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The Experts below are selected from a list of 21 Experts worldwide ranked by ideXlab platform
Yu Zhao – 1st expert on this subject based on the ideXlab platform
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Learning three-way affinity embeddings for knowledge base completion
2016 8th IEEE International Conference on Communication Software and Networks (ICCSN), 2016Co-Authors: Yu ZhaoAbstract:Knowledge bases are an extremely important database for knowledge management, which is very useful for question Answering, Query expansion and other related tasks. However, it often suffers from incompleteness. In this paper, we propose a Three-Way Affinity Embeddings model (TWAE) to map both the entity and relationship into two vectors and consider any two of them direct interaction, and then predict the possible truth of additional facts. The basic idea is that the confidence of the additional predicted fact is determined by three-way affinities in the triplet using the latent representation of each item. Experiments show that our model performs excellent.
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A Novel Multimodal Deep Neural Network Framework for Extending Knowledge Base
Computación y sistemas, 2016Co-Authors: Yu Zhao, Sheng Gao, Patrick Gallinari, Jun GuoAbstract: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.
Jun Guo – 2nd expert on this subject based on the ideXlab platform
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A Novel Multimodal Deep Neural Network Framework for Extending Knowledge Base
Computación y sistemas, 2016Co-Authors: Yu Zhao, Sheng Gao, Patrick Gallinari, Jun GuoAbstract: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.
Patrick Gallinari – 3rd expert on this subject based on the ideXlab platform
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A Novel Multimodal Deep Neural Network Framework for Extending Knowledge Base
Computación y sistemas, 2016Co-Authors: Yu Zhao, Sheng Gao, Patrick Gallinari, Jun GuoAbstract: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.