图 书 馆
基于语言模型的本体论新概念布局框架A Language Model based Framework for New Concept Placement in Ontologies |
|
| 课程网址: | https://videolectures.net/eswc2024_dong_concept_placement/ |
| 主讲教师: | Han Dong |
| 开课单位: | 2024年上海世博会 |
| 开课时间: | 2024-06-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | 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. |
| 关 键 词: | 语言模型; 本体论新概念; 布局框架 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2024-08-05：liyq |
| 最后编审: | 2024-08-05：liyq |
| 阅读次数: | 15 |