Ponencia
Semantic Preserving Embeddings for Multi-Relational Graphs
Autor/es | Almagro Blanco, Pedro
Sancho Caparrini, Fernando |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2017 |
Fecha de depósito | 2021-04-19 |
Publicado en |
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ISBN/ISSN | 978-1-5090-5443-5 |
Resumen | In this paper a new machine learning approach to
the study of Multi-Relational Graphs as semantic data structures
is presented. It shows how vector representations that maintain
semantic and topological features of the ... In this paper a new machine learning approach to the study of Multi-Relational Graphs as semantic data structures is presented. It shows how vector representations that maintain semantic and topological features of the original data can be obtained from neural encoding architectures and considering the topological properties of the graph. Also, semantic features of these new representations are tested by using some machine learning tasks and new directions on efficient link discovery methodologies on large relational datasets are investigated. |
Agencias financiadoras | Junta de Andalucía Ministerio de Economía y Competitividad (MINECO). España |
Identificador del proyecto | TIC-6064
TIN2013-41086-P |
Cita | Almagro Blanco, P. y Sancho Caparrini, F. (2017). Semantic Preserving Embeddings for Multi-Relational Graphs. En SAI 2017: Science and Information Conference (41-50), London, UK: IEEE Computer Society. |
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