The IFLA Subject Analysis and Access Section’s Working Group on Automated Indexing invites you to the following webinar:

Where Do We Meet? Perspectives from Software Developers and Subject Specialists on Creating Machine Learning Projects

When: Wednesday, November 9, 2022, 14:00 – 18:00 CET

Where: online [via Zoom]

Register here for the event.

Programme

  • Supporting Subject Librarians with AI Solutions, Osma Suominen, National Library of Finland
  • A Case Study on Applying Machine Learning Methods in Automated Subject Headings to Dataset Records, Mingfang Wu, Australian Research Data Commons
  • Comparing Methods for Automated Keyword Extraction—Insights and Pitfalls in Setting Up and Evaluation Plan for Method Selection, Maximilian Kähler, National Library of Germany
  • Interdisciplinary Teamwork as a Success Factor: Competencies Needed for Adaptation and Implementation of a Subject Indexing Support Software at the Leibniz Information Center for Economics (ZWB), Claudia Liebetruth, ZBW
  • Autocategorization Projects: a Taxanomist’s Perspective, Bob Kasenchack, Factor
  • Panel Discussion featuring Diane Rasmussen Pennington, University of Strathclyde

About the speakers

Osma Suominen is an information systems specialist at the National Library of Finland. He is currently working on automated subject indexing, in particular the Annif tool and the Finto AI service, as well as the publishing of bibliographic data as Linked Data. He is also one of the creators of the Finto.fi thesaurus and ontology service and is leading the development of the Skosmos vocabulary browser used in Finto. Osma Suominen earned his doctoral degree at Aalto University while doing research on semantic portals and the quality of controlled vocabularies within the FinnONTO series of projects.

Dr. Mingfang Wu is senior research data specialist at the Australian Research Data Commons (ARDC). She has conducted research in the areas of interactive information retrieval, search log analysis, interfaces supporting exploratory search and enterprise search. Her recent research focuses on the data discovery paradigms as part of the Research Data Alliance initiative and for improving data discovery service of an Australian national research data catalogue.

Maximilian Kähler acquired degrees in mathematical sciences from the universities of Göttingen, Durham (UK) and Leipzig. After completing his studies, he specialized as Data Scientist and Research Software Engineer. Prior work has led him to the Federal Institute for Quality Assurance and Transparency in Health Care (IQTIG) in Berlin and the Helmholtz Center for Environmental Science (UFZ) in Leipzig, before joining the German National Library in October 2021. Mr. Kähler is part of the Department for Automatic Indexing and Online Publications. As a scientific employee, he is part of a research project that investigates the possibilities to exploit recent advances in natural language processing and novel machine learning approaches for the task of automated subject indexing.

Claudia Liebetruth is a subject librarian at the ZBW Leibniz Information Centre for Economics, Germany. As a project manager for the “Digital Assistant” she takes part in automatization efforts of the ZBW.  She holds a master’s degree in international management. She started her career as a learning and development specialist.

Bob Kasenchak is an information architect at Factor. A taxonomist and ontologist with an interest in knowledge graphs and Linked Data, he has worked for over a decade building and implementing taxonomy projects for publishing, enterprise, technology, and e-commerce clients. He brings experience with information modeling and semantic software to client-focused metadata and vocabulary projects. Bob holds an MM in Theoretical Studies from the New England Conservatory of Music and a BA in Liberal Arts from St. John’s College, Santa Fe. A frequent writer and presenter on semantic topics at conferences and in journals, Bob’s ongoing research interests include ontologies, knowledge graphs, and automatic text classification.