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Graph measures capture such particularities through descriptive statistics. Besides the classical set of measures established in the field of network analysis, such as size and volume of the graph or the type of degree distribution of its vertices, there has been some effort to define measures that capture some of the aforementioned particularities RDF graphs adhere to. However, some of them are redundant, computationally expensive, and not meaningful enough to describe RDF graphs. In particular, it is not clear which of them are efficient metrics to capture specific distinguishing characteristics of datasets in different knowledge domains (e.g.,\n                    <jats:italic toggle=\"yes\">Cross Domain<\/jats:italic>\n                    vs.\n                    <jats:italic toggle=\"yes\">Linguistics<\/jats:italic>\n                    ). In this work, we address the problem of identifying a minimal set of measures that is efficient, essential (non-redundant), and meaningful. Based on 54 measures and a sample of 280 graphs of nine knowledge domains from the Linked Open Data Cloud, we identify an essential set of 13 measures, having the capacity to describe graphs concisely. These measures have the capacity to present the topological structures and differences of datasets in established knowledge domains.\n                  <\/jats:p>","DOI":"10.3233\/sw-200409","type":"journal-article","created":{"date-parts":[[2020,10,20]],"date-time":"2020-10-20T11:05:37Z","timestamp":1603191937000},"page":"789-812","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Charaterizing RDF graphs through graph-based measures \u2013 framework and assessment"],"prefix":"10.1177","volume":"12","author":[{"given":"Matth\u00e4us","family":"Zloch","sequence":"first","affiliation":[{"name":"Leibniz-Institute for the Social Sciences","place":["Germany"]},{"name":"Heinrich-Heine University","place":["Germany"]}]},{"given":"Maribel","family":"Acosta","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology","place":["Germany"]},{"name":"Ruhr-University Bochum","place":["Germany"]}]},{"given":"Daniel","family":"Hienert","sequence":"additional","affiliation":[{"name":"Leibniz-Institute for the Social Sciences","place":["Germany"]}]},{"given":"Stefan","family":"Conrad","sequence":"additional","affiliation":[{"name":"Heinrich-Heine University","place":["Germany"]}]},{"given":"Stefan","family":"Dietze","sequence":"additional","affiliation":[{"name":"Leibniz-Institute for the Social Sciences","place":["Germany"]},{"name":"Heinrich-Heine University","place":["Germany"]}]}],"member":"179","published-online":{"date-parts":[[2020,10,20]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","unstructured":"Z.Abedjan T.Gr\u00fctze A.Jentzsch and F.Naumann Profiling and mining RDF data with ProLOD++ in: IEEE 30th International Conference on Data Engineering \u2013 ICDE 2014 I.F.Cruz E.Ferrari Y.Tao E.Bertino and G.Trajcevski eds IEEE Computer Society Los Alamitos CA USA 2014 pp. 1198\u20131201 ISBN 978-1-4799-3480-5. doi:10.1109\/ICDE.2014.6816740.","DOI":"10.1109\/ICDE.2014.6816740"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","unstructured":"A.Abele Linked data profiling: Identifying the domain of datasets based on data content and metadata in: Proceedings of the 25th International Conference Companion on World Wide Web J.Bourdeau J.Hendler R.Nkambou I.Horrocks and B.Y.Zhao eds WWW \u201916 Companion International World Wide Web Conferences Steering Committee Republic and Canton of Geneva CHE 2016 pp. 287\u2013291. 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