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Our work aims to make the arguments underlying a scientific controversy more clear and more understandable. A long-term goal of our research is to use argumentation theory to help improve science communication, and particularly to help reduce information disorders such as misinformation, disinformation, and malinformation (Wardle, 2018). We conduct a case study about one public controversy: whether masks can interrupt or reduce the spread of COVID-19. We are mapping this controversy using an argumentation theory called polylogue analysis (Lewiński and Aakhus, 2022). The polylogue diagrams resulting from our case study could be used in the future to determine whether argumentation theory can help improve the quality of communication about controversies in science. In the future, the landscape of a controversy could be used to determine the alignment of players and positions (for instance to highlight conflicts of interest); to help stimulate people’s critical thinking and analytic skills; and to elucidate the subtle positions in controversies., Funding: United States Institute of Museum and Library Services RE-250162-OLS-21, {"references":["Altmetric.com page for Physical interventions to interrupt or reduce the spread of respiratory viruses: Overview of attention for article published in Cochrane database of systematic reviews, January 2023. (n.d.). Altmetric.com. Retrieved March 30, 2023, from https://cochrane.altmetric.com/details/141934282","Eysenbach, G. (2020). How to fight an infodemic: The four pillars of infodemic management. Journal of Medical Internet Research, 22(6), e21820. https://doi.org/10.2196/21820","Jefferson, T., Dooley, L., Ferroni, E., Al-Ansary, L. A., Driel, M. L. van, Bawazeer, G. A., Jones, M. A., Hoffmann, T. C., Clark, J., Beller, E. M., Glasziou, P. P., & Conly, J. M. (2023). Physical interventions to interrupt or reduce the spread of respiratory viruses. Cochrane Database of Systematic Reviews, 1. https://doi.org/10.1002/14651858.CD006207.pub6","Lewiński, M., & Aakhus, M. (2022). Argumentation in Complex Communication: Managing Disagreement in a Polylogue. Cambridge University Press.","Soares-Weiser, K. (2023, March 10). Statement on \"Physical interventions to interrupt or reduce the spread of respiratory viruses\" review. Cochrane. https://www.cochrane.org/news/statement-physical-interventions-interrupt-or-reduce-spread-respiratory-viruses-review","van Eemeren, F. H., Garssen, B., Krabbe, E. C. W., Henkemans, A. F. S., Verheij, B., & Wagemans, J. H. M. (2014). Argumentation and Artificial Intelligence. In F. H. van Eemeren, B. Garssen, E. C. W. Krabbe, A. F. Snoeck Henkemans, B. Verheij, & J. H. M. Wagemans (Eds.), Handbook of Argumentation Theory (pp. 615–675). Springer Netherlands. https://doi.org/10.1007/978-90-481-9473-5_11","Wardle, C. (2018). The need for smarter definitions and practical, timely empirical research on information disorder. Digital Journalism, 6(8), 951–963. https://doi.org/10.1080/21670811.2018.1502047"]}

Humans constantly create narratives to provide explanations for how and why something happens. Designing systems able to build such narratives would therefore contribute to building more human-centric systems, and to support uses like decision-making processes. Here, a narrative is seen as a sequence of events. My thesis investigates how a narrative can be built computationally. Four research questions are identified: representation, construction, link prediction and evaluation. A case study on the French Revolution, based upon Wikidata and Wikipedia is presented. This prototype helps identifying the first challenges such as dynamic representation and evaluation of a narrative.

Ontology Alignment (OA) is generally performed by requesting two parties to provide their complete knowledge to a third party that suggests potential schema alignments. This might however not always be possible or helpful, as for example, when two organisations want to query each other’s knowledge, and none of them is willing to share their schema due to information privacy considerations. This Ph.D. explores how to allow multi-agent communication in cases where agents operate using different ontologies that cannot be fully exposed or shared. Our preliminary experiments focus on the case where agents’ knowledge is describing a common set of entities and has the form of Knowledge Graphs (KGs). The suggested methodology is based on the grounded naming game, where agents are forced to develop their own language in order to refer to corresponding schema concepts of different ontologies. This way, agents that use different ontologies can still communicate successfully for a task at hand, without revealing any private information. We have performed some proof of concept experiments applying our suggested method on artificial cases and we are on the process of extending our methodology so that it can be applied in real-world KGs.

Systematic reviews are extremely time-consuming. The goal of this work is to assess work savings and recall for a publication type filtering strategy that uses the output of two machine learning models, Multi-Tagger and web RCT Tagger, applied retrospectively to 10 systematic reviews on drug effectiveness. Our filtering strategy resulted in mean work savings of 33.6% and recall of 98.3%. Of 363 articles finally included in any of the systematic reviews, 7 were filtered out by our strategy, but 1 "error" was actually an article using a publication type that the SR team had not pre-specified as relevant for inclusion. Our analysis suggests that automated publication type filtering can potentially provide substantial work savings with minimal loss of included articles. Publication type filtering should be personalized for each systematic review and might be combined with other filtering or ranking methods to provide additional work savings for manual triage.