Social bookmarking systems and their emergent information structures, known as folksonomies, are increasingly important data sources for Semantic Web applications. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, and which measures are best suited for applications such as navigation support, semantic search, and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures derived from established information-theoretic, statistical, and practical measures. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity among tags and resources, considering different ways to aggregate annotations across users. After comparing how tag similarity measures predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory. We also investigate the issue of scalability. We find that mutual information with distributional micro-aggregation across users yields the highest accuracy, but is not scalable; per-user projection with collaborative aggregation provides the best scalable approach via incremental computations. The results are consistent across resource and tag similarity.
Social bookmarking systems and their emergent information structures, known as folksonomies, are increasingly important data sources for Semantic Web applications. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, and which measures are best suited for applications such as navigation support, semantic search, and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures derived from established information-theoretic, statistical, and practical measures. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity among tags and resources, considering different ways to aggregate annotations across users. After comparing how tag similarity measures predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory. We also investigate the issue of scalability. We find that mutual information with distributional micro-aggregation across users yields the highest accuracy, but is not scalable; per-user projection with collaborative aggregation provides the best scalable approach via incremental computations. The results are consistent across resource and tag similarity.
J. Funk, and L. Schmidt. DELFI 2020: 18. Fachtagung Bildungstechnologien (Online 2020), page 25-36 *** Best Paper Award ***. Bonn, Gesellschaft für Informatik, (2020)
J. Funk, and L. Schmidt. Arbeit HUMAINE gestalten: 67. Kongress der Gesellschaft für Arbeitswissenschaft (Bochum 2021), page 1-6 (B.2.1). Dortmund, GfA-Press, (2021)
A. Faulhaber, and L. Schmidt. Arbeit HUMAINE gestalten: 67. Kongress der Gesellschaft für Arbeitswissenschaft (Bochum 2021), page 1-6 (B.5.6). Dortmund, GfA-Press, (2021)
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A. Faulhaber, M. Hoppe, and L. Schmidt. 28th IEEE Conference on Virtual Reality and 3D User Interfaces: Abstracts and Workshops (Christchurch 2022), page 586–587. Piscataway, IEEE, (2022)
F. Wehr, L. Beckers, and L. Schmidt. Arbeitswissenschaft in-the-loop - Mensch-Technologie-Integration und ihre Auswirkung auf Mensch, Arbeit und Arbeitsgestaltung: 70. Kongress der Gesellschaft für Arbeitswissenschaft (Stuttgart 2024), page 1-6 (D.2.1). Sankt Augustin, GfA-Press, (2024)
D. Mack, J. Funk, and L. Schmidt. Arbeitswissenschaft in-the-loop - Mensch-Technologie-Integration und ihre Auswirkung auf Mensch, Arbeit und Arbeitsgestaltung: 70. Kongress der Gesellschaft für Arbeitswissenschaft (Stuttgart 2024), page 1-6 (H.4.2). Sankt Augustin, GfA-Press, (2024)
E. Landau, and L. Schmidt. Arbeitswissenschaft in-the-loop - Mensch-Technologie-Integration und ihre Auswirkung auf Mensch, Arbeit und Arbeitsgestaltung: 70. Kongress der Gesellschaft für Arbeitswissenschaft (Stuttgart 2024), page 1-6 (H.3.5). Sankt Augustin, GfA-Press, (2024)
J. Hegenberg, and L. Schmidt. Arbeitswissenschaft in-the-loop - Mensch-Technologie-Integration und ihre Auswirkung auf Mensch, Arbeit und Arbeitsgestaltung: 70. Kongress der Gesellschaft für Arbeitswissenschaft (Stuttgart 2024), page 1-6 (D.6.4). Sankt Augustin, GfA-Press, (2024)
J. Hennrich, E. Ritz, P. Hofmann, and N. Urbach. Capturing artificial intelligence applications’ value proposition in healthcare – a qualitative research study, (2024)
F. Heidecker, T. Susetzky, E. Fuchs, and B. Sick. IEEE International Conference on Intelligent Transportation Systems (ITSC), page 1522--1529. IEEE, (2023)
A. Roßnagel, and P. Richter. General Data Protection Regulation: Article-by-Article Commentary, Nomos / Beck / Hart, Baden-Baden / München / Bloomsbury, (2023)
G. Hornung. Mensch - Technik - Umwelt: Verantwortung für eine sozialverträgliche Zukunft. Festschrift für Alexander Roßnagel zum 70. Geburtstag, Baden-Baden, (2020)
G. Hornung. Mensch - Technik - Umwelt: Verantwortung für eine sozialverträgliche Zukunft. Festschrift für Alexander Roßnagel zum 70. Geburtstag, Baden-Baden, (2020)