We investigate the task of inserting new concepts extracted from texts into an ontology using language models. We explore an approach with three steps: edge search which is to find a set of candidate locations to insert (i.e., subsumptions between concepts), edge formation and enrichment which leverages the ontological structure to produce and enhance the edge candidates, and edge selection which eventually locates the edge to be placed into. In all steps, we propose to leverage neural methods, where we apply embedding-based methods and contrastive learning with Pre-trained Language Models (PLMs) such as BERT for edge search, and adapt a BERT fine-tuning-based multi-label Edge-Cross-encoder, and Large Language Models (LLMs) such as GPT series, FLAN-T5, and Llama 2, for edge selection. We evaluate the methods on recent datasets created using the SNOMED CT ontology and the MedMentions entity linking benchmark. The best settings in our framework use fine-tuned PLM for search and a multi-label Cross-encoder for selection. Zero-shot prompting of LLMs is still not adequate for the task, and we propose explainable instruction tuning of LLMs for improved performance. Our study shows the advantages of PLMs and highlights the encouraging performance of LLMs that motivates future studies.
Featuring exclusive new work, Precious and Impossible gathers together over three decades of poetry from the T S Eliot Prize-winning Anthony Joseph. With an introduction by Lauri ScheyerFor over three decades, Anthony Joseph's work has explored the transnational vibrations of the African diaspora. Precious and Impossible brings together thirty years of Joseph's poetry and lyrics – cementing his status as one of our greatest living poets and polymaths'Joseph is both a faithful heir and an agnostic rebel; a Black poet haunted by Africa's past as well as a bilingual post-modernist amused by the possibilities of the future' Ali Alizadeh
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S. Kalathil, T. Li, H. Dong, and H. Liang. Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, page 207–215. SCITEPRESS - Science and Technology Publications, (2024)