Mapping Art to a Knowledge Graph: Using Data for Exploring the Relations among Visual Objects in Renaissance Art
Graph-like structures, which are increasingly popular in data representation, stand out since they enable the integration of information from multiple sources. At the same time, clustering algorithms applied on graphs allow for group entities based on similar characteristics, and discover statistica...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/14/7/206 |
| _version_ | 1797406309326258176 |
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| author | Alexandros Kouretsis Iraklis Varlamis Laida Limniati Minas Pergantis Andreas Giannakoulopoulos |
| author_facet | Alexandros Kouretsis Iraklis Varlamis Laida Limniati Minas Pergantis Andreas Giannakoulopoulos |
| author_sort | Alexandros Kouretsis |
| collection | DOAJ |
| description | Graph-like structures, which are increasingly popular in data representation, stand out since they enable the integration of information from multiple sources. At the same time, clustering algorithms applied on graphs allow for group entities based on similar characteristics, and discover statistically important information. This paper aims to explore the associations between the visual objects of the Renaissance in the Europeana database, based on the results of topic modeling and analysis. For this purpose, we employ Europeana’s Search and Report API to investigate the relations between the visual objects from this era, spanning from the 14th to the 17th century, and to create clusters of similar art objects. This approach will lead in transforming a cultural heritage database with semantic technologies into a dynamic digital knowledge representation graph that will relate art objects and their attributes. Based on associations between metadata, we will conduct a statistic analysis utilizing the knowledge graph of Europeana and topic modeling analysis. |
| first_indexed | 2024-03-09T03:24:33Z |
| format | Article |
| id | doaj.art-7cf6ecc3ac0a48b1b0d59fac8bf133a5 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-09T03:24:33Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-7cf6ecc3ac0a48b1b0d59fac8bf133a52023-12-03T15:04:34ZengMDPI AGFuture Internet1999-59032022-07-0114720610.3390/fi14070206Mapping Art to a Knowledge Graph: Using Data for Exploring the Relations among Visual Objects in Renaissance ArtAlexandros Kouretsis0Iraklis Varlamis1Laida Limniati2Minas Pergantis3Andreas Giannakoulopoulos4Department of Audio and Visual Arts, Ionian University, 7 Tsirigoti Square, 49100 Corfu, GreeceDepartment of Informatics and Telematics, Harokopio University of Athens, Omirou 9, Tavros, 17778 Athens, GreeceBrilliantPR Digital Agency, 340 Kifisias Str., 15451 Athens, GreeceDepartment of Audio and Visual Arts, Ionian University, 7 Tsirigoti Square, 49100 Corfu, GreeceDepartment of Audio and Visual Arts, Ionian University, 7 Tsirigoti Square, 49100 Corfu, GreeceGraph-like structures, which are increasingly popular in data representation, stand out since they enable the integration of information from multiple sources. At the same time, clustering algorithms applied on graphs allow for group entities based on similar characteristics, and discover statistically important information. This paper aims to explore the associations between the visual objects of the Renaissance in the Europeana database, based on the results of topic modeling and analysis. For this purpose, we employ Europeana’s Search and Report API to investigate the relations between the visual objects from this era, spanning from the 14th to the 17th century, and to create clusters of similar art objects. This approach will lead in transforming a cultural heritage database with semantic technologies into a dynamic digital knowledge representation graph that will relate art objects and their attributes. Based on associations between metadata, we will conduct a statistic analysis utilizing the knowledge graph of Europeana and topic modeling analysis.https://www.mdpi.com/1999-5903/14/7/206machine learningdata miningvisualizationtopic modelingcluster analysisknowledge graph |
| spellingShingle | Alexandros Kouretsis Iraklis Varlamis Laida Limniati Minas Pergantis Andreas Giannakoulopoulos Mapping Art to a Knowledge Graph: Using Data for Exploring the Relations among Visual Objects in Renaissance Art Future Internet machine learning data mining visualization topic modeling cluster analysis knowledge graph |
| title | Mapping Art to a Knowledge Graph: Using Data for Exploring the Relations among Visual Objects in Renaissance Art |
| title_full | Mapping Art to a Knowledge Graph: Using Data for Exploring the Relations among Visual Objects in Renaissance Art |
| title_fullStr | Mapping Art to a Knowledge Graph: Using Data for Exploring the Relations among Visual Objects in Renaissance Art |
| title_full_unstemmed | Mapping Art to a Knowledge Graph: Using Data for Exploring the Relations among Visual Objects in Renaissance Art |
| title_short | Mapping Art to a Knowledge Graph: Using Data for Exploring the Relations among Visual Objects in Renaissance Art |
| title_sort | mapping art to a knowledge graph using data for exploring the relations among visual objects in renaissance art |
| topic | machine learning data mining visualization topic modeling cluster analysis knowledge graph |
| url | https://www.mdpi.com/1999-5903/14/7/206 |
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