Ph.D. Symposium

The ESWC 2020 Ph.D. Symposium is a forum for Ph.D. students working in all areas of Semantic Web research to present their work, meet with peers and experienced researchers, receive feedback, and learn from each other’s experiences. It aims at helping Ph.D. students in developing the skills and confidence required to conduct and promote their research, as well as providing them with an opportunity to attend one of the most important research conferences on the Semantic Web.

The ESWC Ph.D. Symposium will give students the opportunity to:

  1. Learn by constructive criticism: Established researchers and Ph.D. student advisors will provide constructive feedback to the submitted papers by means of an open and non-adversarial review process.
  2. Learn from a mentor: Each student of an accepted paper will be assigned to a mentor- a selected member of the programme committee. Students will interact with their mentors on both the revision of their papers and the preparation of their presentations.
  3. Learn about research: Doing good research goes beyond writing a good paper; it includes perspectives on research as an endeavour and a career. Besides the presentations, coffee breaks and the Ph.D. mentoring lunch will be used to exchange ideas and ask questions about all aspects of pursuing a Ph.D. and a research career in general.
  4. Learn by presenting: Accepted contributions will be presented at the Ph.D. Symposium. All accepted contributions will also be included at the general poster session of ESWC. Students’ posters will be presented alongside posters and demonstrations of the main conference.

Additional information can be found in the Call for Ph.D. Symposium.

Ph.D. Symposium – June 2, 2020

Schedule Paper PDF File Presentation
10:00 – 11:00 PhD Symposium Opening
10:00 – 10:15

Opening and Introductions

10:15 – 11:00

Keynote Stefan Schlobach & Discussion

11:00 – 11:30 Break
11:30 – 13:15 Session 1: Knowledge Graph Management
11:30 – 12:05

Towards Transforming Tabular Datasets into Knowledge Graphs

Nora Abdelmageed

paper
12:05 – 12:40

Domain-Specific Knowledge Graph Construction for Semantic Analysis

Nitisha Jain

paper
12:40 – 13:15

Enabling Web-scale Knowledge Graphs Querying

Amr Azzam

paper
13:15 – 14:00 Lunch
14:00 – 15:45 Session 2: Machine Learning and the Semantic Web
14:00 – 14:35

Explicable Question Answering

Endri Kacupaj

paper
14:35 – 15:10

Evolving meaning for supervised learning in complex biomedical domains using knowledge graphs

Rita Sousa

paper
15:10 – 15:45

Semantic Parsing of Textual Requirements

Ole Magnus Holter

paper
15:45 – 16:15 Break
16:15 – 16:50 Session 3: Ontologies
16:15 – 16:50

Towards Automatic Matching of Domain-Specific Schemas Utilizing General Purpose External Background Knowledge

Jan Philipp Portisch

paper
16:50 – 17:00 Closing

Ph.D. Symposium Chairs

Maribel Acosta, Karlsruhe Institute of Technology
e-mail: maribel.acosta@kit.edu

Axel Polleres, Vienna University of Economics and Business
e-mail: axel.polleres@wu.ac.at

Keynote Speaker

Stefan Schlobach, Vrije Universiteit Amsterdam

Program Committee

Oscar Corcho, Universidad Politécnica de Madrid
Elena Demidova, L3S Research Center
Irini Fundulaki, ICS-FORTH
Paul Groth, University of Amsterdam
Andreas Harth, University of Erlangen-Nuremberg and Fraunhofer IIS-SCS
Olaf Hartig, Linköping University
Markus Krötzsch, TU Dresden
Axel-Cyrille Ngonga Ngomo, Paderborn University
Heiko Paulheim, University of Mannheim
Achim Rettinger, Trier University
Rudi Studer, Karlsruhe Institute of Technology
Maria-Esther Vidal, Universidad Simon Bolivar and Technische Informationsbibliothek (TIB)
Josiane Xavier Parreira, Siemens AG Österreich

Mentors

Elena Demidova, L3S Research Center
Andreas Harth, University of Erlangen-Nuremberg and Fraunhofer IIS-SCS
Olaf Hartig, Linköping University
Axel-Cyrille Ngonga Ngomo, Paderborn University
Heiko Paulheim, University of Mannheim
Rudi Studer, Karlsruhe Institute of Technology
Maria-Esther Vidal, Universidad Simon Bolivar and Technische Informationsbibliothek (TIB)

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