A Novel Multimodal Deep Neural Network Framework for Extending Knowledge Base
Título del documento: A Novel Multimodal Deep Neural Network Framework for Extending Knowledge Base
Revista: Computación y sistemas
Base de datos: PERIÓDICA
Número de sistema: 000411064
ISSN: 1405-5546
Autors: 1
1
2
1
Institucions: 1Beijing University of Posts and Telecommunications, Beijing. China
2Universite Pierre et Marie Curie, París. Francia
Any:
Període: Jul-Sep
Volum: 20
Número: 3
Paginació: 459-466
País: México
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Experimental, aplicado
Resumen en inglés Knowledge base is a very important database for knowledge management, which is very useful for Question Answering, Query Expansion and other AI tasks. However, due to the fast-growing knowledge on the web and not all common knowledge expressed in the text is explicit, the knowledge base always suffers from incompleteness. Recently many researchers are trying to solve the problem as link prediction, only using the existing knowledge base, however, it is just knowledge base completion without adding new entities, which emerges from unstructured text not in existing knowledge base. In this paper, we propose a multimodal deep neural network framework that trying to learn new entities from unstructured text and to extend the knowledge base. Experiments demonstrate the excellent performance
Disciplines Ciencias de la computación,
Literatura y lingüística
Paraules clau: Procesamiento de datos,
Lingüística aplicada,
Lingüística computacional,
Inserción de palabras,
Bases de conocimiento,
Redes neuronales
Keyword: Computer science,
Literature and linguistics,
Data processing,
Applied linguistics,
Computing linguistics,
Word embedding,
Knowledge bases,
Neural networks
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