A Novel Multimodal Deep Neural Network Framework for Extending Knowledge Base
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Redalyc
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| Format: | Artículo científico |
| Langue: | en |
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Instituto Politécnico Nacional
2016
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| _version_ | 1876466051879796736 |
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| author | Zhao Yu |
| author_facet | Zhao Yu |
| contents | A Novel Multimodal Deep Neural Network Framework for Extending Knowledge Base Zhao Yu Gao Sheng Patrick Gallinari Guo Jun Computación embedding based model word embedding deep neural network Extending knowledge base 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. 2016 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61547469014 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.20 |
| format | Artículo científico |
| id | redalyc_61547469014 |
| institution | Redalyc |
| language | en |
| publishDate | 2016 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | A Novel Multimodal Deep Neural Network Framework for Extending Knowledge Base Zhao Yu Computación embedding based model word embedding deep neural network Extending knowledge base A Novel Multimodal Deep Neural Network Framework for Extending Knowledge Base Zhao Yu Gao Sheng Patrick Gallinari Guo Jun Computación embedding based model word embedding deep neural network Extending knowledge base 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. 2016 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61547469014 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.20 |
| title | A Novel Multimodal Deep Neural Network Framework for Extending Knowledge Base |
| topic | Computación embedding based model word embedding deep neural network Extending knowledge base |
| url | https://www.redalyc.org/articulo.oa?id=61547469014 |