This paper describes the KnowledgeHub tool, a scientific literature Information Extraction (IE) and Question Answering (QA) pipeline. This is achieved by supporting the ingestion of PDF documents that are converted to text and structured representations. An ontology can then be constructed where a user defines the types of entities and relationships they want to capture. A browser-based annotation tool enables annotating the contents of the PDF documents according to the ontology. Named Entity Recognition (NER) and Relation Classification (RC) models can be trained on the resulting annotations and can be used to annotate the unannotated portion of the documents. A knowledge graph is constructed from these entity and relation triples which can be queried to obtain insights from the data. Furthermore, we integrate a suite of Large Language Models (LLMs) that can be used for QA and summarisation that is grounded in the included documents via a retrieval component. KnowledgeHub is a unique tool that supports annotation, IE and QA, which gives the user full insight into the knowledge discovery pipeline.

该研究描述了KnowledgeHub工具，一种科学文献信息提取和问题回答的流程，通过支持将PDF文档转换为文本和结构化表示，以构建本体论，并使用基于浏览器的注释工具对PDF文档的内容进行注释，然后通过训练命名实体识别和关系分类模型来构建知识图谱，结合大型语言模型进行问题回答和摘要生成，支持用户全面了解知识发现流程。

KnowledgeHub：辅助科学发现的端到端工具