=> talks, keynotes & media
=> teaching
=> supervision


/ INVITED TALKS, KEYNOTES & INTERVIEWS


/ TEACHING

  • “Data and Knowledge Engineering” (lecture, SS 2019, SS2020, SS2021, 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)


/ SUPERVISION

I am 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.

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