SumMER: Structural Summarization for RDF/S KGs
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. S...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/16/1/18 |
| _version_ | 1827629411885645824 |
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| author | Georgia Eirini Trouli Alexandros Pappas Georgia Troullinou Lefteris Koumakis Nikos Papadakis Haridimos Kondylakis |
| author_facet | Georgia Eirini Trouli Alexandros Pappas Georgia Troullinou Lefteris Koumakis Nikos Papadakis Haridimos Kondylakis |
| author_sort | Georgia Eirini Trouli |
| collection | DOAJ |
| description | 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’s importance. Moving from one centrality measure to many however, has the potential to generate a more objective view on nodes’ 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. |
| first_indexed | 2024-03-09T13:50:05Z |
| format | Article |
| id | doaj.art-664351d3faf54b0490dd5d18490d8676 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-09T13:50:05Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-664351d3faf54b0490dd5d18490d86762023-11-30T20:51:15ZengMDPI AGAlgorithms1999-48932022-12-011611810.3390/a16010018SumMER: Structural Summarization for RDF/S KGsGeorgia Eirini Trouli0Alexandros Pappas1Georgia Troullinou2Lefteris Koumakis3Nikos Papadakis4Haridimos Kondylakis5Department of Electrical and Computer Engineering, Hellenic Mediterranean University (HMU), 71309 Heraklion, GreeceComputer Science Department, University of Crete, 70013 Crete, GreeceInstitute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, GreeceInstitute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, GreeceDepartment of Electrical and Computer Engineering, Hellenic Mediterranean University (HMU), 71309 Heraklion, GreeceInstitute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, GreeceKnowledge 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’s importance. Moving from one centrality measure to many however, has the potential to generate a more objective view on nodes’ 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.https://www.mdpi.com/1999-4893/16/1/18RDF KGssemantic summariesgraph summaries |
| spellingShingle | Georgia Eirini Trouli Alexandros Pappas Georgia Troullinou Lefteris Koumakis Nikos Papadakis Haridimos Kondylakis SumMER: Structural Summarization for RDF/S KGs Algorithms RDF KGs semantic summaries graph summaries |
| title | SumMER: Structural Summarization for RDF/S KGs |
| title_full | SumMER: Structural Summarization for RDF/S KGs |
| title_fullStr | SumMER: Structural Summarization for RDF/S KGs |
| title_full_unstemmed | SumMER: Structural Summarization for RDF/S KGs |
| title_short | SumMER: Structural Summarization for RDF/S KGs |
| title_sort | summer structural summarization for rdf s kgs |
| topic | RDF KGs semantic summaries graph summaries |
| url | https://www.mdpi.com/1999-4893/16/1/18 |
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