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Jointly Linking Visual and Textual Entity Mentions with Background Knowledge
“A picture is worth a thousand words”, the adage reads. However, pictures cannot replace words in terms of their ability to efficiently convey clear (mostly) unambiguous and concise knowledge. Images and text, indeed, reveal different and complementary information that, if combined, result in more i...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298199/ http://dx.doi.org/10.1007/978-3-030-51310-8_24 |
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author | Dost, Shahi Serafini, Luciano Rospocher, Marco Ballan, Lamberto Sperduti, Alessandro |
author_facet | Dost, Shahi Serafini, Luciano Rospocher, Marco Ballan, Lamberto Sperduti, Alessandro |
author_sort | Dost, Shahi |
collection | PubMed |
description | “A picture is worth a thousand words”, the adage reads. However, pictures cannot replace words in terms of their ability to efficiently convey clear (mostly) unambiguous and concise knowledge. Images and text, indeed, reveal different and complementary information that, if combined, result in more information than the sum of that contained in the single media. The combination of visual and textual information can be obtained through linking the entities mentioned in the text with those shown in the pictures. To further integrate this with agent background knowledge, an additional step is necessary. That is, either finding the entities in the agent knowledge base that correspond to those mentioned in the text or shown in the picture or, extending the knowledge base with the newly discovered entities. We call this complex task Visual-Textual-Knowledge Entity Linking (VTKEL). In this paper, after providing a precise definition of the VTKEL task, we present a dataset composed of about 30K commented pictures, annotated with visual and textual entities, and linked to the YAGO ontology. Successively, we develop a purely unsupervised algorithm for the solution of the VTKEL tasks. The evaluation on the VTKEL dataset shows promising results. |
format | Online Article Text |
id | pubmed-7298199 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72981992020-06-17 Jointly Linking Visual and Textual Entity Mentions with Background Knowledge Dost, Shahi Serafini, Luciano Rospocher, Marco Ballan, Lamberto Sperduti, Alessandro Natural Language Processing and Information Systems Article “A picture is worth a thousand words”, the adage reads. However, pictures cannot replace words in terms of their ability to efficiently convey clear (mostly) unambiguous and concise knowledge. Images and text, indeed, reveal different and complementary information that, if combined, result in more information than the sum of that contained in the single media. The combination of visual and textual information can be obtained through linking the entities mentioned in the text with those shown in the pictures. To further integrate this with agent background knowledge, an additional step is necessary. That is, either finding the entities in the agent knowledge base that correspond to those mentioned in the text or shown in the picture or, extending the knowledge base with the newly discovered entities. We call this complex task Visual-Textual-Knowledge Entity Linking (VTKEL). In this paper, after providing a precise definition of the VTKEL task, we present a dataset composed of about 30K commented pictures, annotated with visual and textual entities, and linked to the YAGO ontology. Successively, we develop a purely unsupervised algorithm for the solution of the VTKEL tasks. The evaluation on the VTKEL dataset shows promising results. 2020-05-26 /pmc/articles/PMC7298199/ http://dx.doi.org/10.1007/978-3-030-51310-8_24 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Dost, Shahi Serafini, Luciano Rospocher, Marco Ballan, Lamberto Sperduti, Alessandro Jointly Linking Visual and Textual Entity Mentions with Background Knowledge |
title | Jointly Linking Visual and Textual Entity Mentions with Background Knowledge |
title_full | Jointly Linking Visual and Textual Entity Mentions with Background Knowledge |
title_fullStr | Jointly Linking Visual and Textual Entity Mentions with Background Knowledge |
title_full_unstemmed | Jointly Linking Visual and Textual Entity Mentions with Background Knowledge |
title_short | Jointly Linking Visual and Textual Entity Mentions with Background Knowledge |
title_sort | jointly linking visual and textual entity mentions with background knowledge |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298199/ http://dx.doi.org/10.1007/978-3-030-51310-8_24 |
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