Ontology Enhanced Claim Detection
Zehra Melce Hüsünbeyi Tatjana Scheffler
We propose an ontology enhanced model for sentence based claim detection. We
fused ontology embeddings from a knowledge base with BERT sentence embeddings
to perform claim detection for the ClaimBuster and the NewsClaims datasets. Our
ontology enhanced approach showed the best results with these small-sized
unbalanced datasets, compared to other statistical and neural machine learning
models. The experiments demonstrate that adding domain specific features
(either trained word embeddings or knowledge graph metadata) can improve
traditional ML methods. In addition, adding domain knowledge in the form of
ontology embeddings helps avoid the bias encountered in neural network based
models, for example the pure BERT model bias towards larger classes in our
small corpus.