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Technical University of Munich

Dr. Phillip Schneider (2020-2024)

Curriculum Vitae

Phillip Schneider was a research associate at the chair of Software Engineering for Business Information Systems (sebis) at the Technical University of Munich from 2020 to 2024.

Research Interests

  • Natural Language Processing

  • Dialogue Systems

  • Information Retrieval

  • Exploratory search

  • Knowledge Graphs

 

Latest Talks

  • Google DeepMind & AI Grid Meetup at Google Berlin (2024)
  • Knowledge Graph Lab at King’s College London (2024)
  • BR AI Meets Journalism Workshop at BR Munich(2024)
  • Speech and Audio Processing Laboratory at Kyoto University (2023)
  • AI Research Center at National Institute of Advanced Industrial Science and Technology Tokyo (2023)
  • NLIntWorkshop on Natural Language Interfaces at IJCNLP-AACL Conference (2023)
  • Holtzbrinck Publishing Group at Holtzbrinck Technology Day (2022)
  • Middlesex University Dubai at Google Developers Student Club (2022)

 

 

Publications

1. Schneider, P. (2020). App ecosystem out of balance: An empirical analysis of update interdependence between operating system and application software
(Master’s Thesis, Universit ̈atsbibliothek Johann Christian Senckenberg).

2. Schneider, P., Schopf, T., Vladika, J., Galkin, M., Simperl, E., & Matthes, F. (2022). A Decade of Knowledge Graphs in Natural Language Processing: A
Survey. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International
Joint Conference on Natural Language Processing (AACL-IJCNLP).

3. Schneider, P., Voggenreiter, M., Gulraiz, A., & Matthes, F. (2022). Semantic Similarity-Based Clustering of Findings From Security Testing Tools. In
Proceedings of the 5th International Conference on Natural Language and Speech Processing (ICNLSP).

4. Schneider, P., Afzal, A., Vladika, J., Braun, D., & Matthes, F. (2023). Investigating conversational search behavior for domain exploration. In European
Conference on Information Retrieval (ECIR). *Best Student Short Paper Award*

5. Schneider, P., Rehtanz, N., Jokinen, K., & Matthes, F. (2023). Voice-based conversational agents and knowledge graphs for improving news search in
assisted living. In Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA).

6. Voggenreiter, M., Schneider, P., & Gulraiz, A. (2023). Aggregating Industrial Security Findings with Semantic Similarity-Based Techniques. In Practical
Solutions for Diverse Real-World NLP Applications. Cham: Springer International Publishing.

7. Schneider, P., Rehtanz, N., Jokinen, K., & Matthes, F. (2023). From Data to Dialogue: Leveraging the Structure of Knowledge Graphs for Conversational
Exploratory Search. In Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation (PACLIC).

8. Schneider, P., Klettner, M., Jokinen, K., Simperl, E., & Matthes, F. (2024). Evaluating Large Language Models in Semantic Parsing for Conversational
Question Answering over Knowledge Graphs. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence (ICAART).

9. Schneider, P., Klettner, M., Simperl, E., & Matthes, F. (2024). A Comparative Analysis of Conversational Large Language Models in Knowledge-Based
Text Generation. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL).

10. Schneider, P., Poelman, W., Rovatsos, M., & Matthes, F. (2024). Engineering conversational search systems: A review of applications, architectures,
and functional components. In 6th Workshop on NLP for ConvAI (ACL).

11. Schneider, P., Machner, N., Jokinen, K., & Matthes, F. (2024) Bridging Information Gaps in Dialogues With Grounded Exchanges Using Knowledge
Graphs. In Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL).

12. Dingsøyr, T., Schneider, P., Bergersen, G. R., & Lindsjørn, Y. (2024). Challenges in Understanding the Relationship between Teamwork Quality
and Project Success in Large-Scale Agile Projects. In Proceedings of the 2024 IEEE/ACM 17th International Conference on Cooperative and Human Aspects
of Software Engineering.

13. Schneider, P., Schopf, T., Vladika, J., & Matthes, F. (2024). Enterprise Use Cases Combining Knowledge Graphs and Natural Language Processing.
Informing Possible Future Worlds: Essays in Honour of Ulrich Frank. Logos Verlag Berlin.

14. Vladika, J., Schneider, P., & Matthes, F. (2024, May). HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking. In Proceedings
of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING).

15. Jokinen, K., Schneider, P., & Mori, T. (2024). Towards Harnessing Large Language Models for Comprehension of Conversational Grounding. In Proceedings
of the 14th International Workshop on Spoken Dialogue Systems Technology (IWSDS).

16. Vladika, J., Schneider, P., & Matthes, F. (2024). MedREQAL: Examining Medical Knowledge Recall of Large Language Models via Question Answering. In
Findings of the Association for Computational Linguistics (ACL).

Research Projects

Conversational Graph-Based Navigation Over Semantically Connected Content (COGNOSCO)

In cooperation with Springer Nature, the COGNOSCO project (from Latin cognōscere: to discover/learn/know) aims at investigating conversational search systems that enable explorative navigation through knowledge databases. Through the combination of Natural Language Processing and knowledge graphs, the interactive search process is realized by text-based, information-providing dialogs. 

AI-Based Digital Health Assistant (ALPHA-KI) 

The purpose of the digital health assistant ALPHA is to make the everyday life of chronically ill people healthier, safer and more entertaining. ALPHA comes in form of a smartwatch that is always worn on the body. As a voice-based device, it is controlled through natural language alone. The overarching goal of this research project involves four AI-related focal points. 

 Teaching

TermLevelTitleTypeRole
SS 24MasterSEBA Lab Course (NLP)Lab CourseOrganizer
SS 24MasterNatural Language Processing - Methods and ApplicationsSeminarAdvisor
WS 23/24MasterSEBA Lab CourseLab CourseAdvisor
SS 23MasterSEBA Lab Course (NLP) Lab Course Organizer
SS 23MasterNatural Language Processing - Methods and Applications SeminarAdvisor
WS 22/23MasterSEBA Lab Course Lab Course Advisor
WS 22/23MasterStrategic IT Management & Enterprise Architecture Management LectureAdvisor
SS 22MasterNatural Language Processing - Methods and ApplicationsSeminarAdvisor
SS 22Bachelor/MasterConversational AI WorkshopCertificate CourseOrganizer
SS 22MasterEAM MiniprojekteSeminarOrganizer
WS 21/22MasterSEBA Lab CourseLab CourseAdvisor
WS 21/22MasterStrategic IT Management & Enterprise Architecture ManagementLectureOrganizer
SS 21MasterNatural Language Processing - Methods and ApplicationsSeminarAdvisor
SS 21MasterEAM MiniprojekteSeminarAdvisor
WS 20/21MasterSEBA Lab CourseLab CourseAdvisor

 

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