Cross-validation of Answers with SUMO and GPT
Abstract
We have developed a tool for fact-checking in automated question answering based on four technologies: (i) the Suggested Upper Merged Ontology (SUMO) for knowledge representation, (ii) the Vampire theorem prover [1] for fact verification, (iii) WordNet for lexical semantics and (iv) GPT (Generative Pretrained Transformer) for concept learning and alignment. SUMO provides a structured representation of knowledge in an expressive logic, facilitating semantic understanding and analysis. Vampire serves as an automated reasoning tool to check the validity of facts and claims. WordNet and GPT contribute to concept learning and alignment, enhancing the system’s ability to interpret natural language (NL) expressions and align them with the underlying ontological representations. By combining these components, the proposed framework offers a robust solution for fact-checking, combating misinformation, and promoting informed decision-making.
Full citation
"Cross-validation of Answers with SUMO and GPT", Lupu, Dan and Groza Adrian and Adam Pease, Knowledge Base Construction from Pre-Trained Language Models (KBC-LM@ 22nd International Semantic Web Conference (ISWC 2023)), Athens, Greece COEU, , DOI: , 2023.