Education

• Ph.D in Artificial Intelligence
  Vrije University Amsterdam, Amsterdam, Netherlands, 2019.9 - present
• M.S. in Information Science
  Institute of Scientific and Technical Information of China, Beijing, China, 2016.9 - 2019.1
• B.S. Library Science
  Northeast Normal University, Changchun, China, 2010.9 - 2014.7

Work experience

• PhD Researcher - AI Engineering & LLM Systems, 2019.09 - present
  • VU Amsterdam, Amsterdam, Netherlands
  • Developed applied AI systems for question answering, retrieval, and recommendation over scientific knowledge, covering problem definition, data preparation, model and retrieval design, evaluation, error analysis, and iterative improvement.
  • Developed FIRESPARQL, an LLM-based natural-language-to-SPARQL framework; fine-tuned Llama 3 8B with LoRA using Hugging Face and LLaMA-Factory, and built reproducible inference and evaluation pipelines for the SciQA Benchmark.
  • Built ontology-grounded RAG workflows using GPT-4, embedding-based retrieval, and structured prompts to generate competency questions across multiple domains.
  • Designed a property-guided subgraph retrieval framework combining semantic entity and property alignment, graph traversal, LLM-based filtering, and structured evidence selection for multi-hop knowledge graph question answering.
  • Engineered batch pipelines to execute and evaluate 2,600+ SPARQL queries on million-scale RDF data using Python, QLever, Virtuoso, Docker, and HPC/SLURM.
  • Built a scholarly paper recommendation pipeline over 100k+ papers, including parsing, preprocessing, embeddings, user profile generation, ranking, and evaluation.
• Visiting PhD Researcher, 2025.08 - 2025.10
  • Open University, United Kingdom
  • Worked on GraphRAG-related research, focusing on retrieval and reasoning over graph-structured knowledge.
• Data Analyst User Growth (Full-time Internship), 2018.9 - 2019.1
  • Beijing Didi Chuxing Technology Co., Ltd., Beijing, China
  • Duties includes:
    • Monitored and analyzed product data to identify trends and insights, optimizing user experience and supporting sales targets.
    • Collected, processed, and analyzed user behavior data from multiple channels to develop comprehensive user profiles.
• Data Analyst (Part-time Internship), 2018.3 - 2018.9
  • John Wiley & Sons, Inc, Beijing, China
  • Duties included:
    • Conducted comprehensive data analysis on Wiley journal database usage for all universities in China, providing insights into academic resource consumption.
    • Evaluated the cost-effectiveness of journal subscriptions, supporting data-driven decision-making for university libraries.
    • Identified key user behavior patterns and formulated strategies to optimize academic journal access and utilization.
• Library Assistant (Full time), 2014.7 - 2015.9
  • The Chinese University of Hong Kong, Shenzhen, China
  • Duties included:
    • Perform copy cataloging of bibliographic records to enhance discoverability and equity of access to materials in the collection. This is achieved by using the Library’s ILS, OCLC Connexion, and other online tools to identify, edit, and import bibliographic records using Dewey Decimal Classification (DDC), Library of Congress Authorities (such as LC Subject Headings) and Resource Description and Access (RDA) standards.
    • Maintain the accuracy of holdings/records in OCLC to support Interlibrary Loan service.

Skills

• Programming: Python, SQL, Pandas, Numpy, PyTorch, Scikit-learn, Gensim, Transformers
• LLMs: RAG implementation using GPT Assistant and LlamaIndex
• Semantic web technologies: SPARQL, RDF, RDFS
• Graph databased: Neo4j, GraphDB
• Language: English, Mandarin, Cantonese, Dutch

Publications

FIRESPARQL: A LLM-based Framework for SPARQL Query Generation over Scholarly Knowledge Graphs Permalink

Pan, X., de Boer, V., & van Ossenbruggen, J. (2025). FIRESPARQL: A LLM-based Framework for SPARQL Query Generation over Scholarly Knowledge Graphs. arXiv:2508.10467. Accepted at the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K).

A RAG Approach for Generating Competency Questions in Ontology Engineering

Pan, X., Ossenbruggen, J.v., de Boer, V., Huang, Z. (2025). A RAG Approach for Generating Competency Questions in Ontology Engineering. In: Sfakakis, M., Garoufallou, E., Damigos, M., Salaba, A., Papatheodorou, C. (eds) Metadata and Semantic Research. MTSR 2024. Communications in Computer and Information Science, vol 2331. Springer, Cham. https://doi.org/10.1007/978-3-031-81974-2_6

Column Vocabulary Association (CVA): Semantic Interpretation of Dataless Tables

Martorana, M., Pan, X., Kruit, B., Kuhn, T., & van Ossenbruggen, J. (2024). Column Vocabulary Association (CVA): Semantic Interpretation of Dataless Tables. In 2024 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, SemTab 2024 (pp. 27-42). CEUR-WS. org.

Closed-Source vs Open-Source RAGs: lessons learned from the SemTab challenge

Martorana, M., Pan, X., Kruit, B., Kuhn, T., & van Ossenbruggen, J. (2024). Closed-Source vs Open-Source RAGs: lessons learned from the SemTab challenge.

Enhancing Scholarly Paper Recommendation by Modelling Diversity of Research Interests

Pan, X., Wang, S., Liu, T., van Ossenbruggen, J., de Boer, V., Huang, Z. (2024). Enhancing Scholarly Paper Recommendation by Modelling Diversity of Research Interests. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2024. Communications in Computer and Information Science, vol 2145. Springer, Singapore. https://doi.org/10.1007/978-981-97-5934-7_16

KG4NH: a comprehensive knowledge graph for question answering in dietary nutrition and human health

Fu, C., Pan, X., Wu, J., Cai, J., Huang, Z., van Harmelen, F., ... & He, T. (2023). KG4NH: a comprehensive knowledge graph for question answering in dietary nutrition and human health. IEEE journal of biomedical and health informatics.

A Semantic Web Technology Index

Lan, G., Liu, T., Wang, X., Pan, X., & Huang, Z. (2022). A semantic web technology index. Scientific reports, 12(1), 3672.

Exploring the microbiota-gut-brain axis for mental disorders with knowledge graphs

Liu, T., Pan, X., Wang, X., Feenstra, K. A., Heringa, J., & Huang, Z. (2021). Exploring the microbiota-gut-brain axis for mental disorders with knowledge graphs. Journal of Artificial Intelligence for Medical Sciences, 1(3), 30-42.

Predicting the relationships between gut microbiota and mental disorders with knowledge graphs

Liu, T., Pan, X., Wang, X., Feenstra, K. A., Heringa, J., & Huang, Z. (2021). Predicting the relationships between gut microbiota and mental disorders with knowledge graphs. Health information science and systems, 9, 1-9.

Talks and Posters

Enhancing QA over Scholarly Knowledge Graphs: Addressing Semantic and Structural Challenges

February 27, 2025

Poster at ELLIS workshop on Representation Learning and Generative Models for Structured Data, Amsterdam, NL

A RAG Approach for Generating Competency Questions in Ontology Engineering

February 26, 2025

Invited Talk at Elsevier, Amsterdam, NL

Creating a Fine-grained Scholarly Knowledge Graph from Abstracts

April 11, 2024

Poster at ICT.OPEN2024, Utrecht, NL

Awards

• 3rd place in AI and I Hackathon 2024 (Munich): Co-Creativity between Humans and LLMs
• Post-graduate Academic Scholarship (2nd Class) , 2016-2018, by Institute of Scientific and Technical Information of China, Beijing, China
• Undergraduate Academic Scholarship (2nd Class) , 2012-2014, by Northeast Normal University, Changchun, China