INAS

In scientific argumentation, a carefully developed and thought-through hypothesis is often crucial for researchers to be able to conduct a successful study and, in the end, present a new, high-quality finding or argument. Thus, an initial hypothesis needs to be specific enough that a researcher can test it based on data, but, at the same time, it should also relate to and extend previous general claims made in the community. In this project, we investigate how argumentation machines can (i) represent concrete and more abstract knowledge on hypotheses and their underlying concepts, (ii) automatically compute semantic relations between hypotheses made in scientific publications, and between hypotheses and datasets, and (iii) interactively support users in developing their own hypothesis based on these resources. This project will thus combine methods from different disciplines: natural language processing, knowledge representation and semantic web and -- as an example for a scientific domain -- invasion biology. Our starting point is an existing resource in invasion biology that organizes and relates core hypotheses in the field and associates them to meta-data for related publications and studies in terms of a network (https://hi-knowledge.org). This network, however, is currently static (i.e. needs substantial manual curation to be extended to incorporate new claims) and, moreover, is not easily accessible for users who miss specific background and domain knowledge in invasion biology. Our goal is to develop (i) a semantic model for representing knowledge on concepts and hypotheses, such that also non-expert users can use the network; (ii) a tool that automatically computes links from publication abstracts (and data) to these hypotheses and detects new hypotheses to obtain a dynamic network; and (iii) an interactive system that supports users in refining and relating their initial, potentially underdeveloped hypothesis.

Website: https://inas-argumentation.github.io/
This project is part of our Hi Knowledge initiative: https://hi-knowledge.org.