=> talks, keynotes & media
=> teaching
=> supervision
/ INVITED TALKS, KEYNOTES & INTERVIEWS
Keynote “
Panelist in panel„Re-Kalibrierung – Wissenschaftskommunikation im digitalen Wandel”, 15 July 2022, Württembergische Landesbibliothek Stuttgart / Live Stream.
Interview (22.9.2021) for HeiCAD Podcast series on “How to mine 10 bn tweets for science?” (German only)
Invited research colloquium on “Knowledge graphs for research data in the social sciences” at AIFB – Institute of Applied Informatics and Formal Description Methods, 22 February 2020.
Panelist on “Zukunft der Digitalisierungsforschung” at CAIS – Center for Advanced Internet Studies, Düsseldorf, 11 Februrary, 2020.
Invited research seminar on “Research knowledge graphs” at College of Computer Science and Engineering, Chongqing University of Technology (China), 1 November 2019.
Invited research colloquium on “Knowledge graphs for research data in the social sciences” at AIFB – Institute of Applied Informatics and Formal Description Methods, 22 February 2020.
Panelist on “Zukunft der Digitalisierungsforschung” at CAIS – Center for Advanced Internet Studies, Düsseldorf, 11 Februrary, 2020.
Invited research seminar on “Research knowledge graphs” at College of Computer Science and Engineering, Chongqing University of Technology (China), 1 November 2019.
Keynote at LWDA2019 on “Beyond research data infrastructures: exploiting artificial & crowd intelligence towards building research knowledge graphs“, Humboldt University Berlin, 02 October, 2019.
[ slides ] [ abstract ]
Inaugural lecture at Heinrich-Heine-University Düsseldorf on “From (Web) Data to Knowledge: on the Complementarity of Human and Artificial Intelligence“, Düsseldorf, 28 May, 2019.
[ slides ]
Invited speaker and panelist on “Using AI to understand everyday learning on the Web” at Digital Enlightenment Forum, Brussels, Belgium, 8 November 2018
[ slides ]
Interview for German newspaper “Hannoversche Allgemeine Zeitung/HAZ” on “Leichter Lernen mit Künstlicher Intelligenz”, i.e. artificial intelligence-based approaches towards understanding learning during Web search (August 2018).
[ interview ]
Panelist on “Do we need a Google for data search?”, joint panel with Paul Groth, Natasha Noy, Aidan Hogan, Jeni Tenison at PROFILES & DATASEARCH2018, in conjunction with The Web Conference 2018 (WWW2018), Lyon, France, 24 April 2018.
[ opening statement slides ]
Seminar on “Beyond Knowledge Graphs and the Semantic Web – Exploiting Entity-Centric Knowledge on the Web” at University of Palermo, Palermo, Italy, 27 September 2017.
[ slides ]
Invited talk on “Analysing User Knowledge, Competence and Learning during Online Activities” at the CNR-ITD (National Research Council – Institute of Educational Technologies), Palermo, 26 September 2017.
[ slides ]
Keynote on Beyond Linked Data – Exploiting Entity-Centric Knowledge on the Web at LDOW2017 – Linked Data on the Web, collocated with 26th International World Wide Web Conference (WWW2017), Perth, Australia, 3 April 2017.
[ slides ]
Invited talk on Big Data in Learning Analytics – Analytics of Everyday Learning, at Learntec 2017, Karlsruhe, 24 January 2017.
[ slides ]
Invited talk on Big Data in Learning-related Sciences at BDVA Summit, Summit of the Big Data Value Association, Valencia, Spain, 1 December 2016.
Keynote on Retrieval, Crawling & Fusion of Entity-centric Data on the Web at 2nd International KEYSTONE conference (IKC2016), Cluj Napoca, Romania, 09 September 2016.
[ slides ]
Invited seminar on Mining & Understanding (Learning) Resources on the Web at Leibniz Insitut für Wissensmedien, Tübingen, Germany, 14 July 2016.
[ slides ]
Invited talk on Semantic Data Integration in Virtual Research Environments at Herder Institute, Marburg, Germany, 18 March 2015.
[ slides ]
Conference keynote “Turning data into knowledge” at 3rd International Conference on Knowledge Engineering and Semantic Web (KESW2014), Kazan, Russia, 30 September 2014.
[ slides ]
Invited talk “From data to knowledge – profiling and interlinking datasets on the Web” at KEYSTONE Working Groups meeting, Hersonissos, Crete, Greece, 25 May 2014.
Invited talk “What’s all the data about – profiling and linking of Web datasets” at LIRRM, Montpellier, France 27 March 2014.
Talk about LinkedUp at Linked Data Europe Workshop at European Data Forum, Athens, Greece, March 2014
Invited talk on “Linked Data for Learning and Learning Analytics” at LearnTec 2014, 04 February 2014, Karlsruhe Germany.
Talk on “Curation and Profiling of Linked Data” at the KNOWESCAPE Workshop on Data Curation & Mining as part of the Open Knowledge Conference 2013, 19 September 2013, Geneva Switzerland.
[ slides ]
Talk on “Linked Data and Education” as part of the 2nd International Open Data Dialog (#odd13), 19 November 2013, Berlin Germany.
[ slides ]
Invited talk on “Linked Data for federation OER data and repositories” as part of a Open Federations 2013: Open Knowledge Sharing for Education (ARIADNE/GLOBE Convening 2013), 8 April 2013, Leuven, Belgium.
[ slides ]
Online lecture on “Linked Data as a new environment for Learning Analytics and education” as part of a MOOC on Learning Analytics, 12 March 2013.
[ slides ]
Invited talk on “Educational Linked Data – Datasets and APIs” at Green Hackathon, 14-16 December 2012, Athens, Greece (see also the Green Hackathon 2012 Brochure)
Keynote at 6th tele-TASK Symposium 2012, 8-9 October 2012, Potsdam, Germany
[ slides ] [ podcast ]
Keynote on “Linked Data for Recommender Systems in Technology-enhanced Learning” at 2nd Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL 2012), collocated with the 7th European Conference on Technology Enhanced Learning (EC-TEL 2012), 18-19 September 2012, Saarbrücken, Germany
[ slides ]
Invited panelist at World Wide Web Conference 2012 – WWW2012, Panel theme: “The Web and Education”, Lyon, France, 19 April 2012.
Invited lecture on “Semantic Web Information Integration” at University of Leeds, UK, 15 May, 2012.
[ slides ]
“Data and Knowledge Engineering” (lecture, SS 2019, SS2020, SS2021, WS2022/2023, Heinrich-Heine-University Düsseldorf), schedule and details can be found on the course Website.
Understanding and interpreting heterogeneous data, in particular in distributed settings suchas the Web, remains a challenging task. State-of-the-art Web applications such as Web search engines rely on a combination of approaches for making sense of data, involving both explicit knowledge, for instance, through knowledge graphs such as Wikidata or the Google knowledge graph and semi-structured Web markup, as well as statistical and machine-learning based approaches.
This course provides an introduction to data and knowledge engineering methods and principles, with a particular focus on the Web. This includes methods related to knowledge graphs and formal data & knowledge representation (RDF, OWL, Description Logics), data integration and linking, information extraction, Web data sharing practices (Linked Data, Semantic Web and affiliated W3C standards such as RDF, RDFa, Microdata), as well as emerging approaches in the context of distributional semantics, such as word and entity embeddings. Attention will also be paid to applications of taught techniques to facilitate data sharing and reuse on the Web.
“Advances in Data Science” (seminar, WS2018/2019, WS2019/2020, WS2020/2021 Heinrich-Heine-University Düsseldorf), schedule and details can be found via the course website.
Learning from data in order to gain useful insights is an important task, generally covered under the data science umbrella. This involves a wide variety of fields such as statistics, artificial intelligence, effective visualization, as well as efficient (big) data engineering, processing and storage, where efficiency and scalability often play crucial roles in order to cater for the quantity and heterogeneity of data. The goal of this seminar is to deepen the understanding about data science & engineering techniques through studying and critically evaluating state-of-the-art literature in the field. Participants will be introduced to the critical assessment and discussion of recent scientific developments, thereby learning about emerging technologies as well as gaining the ability to evaluate and discuss focused scientific works. Participants will be given recent literature covering relevant data science areas. Each participant will review independently 1-2 publications and present and discuss its content and contribution, which are then presented and discussed with the entire student participants. After successful completion, students will have a deepened understanding of state-of-the-art methods and applications in the data science field. Participants will have gained experience in critically assessing and summarising contemporary research publications.
“Introduction to Data Science” (lecture, WS2017/2018, Leibniz University Hannover), schedule and details can be found via the course website.
Learning from data in order to obtain useful insights or predictions is an important task, generally covered under the data science umbrella. This involves skills and knowledge from a wide variety of fields such as statistics, artificial intelligence, effective visualization, as well as efficient (big) data engineering, processing and storage. While data arises from real-world phenomena, for instance, on the Web, data science investigates how to analyse the data to understand such phenomena. The course teaches critical concepts and practical skills in computer programming and statistical inference, in conjunction with hands-on analysis of datasets, involving issues such as data cleansing; sampling; data management for accessing big data efficiently; exploratory data analysis to generate and test hypotheses; prediction based on statistical methods such as regression and classification; and communication of results through visualization.
“KESW – Knowledge Engineering & the Semantic Web” (lecture, SS2017, Leibniz University Hannover), schedule & details via the course website.
Abstract: This course provides an introduction to fundamental knowledge engineering principles as well as practical knowledge and insights into the use and application of state-of-the-art semantic technologies. Semantic (Web) technologies, based on established W3C standards such as RDF/OWL, Linked Data technologies and entity-centric markup (through RDFa and Microformats) enable the application of formal knowledge engineering principles on the Web and have emerged as defacto standards for sharing data or for annotating unstructured Web documents. The wider goal and purpose is to improve understanding and interpretation of Web documents and data, for instance, to facilitate Web search or data reuse. This course introduces key concepts of knowledge engineering and representation, their application specifically in the context of the (Semantic) Web and their contributions to tasks such as knowledge extraction or knowledge discovery.
“Foundations of Information Retrieval” (lecture, WS2016/2017, Leibniz University Hannover, co-lecturer):
Abstract: The lecture gives an introduction to Web Information Retrieval with particular emphasis on the algorithms and technologies used in the modern search engines. The module covers an introduction to the traditional text IR, including Boolean retrieval, vector space model as well as tolerant retrieval. Afterwards, the technical basics of Web IR are discussed, starting with the Web size estimation and duplicate detection followed by link analysis and crawling. This is followed by the introduction of IR evaluation methods and benchmarks. Finally, applications of classification and clustering in the IR domain are discussed. The theoretical basis is illustrated through examples of contemporary search systems, such as Google.
“KESW – Knowledge Engineering & the Semantic Web” (lecture, SS2016, Leibniz University Hannover), schedule & details via the course website.
Abstract: This course provides an introduction to fundamental knowledge engineering principles as well as practical knowledge and insights into the use and application of state-of-the-art semantic technologies. Semantic (Web) technologies, based on established W3C standards such as RDF/OWL, Linked Data technologies and entity-centric markup (through RDFa and Microformats) enable the application of formal knowledge engineering principles on the Web and have emerged as defacto standards for sharing data or for annotating unstructured Web documents. The wider goal and purpose is to improve understanding and interpretation of Web documents and data, for instance, to facilitate Web search or data reuse. This course introduces key concepts of knowledge engineering and representation, their application specifically in the context of the (Semantic) Web and their contributions to tasks such as knowledge extraction or knowledge discovery.
“Foundations of Information Retrieval” (lecture, WS2015/2016, Leibniz University Hannover, guest lecturer)
Supervision of student projects in “Labor Web Technologien”” (since 2013, Leibniz University Hannover)
Supervision of PhD & MSc students (since 2006)Tutorials and tutorial series at major conferences, specifically on knowledge discovery and and semantic technologies (details here)
Since 2011, I am and have been constantly supervising a number of PhD students at GESIS and HHU as well as BSc and MSc projects and theses. Open BSc/MSc thesis topics can be found via the Web pages of my HHU Data & Knowledge Engineering group.
/ stefandietze.net 