KnowledgeHub: An End-to-End Tool for Assisted Scientific Discovery
KnowledgeHub: An End-to-End Tool for Assisted Scientific Discovery
Shinnosuke Tanaka, James Barry, Vishnudev Kuruvanthodi, Movina Moses, Maxwell J. Giammona, Nathan Herr, Mohab Elkaref, Geeth de Mel
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Demo Track. Pages 8815-8819.
https://doi.org/10.24963/ijcai.2024/1039
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. KnowledgeHub is a unique tool that supports annotation, IE and QA, which gives the user full insight into the knowledge discovery pipeline.
Keywords:
Natural Language Processing: NLP: Information extraction
Data Mining: DM: Information retrieval
Humans and AI: HAI: Human-AI collaboration
Machine Learning: ML: Relational learning
Natural Language Processing: NLP: Applications
Natural Language Processing: NLP: Information retrieval and text mining
Natural Language Processing: NLP: Language generation
Natural Language Processing: NLP: Language models
Natural Language Processing: NLP: Named entities
Natural Language Processing: NLP: Question answering
Natural Language Processing: NLP: Summarization
Natural Language Processing: NLP: Tagging, chunking, and parsing
Natural Language Processing: NLP: Tools