Description
Leader of the group Information Access: Jacco van Ossenbruggen.
Our research group develops methods and techniques to better support users in accessing information that is heterogeneous, subjective and potentially inconsistent. We focus on the information’s context, using knowledge graphs to model differences in time and other relevant perspectives.
We work together with social scientists and humanities researchers to evaluate how technology can be used to best interpret complex data, by modelling and comparing different assumptions underlying the interpretation. We advocate the notion of transparency in all critical use of technology to improve trust assessments.
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News
CWI research leads to better understanding of AI classification errors
The future of artificial intelligence (AI) looks bright, but AI is not infallible. In her PhD thesis, CWI researcher Emma Beauxis-Aussalet provides methods to make classification errors more transparent and understandable for end-users with limited expertise in AI.
Readers can increase retrievability of documents in the National Library collection
Crowdsourcing can help decrease the bias of the search engine of the ‘Koninklijke Bibliotheek’, the National Library of the Netherlands. CWI researchers will present that finding at the Joint Conference on Digital Libraries in Texas, which takes place on 4 June 2018.
Female members of European Parliament more likely to be featured on Wikipedia
Researchers from CWI have found that female members of the European Parliament are slightly more likely to be featured on multiple Wikipedia language editions than their male counterparts. However, the variations between language editions are large: while 10 editions over-represent women, 13 editions over-represent men.
ADS & Insight PhD position at CWI
Amsterdam Data Science, in which CWI participates as a partner, recently announced that 3 PhD projects will shortly commence, fully funded by Science Foundation Ireland (an award of around 0.5M€).
Members
Associated Members
Publications
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Wielemaker, J, Riguzzi, F, Kowalski, R.A, Lager, T, Sadri, F, & Calejo, M. (2019). Using SWISH to realize interactive web-based tutorials for logic-based languages. Theory and Practice of Logic Programming, 19(2), 229–261. doi:10.1017/S1471068418000522
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Koolen, M, van Gorp, J, & van Ossenbruggen, J.R. (2018). Toward a model for digital tool criticism: Reflection as integrative practice. Digital Scholarship in the Humanities. doi:10.1093/llc/fqy048
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Ceolin, D, van Son, C, Aroyo, L, Noordegraaf, J, Sener, O, Sharma, R, … Bod, R. (2018). InfoQ: Computational Assessment of Information Quality on the Web. In Digital Humanities, DHBenelux.
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Traub, M.C, van Ossenbruggen, J.R, Samar, T, & Hardman, L. (2018). Impact of Crowdsourcing OCR Improvements on Retrievability Bias. In ACM International Conference Proceeding Series. doi:10.1145/3197026.3197046
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Yin, Y, Zuiderwijk, A, van Ossenbruggen, J.R, G. Jeffery, K, Luthfi, A, & Janssen, M. (2018). How to improve policy making using open data in Virtual Research Environments? An interactive workshop discussing privacy, security and trust strategies. In ACM International Conference Proceeding Series. doi:10.1145/3209281.3209307
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Ceolin, D, Tkacz, L, Sharma, R, & van Son, C. (2018). Bursting the bubble with a needle from the haystack.
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Xie, X, Liu, Y, de Rijke, M, He, J, Zhang, M, & Ma, S. (2018). Why people search for images using web search engines. In WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining (pp. 655–663). doi:10.1145/3159652.3159686
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Su, N, He, J, Liu, Y, Zhang, M, & Ma, S. (2018). User intent, behaviour, and perceived satisfaction in product search. In Proceedings of the International Conference on Web Search and Data Mining (pp. 547–555). doi:10.1145/3159652.3159714
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Dijkshoorn, C, Jongma, L, Aroyo, L, van Ossenbruggen, J.R, Schreiber, G, ter Weele, W, & Wielemaker, J. (2018). The Rijksmuseum collection as linked data. Semantic Web – Interoperability, Usability, Applicability, 9(2), 221–230. doi:10.3233/SW-170257
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Beauxis-Aussalet, E.M.A.L, & Hardman, L. (2018). Extended methods to handle classification biases.
Software
Amalgame: Managing alignments in large vocabularies
Amalgame, short for AMsterdam ALignment GenerAtion MEtatool, is a Semantic Web tool for finding, evaluating and managing vocabulary alignments, i.e. relating corresponding concepts in different vocabularies.
Current projects with external funding
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Automated Knowledge Graph Creation from Scientific Literature
Related partners
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Elsevier