A Good Neighbor, A Found Treasure: Mining Treasured Neighbors for Knowledge Graph Entity Typing (2022.emnlp-main)
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| Challenge: | Existing methods to infer missing types for knowledge graphs only leverage one-hop neighbor information of the central entity, ignoring multi-hop neighbors that can provide valuable clues for inference. |
| Approach: | They propose a method to infer missing types for knowledge graph entities by using neighbor information and co-occurrence relations between types. |
| Outcome: | The proposed method significantly outperforms existing state-of-the-art methods on two widely used datasets. |
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| Challenge: | Existing knowledge graphs are incomplete whether they are constructed manually or automatically, limiting the effectiveness when exploited for downstream applications. |
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