{"type":"ScholarlyArticle","title":"KnowledgeHub: An End-to-End Tool for Assisted Scientific Discovery","authors":[{"name":"Shinnosuke Tanaka","slug":"shinnosuke-tanaka","given":"Shinnosuke","family":"Tanaka","affiliation":"IBM Research Europe"},{"name":"James Barry","slug":"james-barry","given":"James","family":"Barry","affiliation":"IBM Research Europe"},{"name":"Vishnudev Kuruvanthodi","slug":"vishnudev-kuruvanthodi","given":"Vishnudev","family":"Kuruvanthodi","affiliation":"IBM Research Europe"},{"name":"Movina Moses","slug":"movina-moses","given":"Movina","family":"Moses","affiliation":"IBM Research"},{"name":"Maxwell J. Giammona","slug":"maxwell-j-giammona","given":"Maxwell J.","family":"Giammona","affiliation":"IBM Research"},{"name":"Nathan Herr","slug":"nathan-herr","given":"Nathan","family":"Herr","affiliation":"University College London"},{"name":"Mohab Elkaref","slug":"mohab-elkaref","given":"Mohab","family":"Elkaref","affiliation":"IBM Research Europe"},{"name":"Geeth de Mel","slug":"geeth-de-mel","given":"Geeth","family":"de Mel","affiliation":"IBM Research Europe"}],"abstract":"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.","doi":"10.24963/ijcai.2024/1039","url":"https://ijcai.org/proceedings/2024/1039","pdf":"https://ijcai.org/proceedings/2024/1039.pdf","markdown":"https://ijcai.org/proceedings/2024/1039.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/1039","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Demo Track","track":"Demo Track","number":1039,"pages":{"first":8815,"last":8819},"keywords":["Natural Language Processing: NLP: Applications","Machine Learning: ML: Relational learning","Natural Language Processing: NLP: Question answering","Natural Language Processing: NLP: Language models","Humans and AI: HAI: Human-AI collaboration","Natural Language Processing: NLP: Information retrieval and text mining","Natural Language Processing: NLP: Information extraction","Natural Language Processing: NLP: Named entities","Data Mining: DM: Information retrieval","Natural Language Processing: NLP: Language generation","Natural Language Processing: NLP: Tools","Natural Language Processing: NLP: Summarization","Natural Language Processing: NLP: Tagging, chunking, and parsing"],"videos":[],"references":[{"position":1,"raw":"[Brown et al., 2020] Tom Brown, Benjamin Mann, Nick Ry- der, Melanie Subbiah, Jared D Kaplan, Prafulla Dhari- wal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 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