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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.

Additional file 1. Online Supplement Description: Further description of RISRS workshops and excerpts from 2 surveys of workshop participants.

The ArgMining 2022 Shared Task is concerned with predicting the validity and novelty of an inference for a given premise and conclusion pair. We propose two feed-forward network based models (KEViN1 and KEViN2), which combine features generated from several pretrained transformers and the WikiData knowledge graph. The transformers are used to predict entailment and semantic similarity, while WikiData is used to provide a semantic measure between concepts in the premise-conclusion pair. Our proposed models show significant improvement over RoBERTa, with KEViN1 outperforming KEViN2 and obtaining second rank on both subtasks (A and B) of the ArgMining 2022 Shared Task.

In scientific papers, arguments are essential for explaining authors’ findings. As substrates of the reasoning process, arguments are often decorated with discourse indicators such as “which shows that” or “suggesting that”. However, it remains understudied whether discourse indicators by themselves can be used as an effective marker of the local argument components (LACs) in the body text that support the main claim in the abstract, i.e., the global argument. In this work, we investigate whether discourse indicators reflect the global premise and conclusion. We construct a set of regular expressions for over 100 word- and phrase-level discourse indicators and measure the alignment of LACs extracted by discourse indicators with the global arguments. We find a positive correlation between the alignment of local premises and local conclusions. However, compared to a simple textual intersection baseline, discourse indicators achieve lower ROUGE recall and have limited capability of extracting LACs relevant to the global argument; thus their role in scientific reasoning is less salient as expected., Proceedings of the 9th Workshop on Argument Mining

This paper outlines the ongoing work of the DigiNUMA project for creating solutions in data harmonisation, analysis, and dissemination of pan-European archaeological and numismatic Cultural Heritage, using linked data and semantic web technologies. The project focuses on Viking Age (800–1150 AD) Finnish and English numismatic data as a case study. A broader context is gained by research into harmonizing collection data of the National Museum of Finland, the British Museum, and the Fitzwilliam Museum in Cambridge for compatibility with the international Nomisma.org ontology, and by creating tools that can be used to work with other Nomisma.org datasets.

This demo paper demonstrates the idea of publishing and watching videos on the Semantic Web. An in-use application, WarMemoirSampo, is presented that enables scene segments in videos to be searched by their semantic content. While watching a video, additional contextual information is provided dynamically. The system is based on a SPARQL endpoint whose knowledge graph has been extracted automatically from timestamped natural language descriptions of the video contents.

Knowledge graphs in RDF are often generated from heterogeneous data sources to power services. However, knowledge graph generation is an unbalanced effort for producers compared to consumers of a knowledge graph. In this paper, I present my research about (i) investigating current RDF knowledge graph production and consumption approaches, and (ii) how to involve the consumer into a hybrid RDF generation approach to reduce the necessary resources for generating RDF for producers & consumers. I discuss the shortcomings of existing approaches for RDF generation from heterogeneous data sources (i.e., materialization and virtualization) and how I will address these: a Systematic Literature Review; an analysis and a set of guidelines for producers to select the right approach for an use case; and a combined hybrid approach to balance the producer’s and consumer’s effort in RDF generation. I already performed a Systematic Literature Review to get an overview of the existing approaches for RDF production from heterogeneous data sources. These results will be used to establish a set of producer guidelines, a benchmark to compare the current materialization and virtualization approaches, and evaluate the proposed hybrid approach. Thanks to my research, knowledge graph production and consumption will be more balanced and accessible to smaller companies and individuals. This way, they can focus on providing better services on top of a knowledge graph instead of being limited by the lack of computing resources to harvest enormous amounts of data from the Web and integrate it into a knowledge graph.