Download to read offline
Uploaded byMathieu d'Aquin
Data and Knowledge as Commodities
This document summarizes Mathieu d'Aquin's career path and research interests. It notes that he has worked at LORIA in Nancy, France from 2002-2006, at the Knowledge Media Institute at the Open University in Milton Keynes, UK from 2006-2017, and at the Data Science Institute at NUI Galway in Ireland from 2017-2021. His research has focused on using knowledge-driven and hybrid data-driven/knowledge-driven approaches to understand data provenance, content, and results from data analysis in order to achieve intelligent data understanding.
More Related Content
Similar to Data and Knowledge as Commodities
Data and Knowledge as Commodities
- 1. Data and knowledgeas commodities Mathieu d’Aquin @mdaquin
- 2. Nancy (2002-2006) LORIA UHP Nancy1 Milton Keynes (2006-2017) Knowledge Media Institute The Open University Galway (2017-2021) Data Science Institute NUI Galway
- 3. Nancy (2002-2006) LORIA UHP Nancy1 Milton Keynes (2006-2017) Knowledge Media Institute The Open University Galway (2017-2021) Data Science Institute NUI Galway
- 4. Nancy (2002-2006) LORIA UHP Nancy1 Milton Keynes (2006-2017) Knowledge Media Institute The Open University Galway (2017-2021) Data Science Institute NUI Galway
- 5. Nancy (2002-2006) LORIA UHP Nancy1 Milton Keynes (2006-2017) Knowledge Media Institute The Open University Galway (2017-2021) Data Science Institute NUI Galway
- 8. Analytical Statements Synthetic Statements Allhumans are mortals Everyday, Bob wakes up at 6am
- 9.
- 10. Analytical Statements Synthetic Statements Ontologies/ Knowledge-based Systems Machine Learning / Data Mining
- 11.
- 12.
- 13.
- 14.
- 15.
- 16. Starting with accessissues The Semantic Web and Linked Data d'Aquin, Mathieu, and Enrico Motta. "The epistemology of intelligent semantic web systems." Synthesis Lectures on the Semantic Web: Theory and Technology 6, no. 1 (2016): 1-88.
- 17.
- 18.
- 19. d'Aquin, Mathieu, and EnricoMotta. "The epistemology of intelligent semantic web systems." Synthesis Lectures on the Semantic Web: Theory and Technology 6, no. 1 (2016): 1-88.
- 20. Example: data.open.ac.uk Daga, Enrico,Mathieu d’Aquin, Alessandro Adamou, and Stuart Brown. "The open university linked data–data. open. ac. uk." Semantic Web 7, no. 2 (2016): 183-191.
- 21.
- 22.
- 23. d'Aquin et al.(2013) Assessing the Educational Linked Data Landscape, Web Science 2013
- 24. Learner Platform Analytics VLE | Website| Library Assessment | Enrollment School/University Prediction Drop out BI Planning Recommendation Sentiment Analysis Collective Intelligence Behaviour Analysis Collaboration Community Support d'Aquin, Mathieu, Dominik Kowald, Angela Fessl, Elisabeth Lex, and Stefan Thalmann. "Afel-analytics for everyday learning." In Companion Proceedings of the The Web Conference 2018, pp. 439-440. 2018.
- 26. Data Infrastructure forthe city of Milton Keynes, enabling sharing and consuming varied and diverse city scale data.
- 28. d'Aquin, Mathieu, JohnDavies, and Enrico Motta. "Smart cities' data: Challenges and opportunities for semantic technologies." IEEE Internet Computing 19, no. 6 (2015): 66-70.
- 29. Watson - ASemantic Web Search Engine d'Aquin, Mathieu, and Enrico Motta. "Watson, more than a semantic web search engine." Semantic Web 2, no. 1 (2011): 55-63.
- 31.
- 32.
- 33. What’s an explanationanyway? Tiddi, Ilaria, Mathieu d'Aquin, and Enrico Motta. "An ontology design pattern to define explanations." In Proceedings of the 8th International Conference on Knowledge Capture, pp. 1-8. 2015.
- 34. Explaining patterns emergingfrom data Tiddi, Ilaria, Mathieu d’Aquin, and Enrico Motta. "Data patterns explained with linked data." In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 271-275. Springer, Cham, 2015.
- 35. Explaining patterns emergingfrom data :India :Ethiopia :Somalia sA sA sA subj subj subj related related gdp gdp ≤ ≤ ≤ gdp 4,000/pp cat:LeastDeveloped Countries 600/pp 3,800/pp 1,200/pp cat:Africa cat:SouthAsia db:Somalia db:Ethiopia db:India
- 36. Explaining patterns emergingfrom data Countries where men are more educated than women skos:exactMatch🡺dbp:hdiRank ≥ 126 87.8% 197” skos:exactMatch🡺dc:subject db:Category:Least_Developed_Countries 74.7% 524’’ skos:exactMatch🡺dbp:gdpPPPPerCapitaRank ≥ 89 68.3% 269” Countries where women are more educated than men skos:exactMatch🡺dbp:hdiRank ≤ 119 63.4% 198” skos:exactMatch🡺dbp:gdpPPPPerCapitaRank ≤ 56 62.3% 236’’ Countries where education is equal skos:exactMatch🡺dbp:gdpPPPRank ≥ 64 62.0% 234” skos:exactMatch🡺dbp:gdpPPPPerCapitaRank ≥ 29 61.0% 268’’
- 37. Explaining patterns emergingfrom data Tiddi, Ilaria, Mathieu d’Aquin, and Enrico Motta. "Learning to assess linked data relationships using genetic programming." In International Semantic Web Conference, pp. 581-597. Springer, Cham, 2016.
- 38. Towards model interpretability Givenhis biography: Tiziano Vecelli(+0.04)(1488/1490 – 27 August 1576), known in English as Titian, was an Italian(-0.03) painter, the most important member of the 16th-century Venetian school(+0.01). He was born in Pieve di Cadore(-0.03), near Belluno(-0.01), in Veneto(-0.01)(Republicof Venice(-0.01)). His painting methods would profoundly influence future generations of Western Art(+0.001). Would Titian’s art be on display in some major European art museum? The model says no. Nikolov, Andriy, and Mathieu d'Aquin. "Uncovering Semantic Bias in Neural Network Models Using a Knowledge Graph." In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1175-1184. 2020.
- 39.
- 40. Examples of results Paintingsfrom painters from southern Netherlands that are members of the AntwerpGuild are more likely to be classified positively by the model. Also, painters who have works mentioned in their biography that represent the Virgin Marie are more likely to be classified positively by the model. Painters from certain places in Italy are more likely to be classified negatively by the model.
- 41.
- 42. What to dowith it As a way to debug the model: Rules that a domain expert would consider invalid or counter-intuitive might represent some bias in the data or the use of irrelevant information coincidently related to the classification. As a way to extract more from the model: Rules that appear surprising or counter intuitive to the domain expert might also represent unknown valid connections in the data. As a way to assess generalisability: Rules that refer to characteristics that are unrelated to the domain could lead to the misclassification of entities described similarly in another context. For example, some footballers are classified by our model as being likely to be exposed in European museum.
- 43. Where does thatget us? Analytical Statements Synthetic Statements Grounded by Interpreted by
- 44. Where does thatget us? Analytical Statements Synthetic Statements Grounded by Interpreted by Intelligent Data Understanding
- 45. Where does thatget us? Analytical Statements Synthetic Statements Grounded by Interpreted by Intelligent Data Understanding Using knowledge-driven approaches to understand provenance and access to data.
- 46. Where does thatget us? Analytical Statements Synthetic Statements Grounded by Interpreted by Intelligent Data Understanding Using knowledge-driven approaches to understand provenance and access to data. Using hybrid (data- and knowledge-) driven approaches to understand the content of data and what it means.
- 47. Where does thatget us? Analytical Statements Synthetic Statements Grounded by Interpreted by Intelligent Data Understanding Using knowledge-driven approaches to understand provenance and access to data. Using hybrid (data- and knowledge-) driven approaches to understand the content of data and what it means. Using hybrid (data- and knowledge-) driven approaches to understand the results of analysing data.
- 48. Thanks! Enrico Motta, StefanDietze, Marta Sabou, Jean Lieber, Ilaria Tiddi, Alessandro Adamou, Amedeo Napoli, Enrico Daga, Carlo Allocca, Eelco Herder, Andriy Nikolov, Fouad Zablith, Hendrik Drachsler, Fadi Badra, Aldo Gangemi, Davide Taibi, Ning Li, Laurian Gridinoc, Shuangyan Liu, Keerthi Thomas, Silvio Peroni, Sandrine Lafrogne, Dragan Gasevic, Laszlo Szathmary, Vanessa López, Emanuele Bastianelli, Ratnesh Sahay, Sofia Angeletou, Sabrina Kirrane, Pinelopi Troullinou, Grigoris Antoniou, Marieke Guy, Jorge Gracia, Thomas Meilender, Heiner Stuckenschmidt, Helen Barlow, Holger Lewen, Joachim Kimmerle, Daniela Oliveira, Stefan Thalmann, Steffen Staab, Simon Brown, Peter Holtz, Nicolas Jay, Christopher Brewster, Stefan Decker, Alokkumar Jha, Anne Schlicht, Dimitris Plexousakis, Jeff Heflin, Jeff Z. Pan, Dominik Kowald, Óscar Corcho, Yasar Khan, Valentina Presutti, Wassim Derguech, José Manuél Gómez-Pérez, Eduardo Mena, Stuart Brown, Syeda Sana e Zainab, Kavitha Srinivas, Sébastien Brachais, Krishnaprasad Thirunarayan, Elisabeth Lex, Ran Yu, Paul Mulholland, Paul Groth, Christoph Lange, Michel Dumontier, Angela Fessl, Giorgos Flouris, Markus Strohmaier, Martin Dzbor, Mari Carmen Suárez-Figueroa
- 49.