{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T12:16:12Z","timestamp":1773317772622,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T00:00:00Z","timestamp":1672099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hellenic Foundation for Research and Innovation (H.F.R.I.) under the \u201c2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers\u201d","award":["1147"],"award-info":[{"award-number":["1147"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Knowledge graphs are becoming more and more prevalent on the web, ranging from small taxonomies, to large knowledge bases containing a vast amount of information. To construct such knowledge graphs either automatically or manually, tools are necessary for their quick exploration and understanding. Semantic summaries have been proposed as a key technology enabling the quick understanding and exploration of large knowledge graphs. Among the methods proposed for generating summaries, structural methods exploit primarily the structure of the graph in order to generate the result summaries. Approaches in the area focus on identifying the most important nodes and usually employ a single centrality measure, capturing a specific perspective on the notion of a node\u2019s importance. Moving from one centrality measure to many however, has the potential to generate a more objective view on nodes\u2019 importance, leading to better summaries. In this paper, we present SumMER, the first structural summarization technique exploiting machine learning techniques for RDF\/S KGs. SumMER explores eight centrality measures and then exploits machine learning techniques for optimally selecting the most important nodes. Then those nodes are linked formulating a subgraph out of the original graph. We experimentally show that combining centrality measures with machine learning effectively increases the quality of the generated summaries.<\/jats:p>","DOI":"10.3390\/a16010018","type":"journal-article","created":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T05:30:27Z","timestamp":1672205427000},"page":"18","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["SumMER: Structural Summarization for RDF\/S KGs"],"prefix":"10.3390","volume":"16","author":[{"given":"Georgia","family":"Trouli","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Hellenic Mediterranean University (HMU), 71309 Heraklion, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandros","family":"Pappas","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of Crete, 70013 Crete, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Georgia","family":"Troullinou","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8442-4630","authenticated-orcid":false,"given":"Lefteris","family":"Koumakis","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikos","family":"Papadakis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Hellenic Mediterranean University (HMU), 71309 Heraklion, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9917-4486","authenticated-orcid":false,"given":"Haridimos","family":"Kondylakis","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s00778-018-0528-3","article-title":"Summarizing semantic graphs: A survey","volume":"28","author":"Kondylakis","year":"2019","journal-title":"VLDB J."},{"key":"ref_2","unstructured":"Peroni, S., Motta, E., and d\u2019Aquin, M. 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