Abstractified Multi-instance Learning (AMIL) for Biomedical Relation Extraction

arXiv (Cornell University) · 2021

• Computer Science
• Computer Science
• Artificial Intelligence

Relation extraction in the biomedical domain is a challenging task due to a lack of labeled data and a long-tail distribution of fact triples. Many works leverage distant supervision which automatically generates labeled data by pairing a knowledge graph with raw textual data. Distant supervision produces noisy labels and requires additional techniques, such as multi-instance learning (MIL), to denoise the training signal. However, MIL requires multiple instances of data and struggles with very long-tail datasets such as those found in the biomedical domain. In this work, we propose a novel reformulation of MIL for biomedical relation extraction that abstractifies biomedical entities into their corresponding semantic types. By grouping entities by types, we are better able to take advantage of the benefits of MIL and further denoise the training signal. We show this reformulation, which we refer to as abstractified multi-instance learning (AMIL), improves performance in biomedical relationship extraction. We also propose a novel relationship embedding architecture that further improves model performance.

Theoretical Knowledge Graph Reasoning via Ending Anchored Rules.

arXiv (Cornell University) · 2020 · 1 citations

• Computer Science
• Computer Science
• Artificial Intelligence

Discovering precise and specific rules from knowledge graphs is regarded as an essential challenge, which can improve the performances of many downstream tasks and even provide new ways to approach some Natural Language Processing research topics. In this paper, we provide a fundamental theory for knowledge graph reasoning based on the ending anchored rules. Our theory provides precise reasons explaining why or why not a triple is correct. Then, we implement our theory by what we call the EARDict model. Results show that our EARDict model significantly outperforms all the benchmark models on three large datasets of knowledge graph completion. Especially, our model achieves a Hits@10 score of 96.6 percent on WN18RR.