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Found 4 papers, 2 papers with code
Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesics
Existing graph domain adaptation (graph DA) methods often implicitly assume a \textit{mild} shift between source and target graphs, limiting their applicability to real-world scenarios with \textit{large} shifts.
Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning
State-of-the-art methods fall short in the speculative reasoning ability, as they assume the correctness of a fact is solely determined by its presence in KG, making them vulnerable to false negative/positive issues.
Hierarchical Overlapping Belief Estimation by Structured Matrix Factorization
In the synthetic dataset, our model reduces error by 40%.
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
We propose a novel domain-independent framework, called CoType, that runs a data-driven text segmentation algorithm to extract entity mentions, and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces (for entity and relation mentions respectively), where, in each space, objects whose types are close will also have similar representations.
Ranked #11 on
Relation Extraction
on NYT11-HRL
