Large Language Models (LLMs) have received considerable interest in wide applications lately. During pre-training via massive datasets, such a model implicitly memorizes the factual knowledge of trained datasets in its hidden parameters. However, knowledge held implicitly in parameters often makes its use by downstream applications ineffective due to the lack of common-sense reasoning. In this article, we introduce a general framework that permits to build knowledge bases with an aid of LLMs, tailored for processing Web news. The framework applies a rule-based News Information Extractor (NewsIE) to news items for extracting their relational tuples, referred to as knowledge bases, which are then graph-convoluted with the implicit knowledge facts of news items obtained by LLMs, for their classification. It involves two lightweight components: 1) NewsIE: for extracting the structural information of every news item, in the form of relational tuples; 2) BERTGraph: for graph convoluting the implicit knowledge facts with relational tuples extracted by NewsIE. We have evaluated our framework under different news-related datasets for news category classification, with promising experimental results.

翻译：大型语言模型（LLMs）近年来在广泛的应用中受到极大关注。此类模型通过海量数据集进行预训练时，会将其训练数据中的事实知识隐式地记忆在隐藏参数中。然而，由于缺乏常识推理能力，这种隐式存储于参数中的知识往往导致下游应用难以有效利用。本文提出一种通用框架，旨在借助LLMs构建专门用于处理网络新闻的知识库。该框架首先采用基于规则的新闻信息抽取器（NewsIE）从新闻条目中提取关系元组（即知识库），随后通过图卷积操作将其与LLMs获取的新闻隐式知识事实进行融合，以完成新闻分类任务。该框架包含两个轻量级组件：1）NewsIE：用于以关系元组形式提取每条新闻的结构化信息；2）BERTGraph：用于对NewsIE提取的关系元组与隐式知识事实进行图卷积运算。我们在多个新闻相关数据集上对框架进行了新闻分类任务评估，实验结果展现出良好性能。