Databases and Information Systems


Sneha Singhania

Sneha Singhania

Max-Planck-Institut für Informatik
D5: Databases and Information Systems
 office: Campus E1 4, Room 424
Saarland Informatics Campus
66123 Saarbrücken
 website: Google Scholar | LinkedIn | GitHub


I'm a PhD student at the Max Planck Institute for Informatics, advised by Dr. Simon Razniewski and Prof. Gerhard Weikum. I'm passionate about research in natural language processing for Information Extraction and Knowledge Bases. I pursued a research internship at Apple (Cupertino, USA).

Before joining MPII, I worked as an AI Researcher and Engineer at Accenture Labs. I graduated with a Bachelor of Technology and a Master of Technology (dual degree) from the International Institute of Information Technology, Bangalore (IIITB) in 2018.


June 2023: Attending 2023 Netflix Workshop on Personalization, Recommendation and Search and presenting a poster
May 2023: Organizing the 1st workshop on Knowledge Base Construction from Pre-Trained Language Models (KBC-LM) @ ISWC 2023
March 2023: Hosting the 2nd edition of LM-KBC Challenge @ ISWC 2023


  1. Recall Them All: Retrieval-Augmented Language Models for Long Object List Extraction from Long Documents
    Sneha Singhania, Simon Razniewski, and Gerhard Weikum
    Under Submission
  2. Large Language Models and Knowledge Graphs: Opportunities and Challenges
    Jeff Z. Pan, Simon Razniewski, Jan-Christoph Kalo, Sneha Singhania, Jiaoyan Chen, Stefan Dietze, Hajira Jabeen, Janna Omeliyanenko, Wen Zhang, Matteo Lissandrini, Russa Biswas, Gerard de Melo, Angela Bonifati, Edlira Vakaj, Mauro Dragoni, Damien Graux
    Transactions on Graph Data and Knowledge (TGDK) 2023.
  3. Extracting Multi-valued Relations from Language Models
    Sneha Singhania, Simon Razniewski, and Gerhard Weikum
    Repl4NLP workshop at the Association for Computational Linguistics (ACL) 2023. (poster)
    [paper] [dataset]
  4. Evaluating Language Models for Knowledge Base Completion
    Blerta Veseli, Sneha Singhania, Simon Razniewski, and Gerhard Weikum
    Extended Semantic Web Conference (ESWC) 2023. (oral)
    [paper] [code]
  5. LM-KBC: Knowledge Base Construction from Pretrained Language Models
    Sneha Singhania, Tuan-Phong Nguyen, and Simon Razniewski
    Semantic Web Challenge at International Semantic Web Conference (ISWC) 2022.
    [website] [proceedings] [code]
  6. Predicting Document Coverage for Relation Extraction
    Sneha Singhania, Simon Razniewski, and Gerhard Weikum
    Transactions of the Association for Computational Linguistics (TACL) 2022. (oral presentation at ACL 2022)
    [paper] [code]
  7. Decision Support System for Playlist Management and Curation
    Abhisek Mukhopadhyay, Sneha Singhania, Shubhashis Sengupta, and Andrew Fano
    DSO workshop at the International Joint Conference on Artificial Intelligence (IJCAI) 2019. (oral)
  8. Deep Learning methods for (i) Extracting Numeric Semantics, and (ii) Entity Relation Classification
    Sneha Singhania
    Master's Thesis 2018.
  9. Predicting GPS TEC maps using Deep Spatio-temporal Residual Networks
    Sneha Singhania, Bharat Kunduri, Maimaitirebike Maimaiti, Joseph B. H. Baker, and J. Michael Ruohoniemi
    American Geophysical Union (AGU) Workshop on ML in Space Weather 2018. (oral)
    [link] [code]
  10. 3HAN: A Deep Neural Network for Fake News Detection
    Sneha Singhania, Nigel Fernandez, and Shrisha Rao
    International Conference On Neural Information Processing (ICONIP) 2017. (oral)
    [paper] [code]

Work Experience

  • Research Intern
    Feb 2023 - Jun 2023
    Apple, Cupertino, USA
  • AI Reseacher and Engineer
    Sep 2018 - Jan 2020
    Accenture Labs, Bangalore, India
  • Research Intern
    May 2018 - Aug 2018
    Google Summer of Code (GSoC)
    Open source organization: Space@VT, University of Virginia Tech
  • Project Assistant
    Aug 2017 - Dec 2017
    VideoKen, Bangalore, India
  • Research Intern
    Jun 2017 - Jul 2017
    VideoKen, Bangalore, India

Scholarships and Awards

  • 2020, 2021: Heidelberg Laureate Forum (HLF) Young Researcher
  • 2019: Star Innovator Award (zone: India) for top performance at Accenture Labs
  • 2019: Selected for CMMRS, LxMLS, Deep|Bayes, and MLSS summer schools
  • 2018: Selected for presentation at ACM's IRISS as one of India's top student papers
  • 2018: Winner of AI challenge in Cognitive Fashion, IBM Research India
  • 2017: Travel grant by Microsoft Research India and AICTE-INAE
  • 2013: INSPIRE fellowship by Government of India


  • Artificial Intelligence based Music Playlist Reordering and Song Performance Assessment. Abhisek Mukhopadhyay, Shubhashis Sengupta, Andrew Fano, and Sneha Singhania. U.S. Patent 11,106,728. 31 Aug. 2021.
  • Artificial Intelligence Based Music Playlist Curation. Abhisek Mukhopadhyay, Shubhashis Sengupta, Andrew Fano, and Sneha Singhania. U.S. Patent No. 11,061,961. 13 Jul. 2021.

Professional Activities

  • Teaching Assistant: Automated Knowledge Base Construction, Summer term 2022
  • PC member and Reviewer: ARR'24, WWW'24, ECIR'24, EMNLP'23, ACL'23, ACML'23, ESWC'23, ISWC'22, CIKM (2020-23) and Sadhana (2018-2022)
  • Volunteer: ICML (2021), ACL (2021), NAACL (2021)

Student Mentoring

Blerta Veseli, Master's Thesis


Hobbies include running, hiking, painting and endurance cycling