16 citations · 55 across the 8 of their papers we have counts for
4 papers · 1 filter
InGram: Inductive Knowledge Graph Embedding via Relation Graphs
Jaejun Lee, Chanyoung Chung, Joyce Jiyoung Whang
Inductive knowledge graph completion has been considered as the task of predicting missing triplets between new entities that are not observed during training. While most inductive…
Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers
Chanyoung Chung, Jaejun Lee, Joyce Jiyoung Whang
A hyper-relational knowledge graph has been recently studied where a triplet is associated with a set of qualifiers; a qualifier is composed of a relation and an entity, providing…
Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise
Giwon Hong, Jeonghwan Kim, Junmo Kang +2
Most existing retrieval-augmented language models (LMs) assume a naive dichotomy within a retrieved document set: query-relevance and irrelevance. Our work investigates a more chal…
Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Prediction
Chanyoung Chung, Joyce Jiyoung Whang
Knowledge graphs represent known facts using triplets. While existing knowledge graph embedding methods only consider the connections between entities, we propose considering the r…