  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.
