{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T21:33:11Z","timestamp":1780522391693,"version":"3.54.1"},"reference-count":30,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Key Scientific Research Projects of the Higher Education Institutions of Henan Province","award":["25A520054"],"award-info":[{"award-number":["25A520054"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Intelligent Decision Technologies"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>\n                    This research proposes a multi-stage intelligent optimization framework to enhance knowledge graphs\u2019 visual construction and semantic reasoning capabilities by integrating multiple intelligent optimization algorithms into the layout and analysis process. Considering the complexity and evolving structure of knowledge graph data, analyzing knowledge graph data visually to reveal the internal structure and dynamic evolution has become a key issue. Firstly, the particle swarm optimization (PSO) algorithm is applied to reduce feature dimensionality in large-scale datasets, optimize data quality, and select features useful for knowledge graph construction and analysis. Then, the ant colony optimization (ACO) algorithm is adopted to optimize the path relationship between entities, improving the structural integrity of the knowledge graph and the relationship reasoning accuracy. Next, the grey wolf optimizer (GWO) algorithm is utilized to search for semantic associations in large-scale knowledge graphs efficiently, improving knowledge graphs\u2019 reasoning and semantic understanding capabilities. Finally, the firefly algorithm (FA) is used to optimize node distance and path visualization in the graph layout. Three optimal feature subsets\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                        <mml:mrow>\n                          <mml:msub>\n                            <mml:mi>F<\/mml:mi>\n                            <mml:mn>1<\/mml:mn>\n                          <\/mml:msub>\n                        <\/mml:mrow>\n                        <mml:mo>,<\/mml:mo>\n                        <mml:mrow>\n                          <mml:msub>\n                            <mml:mi>F<\/mml:mi>\n                            <mml:mn>2<\/mml:mn>\n                          <\/mml:msub>\n                        <\/mml:mrow>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    and\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                        <mml:mrow>\n                          <mml:msub>\n                            <mml:mi>F<\/mml:mi>\n                            <mml:mn>5<\/mml:mn>\n                          <\/mml:msub>\n                        <\/mml:mrow>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    are found in the PSO algorithm. The classification accuracy is 90%. The FA algorithm is adopted to obtain the optimized coordinate nodes: A (1.5, 1.5), B (2.5, 3.0), C (4.0, 3.0), D\u00a0(3.5, 4.5), and E (5.0, 5.0).\n                  <\/jats:p>","DOI":"10.1177\/18724981251353206","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T08:13:31Z","timestamp":1752740011000},"page":"3154-3171","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Application of intelligent optimization algorithm in constructing visual analysis of knowledge graph dataset"],"prefix":"10.1177","volume":"19","author":[{"given":"Yuping","family":"Li","sequence":"first","affiliation":[{"name":"School of Information Technology, Shangqiu Normal University, Shangqiu 476000, Henan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanjie","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Automation and Internet of Things, Zhengzhou Technical College, Zhengzhou 450121, Henan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,7,17]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-021-09562-1"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2021.1004129"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.123142"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-018-9729-5"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113338"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-021-09694-4"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-04849-z"},{"key":"e_1_3_2_9_2","first-page":"22","article-title":"A novel swarm intelligence optimization approach: sparrow search algorithm[J]","volume":"8","author":"Xue J","year":"2020","unstructured":"Xue J, Shen B. A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Syst Sci & Cont Eng 2020; 8: 22\u201334.","journal-title":"Syst Sci & Cont Eng"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20071880"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2019.03.021"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00366-019-00780-7"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2897580"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102706"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-018-3310-y"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112948"},{"key":"e_1_3_2_17_2","first-page":"1","article-title":"Introduction: what is a knowledge graph?[J]","author":"Fensel D","year":"2020","unstructured":"Fensel D, \u015eim\u015fek U, Angele K, et\u00a0al. Introduction: what is a knowledge graph?[J]. Knowl Graphs: Metho, Tools and Selec use Cases 2020: 1\u201310.","journal-title":"Knowl Graphs: Metho, Tools and Selec use Cases"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1080\/23335777.2015.1114526"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3070843"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3028705"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3522586"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3417451"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101817"},{"key":"e_1_3_2_24_2","first-page":"750","article-title":"A survey on knowledge graph embedding: approaches, applications, and benchmarks[J]","volume":"9","author":"Dai Y","year":"2020","unstructured":"Dai Y, Wang S, Xiong NN, et\u00a0al. A survey on knowledge graph embedding: approaches, applications, and benchmarks[J]. Electronics (Basel) 2020; 9: 750.","journal-title":"Electronics (Basel)"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-024-01297-w"},{"key":"e_1_3_2_26_2","unstructured":"Sun Z Deng ZH Nie JY et\u00a0al. Rotate: knowledge graph embedding by relational rotation in complex space[J]. arXiv preprint arXiv:1902.10197 2019."},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.3390\/sym13030485"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3055147"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3588930"},{"key":"e_1_3_2_30_2","doi-asserted-by":"crossref","unstructured":"Zhao J Cheong KH. Visual evolutionary optimization on combinatorial problems with multimodal large language models: a case study of influence maximization.\u00a0arXiv preprint arXiv:2505.06850 2025.","DOI":"10.1109\/TEVC.2025.3598266"},{"key":"e_1_3_2_31_2","unstructured":"Gr\u00f6tschla F Mathys J Veres R et al. CoRe-GD: a hierarchical framework for scalable graph visualization with GNNs.\u00a0arXiv preprint arXiv:2402.06706 2024."}],"container-title":["Intelligent Decision Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/18724981251353206","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/18724981251353206","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/18724981251353206","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:21:36Z","timestamp":1777454496000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/18724981251353206"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,17]]},"references-count":30,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["10.1177\/18724981251353206"],"URL":"https:\/\/doi.org\/10.1177\/18724981251353206","relation":{},"ISSN":["1872-4981","1875-8843"],"issn-type":[{"value":"1872-4981","type":"print"},{"value":"1875-8843","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,17]]}}}