ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction
Monika Jain Kuldeep Singh Raghava Mutharaju
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\%).