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Title: Using ORKG Ask for search and exploration of scientific articles
ORKG Ask is an open-source scholarly search and exploration system supported by Artificial Intelligence (AI), making it possible to find relevant articles across more than 75 million items. With Ask, researchers are able to find scholarly literature by asking specific research questions. A summary answers the research question based on the most relevant literature. Furthermore, the research question is answered for each listed article. In addition, key insights from research articles are automatically extracted. Finally, powerful filtering options, including semantic concepts, make it possible to narrow down the search and find articles researchers are looking for. In this presentation we will discuss the individual features of the ORKG Ask, focusing on how Ask can be used when doing research, while also mentioning the limitations and challenges when using AI for finding scholarly literature.
Allard is a postdoctoral researcher at TIB and frontend lead for the Open Research Knowledge Graph (ORKG). He obtained a PhD in Computer Science at the Leibniz University Hannover in Germany. He considers himself a full-stack developer interested in the full lifecycle of tools. His research activities are focused on Semantic and Web Technologies, Human Computer Interaction, Crowd-sourced and Collaborative Knowledge Graph Authoring, User Interface Design and Development, User Experience Engineering and Web accessibility.
Background:
As an outcome of the workshop on Large Language Models and the future of scientific publishing we are organizing follow up online talks giving the interested audience tools at hand helping in the scientific publishing process.
ORKG Ask is listed as NFDI4DS service.
NFDIMatWerk, KonsortSWD, NFDI4Chem, NFDI4Earth, Text+, NFDI4DataScience