# Ontology Enhanced Claim Detection

arXiv: https://arxiv.org/abs/2402.12282 · HTML: https://mlsanity.com/paper/2402.12282/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [OWL2Vec*: Embedding of OWL Ontologies](https://mlsanity.com/paper/2009.14654/) (2020)
- [Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection](https://mlsanity.com/paper/1809.08193/) (2018)
- [A Benchmark Dataset of Check-worthy Factual Claims](https://mlsanity.com/paper/2004.14425/) (2020)
- [NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge](https://mlsanity.com/paper/2112.08544/) (2021)
- [Neural Check-Worthiness Ranking with Weak Supervision: Finding Sentences for Fact-Checking](https://mlsanity.com/paper/1903.08404/) (2019)
- [Gradient-Based Adversarial Training on Transformer Networks for Detecting Check-Worthy Factual Claims](https://mlsanity.com/paper/2002.07725/) (2020)
- [Ontology-based Feature Selection: A Survey](https://mlsanity.com/paper/2104.07720/) (2021)

## Abstract (arXiv)

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.