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EnglishSemantic models are fundamental to publish datainto Knowledge Graphs (KGs), since they encodethe precise meaning of data sources, through con-cepts and properties defined within reference on-tologies. However, building semantic models re-quires significant manual effort and expertise. Inthis paper, we present a novel approach based onGraph Neural Networks (GNNs) to build seman-tic models of data sources. GNNs are trained onLinked Data (LD) graphs, which serve as back-ground knowledge to automatically infer the se-mantic relations connecting the attributes of a datasource. At the best of our knowledge, this is thefirst approach that employs GNNs to identify thesemantic relations. We tested our approach on 15target sources from the advertising domain (usedin other studies in the literature), and comparedits performance against two baselines and a tech-nique largely used in the state of the art. Theevaluation showed that our approach outperformsthe state of the art in cases of data source withthe largest amount of semantic relations definedin the ground truth.
Modeling the semantics of data sources with graph neural networks / Futia, Giuseppe; Garifo, Giovanni; Vetro', Antonio; De Martin, Juan Carlos. - ELETTRONICO. - (2020). ((Intervento presentato al convegno International Conference on Machine Learning 2020 - Workshop "Bridge Between Perception and Reasoning: Graph Neural Networks & Beyond" tenutosi a Vienna, Austria nel 18/07/2020.
| Titolo: | Modeling the semantics of data sources with graph neural networks |
| Autori: | |
| Data di pubblicazione: | 2020 |
| Citazione: | Modeling the semantics of data sources with graph neural networks / Futia, Giuseppe; Garifo, Giovanni; Vetro', Antonio; De Martin, Juan Carlos. - ELETTRONICO. - (2020). ((Intervento presentato al convegno International Conference on Machine Learning 2020 - Workshop "Bridge Between Perception and Reasoning: Graph Neural Networks & Beyond" tenutosi a Vienna, Austria nel 18/07/2020. |
| Abstract: | Semantic models are fundamental to publish datainto Knowledge Graphs (KGs), since they encodethe precise meaning of data sources, through con-cepts and properties defined within reference on-tologies. However, building semantic models re-quires significant manual effort and expertise. Inthis paper, we present a novel approach based onGraph Neural Networks (GNNs) to build seman-tic models of data sources. GNNs are trained onLinked Data (LD) graphs, which serve as back-ground knowledge to automatically infer the se-mantic relations connecting the attributes of a datasource. At the best of our knowledge, this is thefirst approach that employs GNNs to identify thesemantic relations. We tested our approach on 15target sources from the advertising domain (usedin other studies in the literature), and comparedits performance against two baselines and a tech-nique largely used in the state of the art. Theevaluation showed that our approach outperformsthe state of the art in cases of data source withthe largest amount of semantic relations definedin the ground truth. |
| Handle: | http://hdl.handle.net/11583/2855045 |
| Appare nelle tipologie: | 5.15 Pubblicazione su portale |
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| Modeling the semantics of data sources with graph neural networks.pdf | Modeling the semantics of data sources with graph neural networks | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2855045
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