Papers by Pankaj Kumar
Type-Sensitive Knowledge Base Inference Without Explicit Type Supervision (P18-2)
Copied to clipboard
| Challenge: | State-of-the-art knowledge base completion models make frequent errors when ranking entities that are not compatible with the type required by the relation. |
| Approach: | They propose to enhance each base factorization with two type-compatibility terms between entity-relation pairs and combine the signals in a novel manner. |
| Outcome: | The proposed model achieves 7% MRR gains over baseline models and predicts supervised types better than baseline models. |