ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction
This work addresses the challenge of extracting relations from biomedical text, which often yields unsatisfactory results with existing methods, by integrating symbolic knowledge to enhance accuracy.
The paper tackles the problem of biomedical relation extraction by introducing ReOnto, a neuro-symbolic approach that combines graph neural networks with ontologies as prior knowledge, resulting in performance improvements of approximately 3% on BioRel and ADE datasets.
Relation Extraction (RE) is the task of extracting semantic relationships between entities in a sentence and aligning them to relations defined in a vocabulary, which is generally in the form of a Knowledge Graph (KG) or an ontology. Various approaches have been proposed so far to address this task. However, applying these techniques to biomedical text often yields unsatisfactory results because it is hard to infer relations directly from sentences due to the nature of the biomedical relations. To address these issues, we present a novel technique called ReOnto, that makes use of neuro symbolic knowledge for the RE task. ReOnto employs a graph neural network to acquire the sentence representation and leverages publicly accessible ontologies as prior knowledge to identify the sentential relation between two entities. The approach involves extracting the relation path between the two entities from the ontology. We evaluate the effect of using symbolic knowledge from ontologies with graph neural networks. Experimental results on two public biomedical datasets, BioRel and ADE, show that our method outperforms all the baselines (approximately by 3\%).