{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T07:23:41Z","timestamp":1777879421085,"version":"3.51.4"},"reference-count":59,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Semantic Web: \u2013 Interoperability, Usability, Applicability"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:p>\n                    When dealing with the structure, content, and quality of knowledge graphs (KGs), most analyses focus on entities, overlooking the significance of relationships and their evolution. In this article, we introduce\n                    <jats:sans-serif>K<\/jats:sans-serif>\n                    nowledge\n                    <jats:sans-serif>REL<\/jats:sans-serif>\n                    ationship\n                    <jats:sans-serif>M<\/jats:sans-serif>\n                    odel (KRELM), a novel and efficient graph model that mimics the behavior of facts accumulation in crowdsourced KGs and accurately simulates the evolution of their structure. By modeling the decentralized process of crowdsourcing, KRELM reproduces key distribution patterns found in relationships, demonstrating that the facts in a KG can be generated incrementally, either by adding new entities or by further describing existing ones. Our theoretical analysis of KRELM reveals that the distribution of facts for relationships follows an exponential law for subjects and a power law for objects, enabling a deeper understanding of knowledge graph dynamics. Experimental validation on major KGs shows that KRELM successfully captures a large part of the structure of real-world relationships, and a longitudinal study of Wikidata confirms its effectiveness in predicting relationship evolution. This work opens new avenues for analyzing and benchmarking KGs.\n                  <\/jats:p>","DOI":"10.1177\/22104968251361342","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T12:57:27Z","timestamp":1756990647000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["A Complex Network Model for Knowledge Graphs\u2019 Relationships"],"prefix":"10.1177","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5119-6523","authenticated-orcid":false,"given":"Hassan","family":"Abdallah","sequence":"first","affiliation":[{"name":"LIFAT, University of Tours, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5171-8499","authenticated-orcid":false,"given":"B\u00e9atrice","family":"Markhoff","sequence":"additional","affiliation":[{"name":"CITERES, University of Tours, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8335-6069","authenticated-orcid":false,"given":"Arnaud","family":"Soulet","sequence":"additional","affiliation":[{"name":"LIFAT, University of Tours, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,9,4]]},"reference":[{"key":"e_1_3_5_2_1","doi-asserted-by":"publisher","DOI":"10.14778\/3717755.3717775"},{"issue":"13","key":"e_1_3_5_3_1","first-page":"2049","article-title":"A survey and experimental comparison of distributed sparql engines for very large RDF data","volume":"10","author":"Abdelaziz I.","year":"2017","unstructured":"Abdelaziz I., Harbi R., Khayyat Z., Kalnis P. (2017). A survey and experimental comparison of distributed sparql engines for very large RDF data. VLDB, 10(13), 2049\u20132060.","journal-title":"VLDB"},{"key":"e_1_3_5_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/35019019"},{"key":"e_1_3_5_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-021-00711-3"},{"key":"e_1_3_5_6_1","doi-asserted-by":"crossref","unstructured":"Auer S. Bizer C. Kobilarov G. Lehmann J. Cyganiak R. Ives Z. (2007). DBpedia: A nucleus for a web of open data. In ISWC (pp. 722\u2013735).","DOI":"10.1007\/978-3-540-76298-0_52"},{"key":"e_1_3_5_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2633993"},{"key":"e_1_3_5_8_1","doi-asserted-by":"crossref","unstructured":"Balaraman V. Razniewski S. Nutt W. (2018). Recoin: Relative completeness in Wikidata. In Companion proceedings of the web conference 2018 (pp. 1787\u20131792).","DOI":"10.1145\/3184558.3191641"},{"key":"e_1_3_5_9_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.286.5439.509"},{"key":"e_1_3_5_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-4371(99)00291-5"},{"key":"e_1_3_5_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrg1272"},{"key":"e_1_3_5_12_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.71.036113"},{"issue":"1","key":"e_1_3_5_13_1","first-page":"1","article-title":"Cidoc crm","volume":"7","author":"Bekiari C.","year":"2021","unstructured":"Bekiari C., Bruseker G., Doerr M., Ore C. E., Stead S., Velios A., Blain M. P., Bricaud F., Crevier-Lalonde C., Hart S., et al (2021). Cidoc crm. International Committee for Documentation (CIDOC) of the International Council of Museums (ICOM). Version, 7(1), 1\u2013368.","journal-title":"International Committee for Documentation (CIDOC) of the International Council of Museums (ICOM). Version"},{"key":"e_1_3_5_14_1","doi-asserted-by":"publisher","DOI":"10.3233\/SW-180294"},{"key":"e_1_3_5_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2014.07.001"},{"key":"e_1_3_5_16_1","unstructured":"Bojchevski A. Shchur O. Z\u00fcgner D. G\u00fcnnemann S. (2018). Netgan: Generating graphs via random walks. In International conference on machine learning (pp. 610\u2013619). PMLR."},{"key":"e_1_3_5_17_1","unstructured":"Bollob\u00e1s B. Borgs C. Chayes J. T. Riordan O. (2003). Directed scale-free graphs. In SODA Vol. 3 (pp. 132\u2013139)."},{"key":"e_1_3_5_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-84628-970-5"},{"key":"e_1_3_5_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-018-0528-3"},{"key":"e_1_3_5_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10888-011-9188-x"},{"key":"e_1_3_5_21_1","doi-asserted-by":"crossref","unstructured":"Chakrabarti D. Zhan Y. Faloutsos C. (2004). R-mat: A recursive model for graph mining. In Proceedings of the 2004 SIAM International conference on data mining (pp. 442\u2013446). SIAM.","DOI":"10.1137\/1.9781611972740.43"},{"key":"e_1_3_5_22_1","doi-asserted-by":"publisher","DOI":"10.1090\/cbms\/107"},{"key":"e_1_3_5_23_1","unstructured":"De Cao N. Kipf T. (2018). Molgan: An implicit generative model for small molecular graphs. textitarXiv preprint arXiv:1805.11973."},{"key":"e_1_3_5_24_1","doi-asserted-by":"crossref","unstructured":"Demartini G. (2019). Implicit bias in crowdsourced knowledge graphs. In Companion proceedings of the 2019 world wide web conference (pp. 624\u2013630).","DOI":"10.1145\/3308560.3317307"},{"key":"e_1_3_5_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3369782"},{"key":"e_1_3_5_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3048186"},{"key":"e_1_3_5_27_1","doi-asserted-by":"publisher","DOI":"10.1098\/rspb.2004.2957"},{"key":"e_1_3_5_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcs.2022.10.036"},{"key":"e_1_3_5_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-020-00611-y"},{"key":"e_1_3_5_30_1","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00572"},{"key":"e_1_3_5_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.websem.2005.06.005"},{"key":"e_1_3_5_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3214832"},{"key":"e_1_3_5_33_1","doi-asserted-by":"crossref","unstructured":"Halpin H. Robu V. Shepherd H. (2007). The complex dynamics of collaborative tagging. In Proceedings of the 16th international conference on World Wide Web (pp. 211\u2013220).","DOI":"10.1145\/1242572.1242602"},{"key":"e_1_3_5_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447772"},{"key":"e_1_3_5_35_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.65.056109"},{"key":"e_1_3_5_36_1","first-page":"675","article-title":"A survey on semantic schema discovery","author":"Kellou-Menouer K.","year":"2021","unstructured":"Kellou-Menouer K., Kardoulakis N., Troullinou G., Kedad Z., Plexousakis D., Kondylakis H. (2021). A survey on semantic schema discovery. The VLDB Journal, 31(4), 675\u2013710.","journal-title":"The VLDB Journal"},{"key":"e_1_3_5_37_1","unstructured":"Li Y. Vinyals O. Dyer C. Pascanu R. Battaglia P. (2018). Learning deep generative models of graphs. arXiv preprint arXiv:1803.03324."},{"key":"e_1_3_5_38_1","doi-asserted-by":"crossref","unstructured":"Liu X. Tu Z. Wang Z. Xu X. Chen Y. (2020). A crowdsourcing-based knowledge graph construction platform. In International conference on service-oriented computing (pp. 63\u201366). Springer.","DOI":"10.1007\/978-3-030-76352-7_9"},{"key":"e_1_3_5_39_1","doi-asserted-by":"crossref","unstructured":"Melo A. Paulheim H. (2017). Synthesizing knowledge graphs for link and type prediction benchmarking. In The Semantic Web: 14th International conference ESWC 2017 Portoro\u017e Slovenia May 28\u2013June 1 2017 Proceedings Part I 14 (pp. 136\u2013151). Springer.","DOI":"10.1007\/978-3-319-58068-5_9"},{"key":"e_1_3_5_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0016-0032(96)00063-4"},{"key":"e_1_3_5_41_1","doi-asserted-by":"publisher","DOI":"10.1080\/00107510500052444"},{"key":"e_1_3_5_42_1","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780198805090.001.0001"},{"key":"e_1_3_5_43_1","doi-asserted-by":"crossref","unstructured":"Oelen A. Stocker M. Auer S. (2022). Tinygenius: intertwining natural language processing with microtask crowdsourcing for scholarly knowledge graph creation. In Proceedings of the 22nd ACM\/IEEE Joint conference on digital libraries (pp. 1\u20135).","DOI":"10.1145\/3529372.3533285"},{"key":"e_1_3_5_44_1","doi-asserted-by":"crossref","unstructured":"Park H. Kim M. S. (2017). Trilliong: A trillion-scale synthetic graph generator using a recursive vector model. In Proceedings of the 2017 ACM International conference on management of data (pp. 913\u2013928).","DOI":"10.1145\/3035918.3064014"},{"key":"e_1_3_5_45_1","doi-asserted-by":"crossref","unstructured":"Pellissier Tanon T. Weikum G. Suchanek F. (2020). Yago 4: A reason-able knowledge base. In ESWC (pp. 583\u2013596).","DOI":"10.1007\/978-3-030-49461-2_34"},{"key":"e_1_3_5_46_1","doi-asserted-by":"crossref","unstructured":"Piao G. Huang W. (2021). Learning to predict the departure dynamics of Wikidata editors. In International semantic web conference (pp. 39\u201355). Springer.","DOI":"10.1007\/978-3-030-88361-4_3"},{"key":"e_1_3_5_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3274410"},{"issue":"1","key":"e_1_3_5_48_1","first-page":"11","article-title":"How does knowledge evolve in open knowledge graphs?","volume":"1","author":"Polleres A.","year":"2023","unstructured":"Polleres A., Pernisch R., Bonifati A., Dell\u2019Aglio D., Dobriy D., Dumbrava S., Etcheverry L., Ferranti N., Hose K., Jim\u00e9nez-Ruiz E., et\u00a0al (2023). How does knowledge evolve in open knowledge graphs?. Transactions on Graph Data and Knowledge, 1(1), 11\u20131.","journal-title":"Transactions on Graph Data and Knowledge"},{"issue":"5","key":"e_1_3_5_49_1","first-page":"1023","article-title":"Extraction of validating shapes from very large knowledge graphs","volume":"16","author":"Rabbani K.","year":"2023","unstructured":"Rabbani K., Lissandrini M., Hose K. (2023). Extraction of validating shapes from very large knowledge graphs. PVLDB, 16(5), 1023\u20131032.","journal-title":"PVLDB"},{"key":"e_1_3_5_50_1","unstructured":"Ramadizsa M. Darari F. Nutt W. Razniewski S. (2023). Knowledge gap discovery: A case study of Wikidata."},{"key":"e_1_3_5_51_1","doi-asserted-by":"publisher","DOI":"10.15346\/hc.v2i1.2"},{"issue":"4","key":"e_1_3_5_52_1","first-page":"355","article-title":"DBnary: Wiktionary as a lemon-based multilingual lexical resource in RDF","volume":"6","author":"S\u00e9rasset G.","year":"2015","unstructured":"S\u00e9rasset G. (2015). DBnary: Wiktionary as a lemon-based multilingual lexical resource in RDF. Semantic Web, 6(4), 355\u2013361.","journal-title":"Semantic Web"},{"key":"e_1_3_5_53_1","doi-asserted-by":"crossref","unstructured":"Simonovsky M. Komodakis N. (2018). Graphvae: Towards generation of small graphs using variational autoencoders. In Artificial neural networks and machine learning\u2013ICANN 2018: 27th International conference on artificial neural networks Rhodes Greece October 4\u20137 2018 Proceedings Part I 27 (pp. 412\u2013422). Springer.","DOI":"10.1007\/978-3-030-01418-6_41"},{"key":"e_1_3_5_54_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0269888913000192"},{"key":"e_1_3_5_55_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2011.09.005"},{"key":"e_1_3_5_56_1","volume-title":"Handbook of knowledge representation","author":"Van Harmelen F.","year":"2008","unstructured":"Van Harmelen F., Lifschitz V., Porter B. (2008). Handbook of knowledge representation. Amsterdam: Elsevier."},{"key":"e_1_3_5_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/2629489"},{"key":"e_1_3_5_58_1","doi-asserted-by":"crossref","unstructured":"Vrande\u010di\u0107 D. Pintscher L. Kr\u00f6tzsch M. (2023). Wikidata: The making of. In the ACM Web conference 2023 (pp. 615\u2013624).","DOI":"10.1145\/3543873.3585579"},{"key":"e_1_3_5_59_1","unstructured":"You J. Ying R. Ren X. Hamilton W. Leskovec J. (2018). GraphRNN: Generating realistic graphs with deep auto-regressive models. In International conference on machine learning (pp. 5708\u20135717). PMLR."},{"key":"e_1_3_5_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2022.105434"}],"container-title":["Semantic Web: \u2013 Interoperability, Usability, Applicability"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/22104968251361342","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/22104968251361342","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/22104968251361342","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T23:43:21Z","timestamp":1777592601000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/22104968251361342"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":59,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["10.1177\/22104968251361342"],"URL":"https:\/\/doi.org\/10.1177\/22104968251361342","relation":{},"ISSN":["1570-0844","2210-4968"],"issn-type":[{"value":"1570-0844","type":"print"},{"value":"2210-4968","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5]]},"article-number":"22104968251361342"}}