises from the fact that the description of graph analysis procedures relies on data structure operations defined in terms of these general-purpose languages, which complicates perception and reduces the clarity of the mathematical methods being implemented. Developing a specialized domain-specific language based on high-level abstractions can address these shortcomings. Such a language will provide a formalized description of methods for analyzing and processing graph models, improving their comprehensibility and accessibility to users. Its software implementation will deliver ready-to-use solutions for executing graph analysis methods. Composing such methods will facilitate solving a wide range of tasks, including the analysis of natural language messages and the study of information publication and dissemination processes in online environments.
References
A. Korger, & J. Baumeister, (2021, September). Rule-based Semantic Relation Extraction in Regulatory Documents. LWDA, pp. 26–37.
M. C. De Marneffe, C. D. Manning, J. Nivre, & D. Zeman, “Universal dependencies,” Computational linguistics, 47(2), pp. 255–308, 2021. https://doi.org/10.1162/coli_a_00402
K. D'Oosterlinck, S. K. Bitew, B. Papineau, C. Potts, T. Demeester, & C. Develder, CAW-coref: Conjunction-Aware Word-level Coreference Resolution. arXiv preprint arXiv:2310.06165. https://doi.org/10.18653/v1/2023.crac-main.2
S. Schuster, & C. D. Manning, “Enhanced English universal dependencies: An improved representation for natural language understanding tasks,” Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), 2016, May, pp. 2371–2378.
C. D. Manning, D. Christopher, M. Surdeanu, J. Bauer, J. Finkel, S. J. Bethard, and D. McClosky, “The Stanford CoreNLP Natural Language Processing Toolkit,” Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2014, pp. 55–60. https://doi.org/10.3115/v1/P14-5010
Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton and Christopher D. Manning, "Stanza: A Python Natural Language Processing Toolkit for Many Human Languages," Association for Computational Linguistics (ACL) System Demonstrations, 2020. https://doi.org/10.18653/v1/2020.acl-demos.14
K. Dong, A. Sun, J. J. Kim, & X. Li, “Syntactic multi-view learning for open information extraction,” 2022. arXiv preprint arXiv:2212.02068. https://doi.org/10.18653/v1/2022.emnlp-main.272
G. Angeli, M. J. J. Premkumar, & C. D. Manning, “Leveraging linguistic structure for open domain information extraction,” Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (vol. 1: Long Papers), 2015, pp. 344–354. https://doi.org/10.3115/v1/P15-1034
, P. Heyvaert, B. De Meester, A. Dimou, & R. Verborgh, “Declarative rules for linked data generation at your fingertips!,” The Semantic Web: ESWC 2018 Satellite Events: ESWC 2018 Satellite Events, Heraklion, Crete, Greece, June 3-7, 2018, Revised Selected Papers 15 (pp. 213–217). Springer International Publishing. https://doi.org/10.1007/978-3-319-98192-5_40
D. Van Assche, T. Delva, P. Heyvaert, B. De Meester, & A. Dimou, “Towards a more human-friendly knowledge graph generation & publication,” ISWC2021, The International Semantic Web Conference, vol. 2980, 2021. CEUR.
Guillaume Plique, “Graphology, a robust and multipurpose Graph object for JavaScript,” Zenodo, Published December 11, 2021 | Version 0.23.2-lib
D. Li, H. Mei, Y. Shen, S. Su, W. Zhang, J. Wang, M. Zu, & W. Chen, “ECharts: a declarative framework for rapid construction of web-based visualization,” Visual Informatics, 2(2), 136–146, 2018. https://doi.org/10.1016/j.visinf.2018.04.011
Downloads
Published
How to Cite
Issue
Section
License
The scientific journal “Electronics and control systems” adheres to the principles of Open Access and provides free, immediate, and permanent access to all published materials without financial, technical, or legal barriers for readers.
All articles are published in Open Access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Copyright
Authors who publish their works in the journal “Electronics and control systems”:
-
retain the copyright to their publications;
-
grant the journal the right of first publication of the article;
-
agree to the distribution of their materials under the CC BY 4.0 license;
-
have the right to reuse, archive, and distribute their works (including in institutional and subject repositories), provided that proper reference is made to the original publication in the journal.




