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Dr Ilaria Tiddi

Profile summary

  • Research Staff
  • Research Associate
  • Faculty of Science, Technology, Engineering & Mathematics
  • Knowledge Media Institute
  • ilaria.tiddi

Research Activity

Externally funded projects

AFEL - Analytics for Everyday Learning

RoleStart dateEnd dateFunding source
Lead01/Jan/201631/Dec/2018EC (European Commission): FP(inc.Horizon2020, H2020, ERC)
The goal of AFEL (Analytics for Everyday Learning) is to develop a multi-disciplinary approach to the analysis and understanding of informal/collective learning as it surfaces implicitly in online social environments. Learning Analytics and Educational Data Mining traditionally relate to the analysis and exploration of data coming from learning environments, especially to understand learners' behaviours in order to improve their learning experience. There is an assumption in these approaches that data is available that explicitly relate to a learning activity. However, studies have for a long time demonstrated that learning activities happen, for a large part, outside of formal educational platforms. This includes informal, collective or incidental learning generally associated, as a side effect, to other (social) environments and activities. Supporting such a broader understanding of learning poses interesting technical and non-technical challenges. The aim of AFEL is to provide the technological grounding for such analyses to be achieved, as well as the cognitive models of learning and collaboration necessary to the understanding of such analyses of informal learning in social environments. This will be achieved through combining the variety of skills available in the consortium to a concrete set of online social environments, dedicated to informal learning or not. More precisely, we will tackle the main challenges of informal learning analytics through 1) developing the tools and techniques necessary to capture information about learning activities from (not necessarily educational) social environments; 2) creating methods for the analysis of such informal learning data, based on combining visual analytics with cognitive models of learning and collaboration; and 3) demonstrating the potential of the approach in improving the understanding of informal learning, and the way it can be better supported.

Publications

Good location, terrible food: detecting feature sentiment in user-generated reviews (2013-12)
Cataldi, Mario; Ballatore, Andrea; Tiddi, Ilaria and Aufaure, Marie-Aude
Social Network Analysis and Mining, 3(4) (pp. 1149-1163)
Learning to Assess Linked Data Relationships Using Genetic Programming (2016-09-23)
Tiddi, Ilaria; d'Aquin, Mathieu and Motta, Enrico
In : 15th International Semantic Web Conference (17-21 Oct 2016, Kobe, Japan) (pp. 581-597)
DKA-robo: dynamically updating time-invalid knowledge bases using robots (2016)
Tiddi, Ilaria; Bastianelli, Emanuele; Daga, Enrico and d'Aquin, Mathieu
In : 20th International Conference on Knowledge Engineering and Knowledge Management (EKAW2016) (19-23 Nov 2016, Bologna, Italy)
Update of time-invalid information in Knowledge Bases through Mobile Agents (2016)
Tiddi, Ilaria; Daga, Enrico; Bastianelli, Emanuele and d'Aquin, Mathieu
In : Integrating Multiple Knowledge Representation and Reasoning Techniques in Robotics (MIRROR-16) (10 Oct 2016, Deajeon, South Korea)
Data Patterns Explained with Linked Data (2015-09)
Tiddi, Ilaria; d'Aquin, Mathieu and Motta, Enrico
In : European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2015) (7-11 Sep 2015, Porto, Portugal) (pp. 271-275)
Using Linked Data traversal to label academic communities (2015)
Tiddi, Ilaria; d'Aquin, Mathieu and Motta, Enrico
In : Semantics, Analytics, Visualisation: Enhancing Scholarly Data Workshop co-located with the 24th International World Wide Web Conference (19 May 2015, Florence, Italy)
An Ontology Design Pattern to Define Explanations (2015)
Tiddi, Ilaria; d'Aquin, Mathieu and Motta, Enrico
In : 8th International Conference on Knowledge Capture (K-CAP 2015) (7-10 Oct 2015, Palisades, NY)
Dedalo: looking for clusters explanations in a labyrinth of Linked Data (2014)
Tiddi, Ilaria; d'Aquin, Mathieu and Motta, Enrico
In : 11th ESWC 2014 (25-29 May 2014, Crete, Greece) (pp. 333-348)
Walking Linked Data: a graph traversal approach to explain clusters (2014)
Tiddi, Ilaria; d'Aquin, Mathieu and Motta, Enrico
In : 5th International Workshop on Consuming Linked Data (COLD 2014) (20 Oct 2014, Riva del Garda, Italy)
Quantifying the bias in data links (2014)
Tiddi, Ilaria; d'Aquin, Mathieu and Motta, Enrico
In : 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2014) (24-28 Nov 2014, Linköping, Sweden) (pp. 531-546)
Using neural networks to aggregate Linked Data rules (2014)
Tiddi, Ilaria; d'Aquin, Mathieu and Motta, Enrico
In : 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2014) (24-28 Nov 2014, Linköping, Sweden) (pp. 547-562)
Explaining clusters with inductive logic programming and linked data (2013)
Tiddi, Ilaria; d'Aquin, Mathieu and Motta, Enrico
In : 12th International Semantic Web Conference (21-25 Oct 2013, Sydney, Australia)
Explaining data patterns using background knowledge from Linked Data (2013)
Tiddi, Ilaria
In : ISWC 2013 Doctoral Consortium co-located with 12th International Semantic Web Conference (ISWC 2013) and the 1st Austalasian Semantic Web Conference (20 Oct 2013, Sydney, Australia) (pp. 56-63)
Ontology learning from open linked data and Web snippets (2012)
Tiddi, Ilaria; Mustapha, Nesrine Ben; Vanrompay, Yves and Aufaure, Marie-Aude
In : On the Move to Meaningful Internet Systems: OTM 2012 Workshops (10-14 Sep 2012, Rome) (pp. 434-443)
Explaining Data Patterns using Knowledge from the Web of Data (2016-11-28)
Tiddi, Ilaria
PhD thesis The Open University

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