Using Causal Threads to Explain Changes in a Dynamic System

We explore developing rich semantic models of systems. Specifically, we consider structured causal explanations about state changes in those systems. Essentially, we are developing process-based dynamic knowledge graphs. As an example, we construct a model of the causal threads for geological changes proposed by the Snowball Earth theory. Further, we describe an early prototype of a graphical interface to present the explanations. Unlike statistical approaches to summarization and explanation such as Large Language Models (LLMs), our approach of direct representation can be inspected and verified directly.Comment: 2023 ICAD

Rich Linking in a Digital Library of Full-Text Scientific Research Reports

With interactive full-text documents, there are opportunities to take advantage of the structure in scientific research reports which has not been systematically captured. We develop a novel “model-oriented” approach and suggest how that approach may support the development of a new generation of browsers for research reports from the Public Library of Science (PLoS). With full-text we can implement more targeted linking than is possible with monolithic reports. For instance, data sets can be linked directly to the workflows which describe how they were generated and analyzed. Traditional citations can be enhanced by anchoring them to specific points in a cited text. If a reader follows a citation, the model-oriented structure can be used to generate a summary of the target document related to the topic of the citation. In addition, we propose text pre-processing and file standards to facilitate ingest and use of full-text articles

Interactive causal schematics for qualitative scientific explanations

Digital Libraries: Implementing Strategies and Sharing Experiences: 8th International Conference on Asian Digital Libraries, ICADL 2005, Bangkok, Thailand, December 12-15, 2005. LNCS 3815/2005 (http://dx.doi.org/10.1007/11599517_50). Retrieved 7/27/06 from http://www.ischool.drexel.edu/faculty/ballen/PAPERS/causality.pdf.We present a simple model for describing causal processes. We apply it to generate schematics of complex scientific processes. Our interface allows users to select among causal threads and then to follow the state transitions of those explanations. Moreover, these schematics can provide a framework for interacting with texts