Integrating Lexical Information into Entity Neighbourhood Representations for Relation Prediction (2021.naacl-main)
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| Challenge: | Existing methods to predict knowledge base relations are limited by maintenance costs and text-based formats. |
| Approach: | They propose a system that can extend relational database tables with information extracted from a document corpus. |
| Outcome: | The proposed system outperforms existing methods by incorporating embeddings of text-based representations of the entities and relations. |
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| Challenge: | Recent work has demonstrated that entity representations can be extracted from pre-trained language models to develop knowledge graph completion models that are more robust to the naturally occurring sparsity found in knowledge graphs. |
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| Challenge: | Existing knowledge graphs (KGs) are incomplete or partial information, in the form of missing relations between entities, which gives rise to the task of knowledge base completion (also known as relation prediction). |
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| Challenge: | Knowledge Bases (KBs) require constant updating to reflect changes to the world they represent. |
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Relation Prediction for Unseen-Entities Using Entity-Word Graphs (D19-53)
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Yuki Tagawa, Motoki Taniguchi, Yasuhide Miura, Tomoki Taniguchi, Tomoko Ohkuma, Takayuki Yamamoto, Keiichi Nemoto
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A Frustratingly Easy Approach for Entity and Relation Extraction (2021.naacl-main)
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| Challenge: | Existing work on end-to-end relation extraction models combine two tasks: named entity recognition and relation extraction. |
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A2N: Attending to Neighbors for Knowledge Graph Inference (P19-1)
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| Challenge: | Existing knowledge graph completion methods learn a fixed embedding for every entity, which is suboptimal as it requires memorizing and generalizing to all possible entity relationships. |
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