Papers with graph completion
StATIK: Structure and Text for Inductive Knowledge Graph Completion (2022.findings-naacl)
Copied to clipboard
Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Murali Annavaram, Aram Galstyan, Greg Ver Steeg
| Challenge: | Knowledge graphs (KGs) represent incomplete knowledge bases. |
| Approach: | They propose to use language models to extract semantic information from text descriptions while using Message Passing Neural Networks to capture structural information. |
| Outcome: | The proposed model achieves state of the art on three challenging inductive baselines. |
Knowledge GeoGebra: Leveraging Geometry of Relation Embeddings in Knowledge Graph Completion (2024.lrec-main)
Copied to clipboard
| Challenge: | Knowledge graph embedding models are limited to the algebra and geometry of the entity embeddable space, the algebra of the relation embeddible space, and the interaction between relation and entity embeds. |
| Approach: | They propose a method that leverages the geometry of relation embeddings and generalizes it with the concept of a butterfly curve, consecutively. |
| Outcome: | The proposed model outperforms existing models on the WN18RR, FB15K-237 and YouTube benchmarks. |