Phoenixes at LLMs4OL 2024 Tasks A, B, and C: Retrieval Augmented Generation for Ontology Learning

Abstract

Large language models (LLMs) showed great capabilities in ontology learning (OL) where they automatically extract knowledge from text. In this paper, we proposed a Retrieval Augmented Generation (RAG) formulation for three different tasks of ontology learning defined in the LLMs4OL Challenge at ISWC 2024. For task A - term typing - we considered terms as a query and encoded the query through the Query Encoder model for searching through knowledge base embedding of types embeddings obtained through Context Encoder. Next, using Zero-Shot Prompt template we asked LLM to determine what types are appropriate for a given term within the term typing task. Similarly, for Task B, we calculated the similarity matrix using an encoder-based transformer model, and by applying the similarity threshold we considered only similar pairs to query LLM to identify whatever pairs have the "is-a" relation between a given type and in a case of having the relationships which one is "parent" and which one is "child". In final, for Task C -- non-taxonomic relationship extraction -- we combined both approaches for Task A and B, where first using Task B formulation, child-parents are identified then using Task A, we assigned them an appropriate relationship. For the LLMs4OL challenge, we experimented with the proposed framework over 5 subtasks of Task A, all subtasks of Task B, and one subtask of Task C using Mistral-7B LLM.