WSTNet International Seminar Speaker Series Webinar | 18 September 2026
WSTNet International Seminar Speaker Series Webinar
18 September 2026 | 10:00–11:30 am EDT
WSTNet Distinguished Talk
Web Science at 20, is it still needed?
Abstract
Yes.
Prof. James Hendler
Tetherless World Professor of Computing, Web and Cognitive Science at RPI
Speaker bio
James Hendler is the Tetherless World Professor of Computing, Web and Cognitive Science at RPI. Hendler is an expert in artificial intelligence, with special expertise including linked-data and Semantic Web, open scientific and government data sharing, and machine learning (especially transfer learning and foundation models). His work in AI and Machine learning have produced over 450 books and papers in his 40 years of AI experience and has been recognized in his citations as a Fellow of the Association for Artificial Intelligence (AAAI), American Association for Advancement of Science (AAAS), Association for Computing Machinery (ACM), and the US National Academy of Public Administration. Hendler was the winner of the 2025 AAAI Feigenbaum Prize.
WSTNet International Seminar
Measuring the Language Divide in Knowledge Graphs
Abstract
Emerging digital technologies are exacerbating the existing divide in Open Access Data between high- and low-resource languages, excluding many communities from the global digital transformation. Multilingual Linked Open Data Knowledge Graphs (LOD could contribute to mitigating this divide through cross-lingual transfer; however, no clear quantitative definition of low-resource languages has yet been established in the context of linked open data knowledge graphs. In this presentation, I present a methodology to analyze the distribution of languages across linked open data knowledge graphs and propose a preliminary multi-level categorization based on DBpedia, BabelNet, and Wikidata. This categorization is leveraged to bring a formal definition of low-, high-, and medium-resource languages
Ndeye-Emilie Mbengue
National Institute for Research in Digital Science and Technology (INRIA)
Speaker bio
Ndeye-Emilie is as first-year PhD student at Université Côte d’Azur, working on abstraction and analogical reasoning to improve representations for low-resource languages. Previously, she was a research engineer at Université de Lorraine, where she focused for one year on analogical reasoning for knowledge graph maintenance, with particular interest on how selecting relevant analogical pairs during inference could affect the performance of analogy-based classifiers. Her research interests include knowledge graph construction, reasoning, and completion in low-resource settings, as well as the use of analogical reasoning to enhance machine learning models. More broadly, she is interested in how low-resource languages are approached and supported within the NLP literature, especially through cross-lingual transfer.