Papers with YAGO
Mind the Labels: Describing Relations in Knowledge Graphs With Pretrained Models (2023.eacl-main)
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| Challenge: | Pretrained language models (PLMs) for data-to-text generation produce inaccurate outputs if labels are ambiguous or incomplete, which is often the case in D2T datasets. |
| Approach: | They propose to use a dataset to descib a relation between two entities using relation labels to train pretrained language models. |
| Outcome: | The proposed models are robust to generalizing to out-of-domain domains on a dataset for descibing a relation between two entities. |
Differentiating Concepts and Instances for Knowledge Graph Embedding (D18-1)
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| Challenge: | Existing knowledge graph embedding methods encode concepts and instances as vectors in a low-dimensional space, ignoring the difference between concepts and instance. |
| Approach: | They propose a knowledge graph embedding model that separates concepts from instances by differentiating concepts and instances. |
| Outcome: | The proposed model outperforms state-of-the-art methods on link prediction and triple classification tasks on YAGO dataset. |
KORE 50ˆDYWC: An Evaluation Data Set for Entity Linking Based on DBpedia, YAGO, Wikidata, and Crunchbase (2020.lrec-1)
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| Challenge: | A major domain of research in natural language processing is named entity recognition and disambiguation (NERD). |
| Approach: | They extend a widely-used data set to include NERD tasks for DBpedia and YAGO, Wikidata and Crunchbase. |
| Outcome: | The extended data set allows for a broader spectrum of evaluation. |
Aligning Cross-Lingual Entities with Multi-Aspect Information (D19-1)
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| Challenge: | Existing knowledge graphs that represent entities in different languages are not covered by existing systems. |
| Approach: | They propose two ways to embed entities from multilingual knowledge graphs into the same vector space, where equivalent entities are close to each other. |
| Outcome: | The proposed method significantly outperforms existing systems on two benchmark datasets. |
Connecting Embeddings for Knowledge Graph Entity Typing (2020.acl-main)
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| Challenge: | Existing knowledge graphs suffer from incompleteness and miss important facts, jeopardizing their usefulness in downstream tasks such as question answering. |
| Approach: | They propose a method which is trained by utilizing local typing knowledge from existing entity type assertions and global triple knowledge in KGs. |
| Outcome: | The proposed model favors inferences that agree with both entity type instances and triple knowledge in KGs. |