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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.

Slides from “Misinformation and truth: from fake news to retractions to preprints"Description: Connecting the dots: A cross-industry discussion on retracted researchIssues around the capturing, acknowledgement, classification, and tracking of retracted research are shared by academic institutions, publishing organizations, and the technology providers who support them. This cross-industry panel, moderated by a researcher and comprised of representatives from a non-profit publisher, an academic library, and a publishing platform provider, will examine shared obstacles and opportunities in processing, documenting, and communicating retractions, and will provide practical strategies for cross-industry collaboration. The panel will be moderated by Jodi Schneider, Assistant Professor, School of Information Sciences, University of Illinois at Urbana-Champaign. Jodi and other members of her research team have been spending significant time in 2020 bringing together representatives from all areas of the scholarly communication ecosystem as part of a Sloan-funded agenda-setting project. This moderated conversation will be one deliverable from a series of multiple workshops, interviews, and white papers.

In this research we aim to demonstrate that an ontology-based system can categorize potential drug-drug interaction (PDDI) evidence items into complex types based on a small set of simple questions. Such a method could increase the transparency and reliability of PDDI evidence evaluation, while also reducing the variations in content and seriousness ratings present in PDDI knowledge bases. We extended the DIDEO ontology with 44 formal evidence type definitions. We then manually annotated the evidence types of 30 evidence items. We tested an RDF/OWL representation of answers to a small number of simple questions about each of these 30 evidence items and showed that automatic inference can determine the detailed evidence types based on this small number of simpler questions. These results show proof-of-concept for a decision support infrastructure that frees the evidence evaluator from mastering relatively complex written evidence type definitions.

A huge amount of human time and effort is spent in "keeping up" with the explosion of trials and papers. Policymakers and scientists need to quickly access what is known on a given topic at the present time. In this project, part of the “Addressing Gaps in Clinically Useful Evidence on Drug-Drug Interactions”, an NLM R01 grant, we are exploring new methods for abstracting and indexing deep knowledge about medication safety. Potential drug-drug interactions are a significant source of preventable drug-related harm. Unfortunately, most drug information sources disagree substantially in their content. One contributing factor is that there is no standard way to represent PDDI knowledge claims and associated evidence in a computable form. Our approach is to (1) construct both an evidence base and a knowledge base (2) model knowledge with ontologies; and (3) annotate the scientific literature and other source documents. Annotations stored in the evidence base (as micropublications) can be filtered to generate a knowledge base (published in the nanopublication format). Ontologies and data models that we use include: * Micropublications Ontology http://purl.org/mp * Nanopublications Ontology http://nanopub.org/ * Open Annotation Data Model http://www.openannotation.org/spec/core/ * The Potential Drug-drug Interaction and Potential Drug-drug Interaction Evidence Ontology https://github.com/DIDEO/DIDEO Code and data from the project is available at Related papers have been published at workshops of the International Semantic Web Conference, BDM2I 2015 at ISWC 2015 http://jodischneider.com/pubs/bdm2i.pdf and LISC 2014 at ISWC 2014 http://jodischneider.com/pubs/lisc2014.pdf

This poster describes a proposal for a conference of stakeholders, "Toward a Diagnosis Driven Profession" regarding dental diagnostic terminologies.

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.

This paper presents a case study of long-term post-retraction citation to falsified clinical trial data (Matsuyama et al. in Chest 128(6):3817–3827, 2005. 10.1378/chest.128.6.3817), demonstrating problems with how the current digital library environment communicates retraction status. Eleven years after its retraction, the paper continues to be cited positively and uncritically to support a medical nutrition intervention, without mention of its 2008 retraction for falsifying data. To date no high quality clinical trials reporting on the efficacy of omega-3 fatty acids on reducing inflammatory markers have been published. Our paper uses network analysis, citation context analysis, and retraction status visibility analysis to illustrate the potential for extended propagation of misinformation over a citation network, updating and extending a case study of the first 6 years of post-retraction citation (Fulton et al. in Publications 3(1):7–26, 2015. 10.3390/publications3010017). The current study covers 148 direct citations from 2006 through 2019 and their 2542 second-generation citations and assesses retraction status visibility of the case study paper and its retraction notice on 12 digital platforms as of 2020. The retraction is not mentioned in 96% (107/112) of direct post-retraction citations for which we were able to conduct citation context analysis. Over 41% (44/107) of direct post-retraction citations that do not mention the retraction describe the case study paper in detail, giving a risk of diffusing misinformation from the case paper. We analyze 152 second-generation citations to the most recent 35 direct citations (2010–2019) that do not mention the retraction but do mention methods or results of the case paper, finding 23 possible diffusions of misinformation from these non-direct citations to the case paper. Link resolving errors from databases show a significant challenge in a reader reaching the retraction notice via a database search. Only 1/8 databases (and 1/9 database records) consistently resolved the retraction notice to its full-text correctly in our tests. Although limited to evaluation of a single case (N = 1), this work demonstrates how retracted research can continue to spread and how the current information environment contributes to this problem.

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.

Submitted by LILIANA GIUSTI SERRA (lgiustiserra@gmail.com) on 2019-09-18T00:30:42Z No. of bitstreams: 1 Tese_deposito_SERRA_Liliana_Giusti.pdf: 2550767 bytes, checksum: 4e162528f984be23e85cd0baf6c9b493 (MD5) Rejected by Satie Tagara (satie@marilia.unesp.br), reason: Conforme portaria nº 206 da CAPES, de 2018, os trabalhos financiados pela CAPES deverão, obrigatoriamente, fazer referência (sugerimos que seja nos agradecimentos) ao apoio recebido, usando as seguintes expressões: "O presente trabalho foi realizado com apoio da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Código de Financiamento 001". on 2019-09-18T13:37:54Z (GMT) Submitted by LILIANA GIUSTI SERRA (lgiustiserra@gmail.com) on 2019-09-18T17:03:57Z No. of bitstreams: 1 Tese_deposito_SERRA_Liliana_Giusti_.pdf: 2551211 bytes, checksum: e9122a568551253e4b34029c633837c8 (MD5) Approved for entry into archive by Satie Tagara (satie@marilia.unesp.br) on 2019-09-18T17:42:05Z (GMT) No. of bitstreams: 1 serra_lg_dr_mar.pdf: 2551211 bytes, checksum: e9122a568551253e4b34029c633837c8 (MD5) Made available in DSpace on 2019-09-18T17:42:05Z (GMT). No. of bitstreams: 1 serra_lg_dr_mar.pdf: 2551211 bytes, checksum: e9122a568551253e4b34029c633837c8 (MD5) Previous issue date: 2019-08-26 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) A inclusão de livros digitais em bibliotecas proporciona outras formas de oferta de serviços e acesso à informação aos usuários. Se por um lado amplia a oferta de acervo, por outro demanda ajustes de metadados bibliográficos e de gestão, acompanhamento da movimentação de livros digitais presentes na(s) plataforma(s) dos fornecedores e atualização constante dos dados e recursos disponíveis aos usuários. A Web Semântica, seus conceitos e tecnologias vêm sendo pesquisadas no ambiente de bibliotecas, analisando as possibilidades para sua utilização em rotinas diversas como, por exemplo, o enriquecimento de catálogos e o reaproveitamento de dados que estão disponíveis na Web. Este cenário despertou o interesse em averiguar se a Web Semântica pode ser utilizada para a criação de modelo de dados (framework) para contribuir com o tratamento, enriquecimento, publicação e reuso de registros bibliográficos e de licenciamento entre fornecedores e bibliotecas e condicionou a formulação do problema de pesquisa. O objetivo geral desta tese consiste na elaboração de proposta de modelo de dados com uso da Web Semântica para gestão de livros digitais licenciados. Para construir este modelo foi realizada pesquisa bibliográfica visando identificar as principais dificuldades observadas com a gestão de livros digitais licenciados e os elementos da Web Semântica que permitam o enriquecimento, publicação e reutilização de dados. A partir dos resultados desta revisão de literatura foi realizada pesquisa exploratória para identificar se o Linked Open Data pode ser utilizado para tratamento, enriquecimento e padronização de dados bibliográficos e o Linked Enterprise Data para dados de licenciamento. Foram identificados os metadados que são necessários para descrição de recursos e formas de contratação e realizado mapeamento em formatos diferentes para conversão de dados de fornecedores (planilhas de dados ou registros no formato ONIX) e de bibliotecas (MARC 21) ao modelo RDF. Com o mapeamento dos metadados foram definidas as etapas para a publicação de datasets como elaboração de política, seleção, fornalização, formatos, licenças, conversão ferramental, processo de recuperação, marketing e feedback. Após a elaboração e construção do modelo foi realizado exemplo de validação a partir de tratamento e enriquecimento de um registro bibliográfico. Como resultado, esta tese apresentou a viabilidade da construção de modelo de dados para gestão de livros digitais licenciados, proporcionando enriquecimento e troca de dados entre bibliotecas e fornecedores de conteúdo digital. A gestão proporcionada pelo modelo permite que as bibliotecas tenham controle da coleção digital licenciada, acompanhando sua disponibilidade no catálogo, as formas de licenciamento vigentes, o controle sobre as contratações duplicadas ou redundantes, aferindo o investimento realizado em recursos de informação em quantidade e qualidade adequados para atender à demanda dos usuários. Adding digital books to a library’s collection allows offers of new services and access to the new information for patrons. On one hand, it is possible to enhance the collection but, on the other hand, it is necessary to update bibliographic and management metadata, in accordance to the moving of resources in providers’ platforms. The Semantic Web and its concepts and technologies have been studied in libraries, analyzing the possibilities to use it in several activities such as catalog enrichment and data reuse from sources available on the Web. This scenario sparked interest in whether the Semantic Web can be used to create a framework for the treatment, enrichment, reuse, and publishing bibliographic and licensing records by providers and libraries. This situation results in the formulation of the research problem. The main objective of this dissertation relies on the proposal of a framework using the Semantic Web for digital books management. To construct this framework, bibliographic research was performed focusing on identifying the main difficulties in digital books management and the core elements of the Semantic Web that allow the enrichment, publishing and reusing of data. From the results of this literature review, exploratory research was done to identify if the Linked Open Data can be used for the treatment, enriching, and patterning of bibliographic data, and if the Linked Enterprise Data can be used for licensing data. The needed metadata for resource description and business models were identified, and data mapping in different formats were done for the conversion of providers’ data (data sheets or records in ONIX format) and libraries (MARC 21) to the RDF model. With this metadata mapping, the steps for dataset publishing were defined, such as preparing the politics, selection, formalization, formats, licenses, data conversion, retrieval, marketing, and feedback. After planning and structuring the framework, the model's validation was done from the treatment and enrichment of one bibliographic record. As a result, this dissertation shows the possibility of enriching and exchanging data from libraries and digital content providers. The management provided by the model allows libraries to have control of the licensed digital collection, keeping track of its availability in the catalog, the current forms of licensing, the control over duplicate or redundant hiring, and gauging the investment made in information resources in adequate quantity and quality to meet user demand. CAPES/PDSE: 88881.190379/2018-01