{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:53:15Z","timestamp":1760151195443,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T00:00:00Z","timestamp":1646179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Atypon (United States)","award":["-"],"award-info":[{"award-number":["-"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The descriptive concepts of \u201csemantic\u201d taxonomies are assigned to content items of the publishing domain for supporting a plethora of operations, mostly regarding the organization and discoverability of the content, as well as for recommendation tasks. However, either not all publishers rely on such structures, or in many cases employ their own proprietary taxonomies, thus the content is either difficult to be retrieved by the end users or stored in publisher-specific fragmented \u201cdata-silos\u201d, respectively. To address these issues, the modular and scalable \u201cDominance Metric\u201d methodology is proposed for rating the dominance and importance of concepts in semantic taxonomies. Our proposed metric is applied both on the vast multidisciplinary Microsoft Academic Graph Fields of Study taxonomy and the MeSH controlled vocabulary in order for their enhanced and refined versions to be produced. Moreover, we describe the cleansing process of the resulting taxonomy from Microsoft\u2019s structure by deduplicating concepts and refining the hierarchical relations towards the increase of its representation quality. Our evaluation procedure provided valuable insights by showcasing that high volume, namely the number of publications a concept is assigned to, does not necessarily imply high influence, but the latter is also affected by the structural and topological properties of the individual entities.<\/jats:p>","DOI":"10.3390\/computers11030035","type":"journal-article","created":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T08:37:16Z","timestamp":1646210236000},"page":"35","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Rating the Dominance of Concepts in Semantic Taxonomies"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8504-3234","authenticated-orcid":false,"given":"Gerasimos","family":"Razis","sequence":"first","affiliation":[{"name":"Computer Science and Biomedical Informatics Department, University of Thessaly, 35131 Lamia, Greece"},{"name":"Atypon Systems LLC, Santa Clara, CA 95054, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ioannis","family":"Anagnostopoulos","sequence":"additional","affiliation":[{"name":"Computer Science and Biomedical Informatics Department, University of Thessaly, 35131 Lamia, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Atypon Systems LLC, Santa Clara, CA 95054, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,2]]},"reference":[{"key":"ref_1","first-page":"335","article-title":"TrendMD: Using AI to enhance discovery and achieve publisher goals","volume":"39","author":"Carelli","year":"2020","journal-title":"Inf. Serv. Use"},{"key":"ref_2","first-page":"661","article-title":"Taxonomy Construction Techniques-Issues and Challenges","volume":"2","author":"Sujatha","year":"2011","journal-title":"Indian J. Comput. Sci. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Shen, J., Wu, Z., Lei, D., Zhang, C., Ren, X., Vanni, M.T., Sadler, B.M., and Han, J. (2018, January 19\u201323). HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD \u201918), London, UK.","DOI":"10.1145\/3219819.3220115"},{"key":"ref_4","unstructured":"Tuan, L.A., Kim, J., and Kiong, N.S. (2014, January 25\u201329). Taxonomy Construction Using Syntactic Contextual Evidence. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sinha, A., Shen, Z., Song, Y., Ma, H., Eide, D., Hsu, B.-J., and Wang, K. (2015, January 18\u201322). An Overview of Microsoft Academic Service (MAS) and Applications. Proceedings of the 24th International Conference on World Wide Web (WWW \u201915 Companion), Florence, Italy.","DOI":"10.1145\/2740908.2742839"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shen, Z., Ma, H., and Wang, K. (2018, January 15\u201320). A Web-scale system for scientific knowledge exploration. Proceedings of the ACL 2018, System Demonstrations, Association for Computational Linguistics, Melbourne, Australia.","DOI":"10.18653\/v1\/P18-4015"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Shen, Z., Wu, C.-H., Ma, L., Chen, C.-P., and Wang, K. (2021, January 1\u20136). SciConceptMiner: A system for large-scale scientific concept discovery. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, Association for Computational Linguistics, online.","DOI":"10.18653\/v1\/2021.acl-demo.6"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Razis, G., and Anagnostopoulos, I. (2014, January 6\u20137). Semantifying Twitter: The Influence Tracker Ontology. Proceedings of the 9th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), Corfu, Greece.","DOI":"10.1109\/SMAP.2014.23"},{"key":"ref_9","unstructured":"Romero, D.M., Galuba, W., Asur, S., and Huberman, B.A. (April, January 28). Influence and passivity in social media. Proceedings of the 20th international conference companion on World Wide Web (WWW \u201911), Hyderabad, India."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Cha, M., Haddadi, H., Benevenuto, F., and Gummadi, P.K. (2010, January 4\u20138). Measuring user influence in Twitter: The million follower fallacy. Proceedings of the 4th International Conference on Weblogs and Social Media (ICWSM \u201910), Dublin, Ireland.","DOI":"10.1609\/icwsm.v4i1.14033"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Razis, G., Anagnostopoulos, I., and Zhou, H. (2021, January 4\u20135). Identifying Dominant Nodes in Semantic Taxonomies. Proceedings of the 16th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP), Corfu, Greece.","DOI":"10.1109\/SMAP53521.2021.9610779"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nargundkar, A., and Rao, Y.S. (2016, January 8\u20139). InfluenceRank: A machine learning approach to measure influence of Twitter users. Proceedings of the International Conference on Recent Trends in Information Technology (ICRTIT \u201916), Chennai, India.","DOI":"10.1109\/ICRTIT.2016.7569535"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.ins.2016.08.023","article-title":"Social influence modeling using information theory in mobile social networks","volume":"379","author":"Peng","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hutchins, B.I., Yuan, X., Anderson, J.M., and Santangelo, G.M. (2016). Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level. PLoS Biol., 14.","DOI":"10.1371\/journal.pbio.1002541"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Jaitly, V., Chowriappa, P., and Dua, S. (2016, January 21\u201324). A framework to identify influencers in signed social networks. Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India.","DOI":"10.1109\/ICACCI.2016.7732403"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hajian, B., and White, T. (2011, January 9\u201311). Modelling Influence in a Social Network: Metrics and Evaluation. Proceedings of the IEEE 3rd International Conference on Privacy, Security, Risk and Trust and IEEE 3rd International Conference on Social Computing, Boston, MA, USA.","DOI":"10.1109\/PASSAT\/SocialCom.2011.118"},{"key":"ref_17","first-page":"6","article-title":"Applying an influence measurement framework to large social network","volume":"7","author":"Almgren","year":"2016","journal-title":"J. Netw. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1142\/S0218194019500050","article-title":"The Influence Ranking for Testers in Bug Tracking Systems","volume":"29","author":"Li","year":"2019","journal-title":"Int. J. Softw. Eng. Knowl. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pal, A., and Ruj, S. (2015, January 8\u201312). CITEX: A new citation index to measure the relative importance of authors and papers in scientific publications. Proceedings of the 2015 IEEE International Conference on Communications (ICC), London, UK.","DOI":"10.1109\/ICC.2015.7248495"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A Coefficient of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_21","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv."},{"key":"ref_22","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., and Dean, J. (2013, January 5\u201310). Distributed Representations of Words and Phrases and their Com-positionality. Proceedings of the 26th International Conference on Neural Information Processing Systems-Volume 2 (NIPS \u201913), Lake Tahoe, NV, USA."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/11\/3\/35\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:30:35Z","timestamp":1760135435000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/11\/3\/35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,2]]},"references-count":22,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["computers11030035"],"URL":"https:\/\/doi.org\/10.3390\/computers11030035","relation":{},"ISSN":["2073-431X"],"issn-type":[{"type":"electronic","value":"2073-431X"}],"subject":[],"published":{"date-parts":[[2022,3,2]]}}}