Papers with DeepPath
Scalable Construction and Reasoning of Massive Knowledge Bases (N18-6)
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| Challenge: | Existing knowledge mining systems assume abundant human annotations for training high quality machine learning models, which is impractical when trying to deploy IE systems to a broad range of domains, settings and languages. |
| Approach: | They introduce how to extract structured facts from text corpora to construct knowledge bases. |
| Outcome: | The proposed methods are weakly-supervised and domain-independent for knowledge base construction across various domains. |
Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning (D19-1)
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| Challenge: | Existing methods for learning knowledge Graphs are incomplete and therefore need well-pretraining. |
| Approach: | They propose a deep reinforcement learning based model which incorporates LSTM and Graph Attention Mechanism as the memory components. |
| Outcome: | The proposed model can get rid of the pretraining process and achieve state-of-the-art performance compared with the other models. |