{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:47:03Z","timestamp":1777704423266,"version":"3.51.4"},"reference-count":23,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2018,8,16]],"date-time":"2018-08-16T00:00:00Z","timestamp":1534377600000},"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":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,10,27]]},"abstract":"<jats:p>\n                    From the mathematical point of view, the goal of ontology learning is to obtain the dimensionality function\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mi>f<\/mml:mi>\n                        <mml:mo>:<\/mml:mo>\n                        <mml:msup>\n                          <mml:mstyle mathvariant=\"double-struck\">\n                            <mml:mi>\u211d<\/mml:mi>\n                          <\/mml:mstyle>\n                          <mml:mi>p<\/mml:mi>\n                        <\/mml:msup>\n                        <mml:mo>\u2192<\/mml:mo>\n                        <mml:mstyle mathvariant=\"double-struck\">\n                          <mml:mi>\u211d<\/mml:mi>\n                        <\/mml:mstyle>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    , and the\n                    <jats:italic>p<\/jats:italic>\n                    -dimensional vector corresponding to the ontology vertex is mapped into one-dimensional real number. In the background of big data applications, the ontology concept corresponds to the high complexity of information, and thus sparse tricks are used in ontology learning algorithm. Through the ontology sparse vector learning, the ontology function\n                    <jats:italic>f<\/jats:italic>\n                    is obtained via ontology sparse vector \u03b2, and then applied to ontology similarity computation and ontology mapping. In this paper, the ontology optimization strategy is obtained by coordinate descent and dual optimization, and the optimal solution is obtained by iterative procedure. Furthermore, the greedy method and active sets are applied in the iterative process. Two experiments are presented where we will apply our algorithm to plant science for ontology similarity measuring and to mathematics ontologies for ontology mapping, respectively. The experimental data show that our primal dual based ontology sparse vector learning algorithm has high efficiency.\n                  <\/jats:p>","DOI":"10.3233\/jifs-169771","type":"journal-article","created":{"date-parts":[[2018,8,17]],"date-time":"2018-08-17T11:18:10Z","timestamp":1534504690000},"page":"4525-4531","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Primal dual based ontology sparse vector learning for similarity measuring and ontology mapping"],"prefix":"10.1177","volume":"35","author":[{"given":"Shu","family":"Gong","sequence":"first","affiliation":[{"name":"Department of Computer Science, Guangdong University Science and Technology, Dongguan, China"}]},{"given":"Liwei","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Guangdong University Science and Technology, Dongguan, China"}]},{"given":"Muhammad","family":"Imran","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, United Arab Emirates University, Al Ain, United Arab Emirates"}]},{"given":"Wei","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Yunnan Normal University, Kunming, China"}]}],"member":"179","published-online":{"date-parts":[[2018,8,16]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.09.125"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12599-017-0488-y"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2017.06.001"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00224-016-9707-z"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1177\/0165551516645528"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2017.04.012"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2017.05.006"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2017.03.001"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2017.02.049"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2017.03.004"},{"key":"e_1_3_1_12_2","first-page":"4585","article-title":"Strong and weak stability of k-partite ranking algorithms","volume":"15","author":"Gao W.","year":"2012","unstructured":"GaoW., GaoY. and ZhangY.G., Strong and weak stability of k-partite ranking algorithms, Information15 (2012), 4585\u20134590.","journal-title":"Information"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/174802"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0218127415400349"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/bxw107"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-016-0651-0"},{"key":"e_1_3_1_17_2","article-title":"Analysis of k-partite ranking algorithm in area under the receiver operating characteristic curve criterion","author":"Gao W.","year":"2017","unstructured":"GaoW. and WangW.F., Analysis of k-partite ranking algorithm in area under the receiver operating characteristic curve criterion, Int J Comput Math (2017). 10.1080\/00207160.2017.1322688.","journal-title":"Int J Comput Math"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.21042\/AMNS.2017.2.00001"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.21042\/AMNS.2017.1.00025"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1155\/2014-438291"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-017-0887-3"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sjbs.2016.09.001"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1080\/03610926.2016.1197254"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-169082"}],"container-title":["Journal of Intelligent &amp; 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