PeopleofSuperintelligence

Researcher

Jerven Tjalling Bolleman

Also known as Jerven Bolleman, Jerven T. Bolleman

Jerven Bolleman is a Principal Software Engineer in the Swiss-Prot group at the SIB Swiss Institute of Bioinformatics in Geneva, and his work on SPARQL and knowledge graphs for protein data has reached language models. As first author, he published the FALDO ontology for sequence-feature locations in 2016 and HAMAP as SPARQL rules in 2020, and in 2019 he coauthored the paper that brought Rhea reactions into UniProtKB's enzyme annotation. In 2024, he coauthored a retrieval-augmented system that uses large language models to turn questions into federated SPARQL queries over bioinformatics knowledge graphs, and he was among the UniProt Consortium authors of the 2025 UniProt paper in Nucleic Acids Research. GigaScience published his collection of more than 1,000 question and SPARQL query pairs in 2025, offered as material for machine learning, and he coauthored a 2026 study of federated queries over public biological SPARQL endpoints with Ruben Taelman and others.

Timeline

  1. 2016

    The FALDO location ontology

    He is first author of the FALDO paper in the Journal of Biomedical Semantics in 2016, written from the Swiss-Prot group at SIB. FALDO, the Feature Annotation Location Description Ontology, describes the positions of annotated features on linear and circular sequences, and the genome browser JBrowse demonstrates it with UniProt protein annotations mapped to genomic locations.

  2. November 2019

    Rhea reactions in UniProtKB

    In November 2019, he coauthors "Enzyme annotation in UniProtKB using Rhea" with Anne Morgat and colleagues in the Swiss-Prot group. The work replaces UniProtKB's textual descriptions of biochemical reactions with equivalents from Rhea and adds search and query facilities to the UniProt website, REST API and SPARQL endpoint.

  3. February 2020

    HAMAP rules as SPARQL

    He is first author of "HAMAP as SPARQL rules—A portable annotation pipeline for genomes and proteomes", published in GigaScience in February 2020. The paper translates HAMAP annotation rules into W3C-standard SPARQL 1.1 and applies them to protein sequences in RDF with freely available SPARQL engines, producing the same annotation as the in-house pipeline.

  4. October 2024

    An LLM system for SPARQL

    In October 2024, he is second author of a preprint, led by Vincent Emonet, that introduces a retrieval-augmented generation system using large language models to turn user questions into federated SPARQL queries over bioinformatics knowledge graphs. The system draws on knowledge-graph metadata such as query examples and schema information, and a validation step corrects the generated queries.

  5. November 2024

    The UniProt 2025 paper

    He is among the UniProt Consortium authors of "UniProt: the Universal Protein Knowledgebase in 2025", published in Nucleic Acids Research in November 2024. The paper describes limiting UniProtKB to high-quality, non-redundant reference proteomes, continued manual curation of the literature alongside machine learning techniques, and updates to automatic annotation of unreviewed entries.

  6. May 2025

    A GigaScience query collection

    GigaScience publishes "A large collection of bioinformatics question–query pairs over federated knowledge graphs" in May 2025, with him as first author, after an arXiv preprint in October 2024. The collection holds more than 1,000 human-written natural language questions with their SPARQL queries, gathered across research groups at SIB, and serves as source material for machine learning systems that translate questions into SPARQL.

  7. 2026

    Federated SPARQL in practice

    In 2026, he is a coauthor, with Ruben Taelman, Elias Crum and others, of "Does SPARQL federation work in the real world?" for the 25th International Semantic Web Conference. The study runs 67 real-world federated SPARQL queries against more than 20 large public SPARQL endpoints at four time points between Spring 2025 and Spring 2026, and finds that algorithmic federation approaches perform substantially worse than manual ones.

  8. As of October 2026

    Principal software engineer at SIB

    He is Principal Software Engineer in the Swiss-Prot group at the SIB Swiss Institute of Bioinformatics in Geneva.

  1. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes SIB Swiss Institute of Bioinformatics. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  2. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.
  3. The United States employs an estimated 48% of people shaping AIWe estimate where the people shaping AI work today, how universities, companies and research institutes split across countries, and how country shares have changed since the 1960s.
  4. 43% of living people shaping AI work outside their country of birthWe estimate that 43% of living people shaping AI work outside their country of birth (520 of 1,198 people with known birth and employment countries).

Sources and credits

Photo: We have no published photo of Jerven Tjalling Bolleman yet. Send us a free-licensed photo.

  1. FALDO: a semantic standard for describing the location of nucleotide and protein feature annotation - PMC ncbi.nlm.nih.gov. Checked 21 October 2026.
  2. Enzyme annotation in UniProtKB using Rhea - PMC pmc.ncbi.nlm.nih.gov. Checked 21 October 2026.
  3. HAMAP as SPARQL rules—A portable annotation pipeline for genomes and proteomes - PMC ncbi.nlm.nih.gov. Checked 21 October 2026.
  4. [2410.06062] LLM-based SPARQL Query Generation from Natural Language over Federated Knowledge Graphs arxiv.org. Checked 21 October 2026.
  5. UniProt: the Universal Protein Knowledgebase in 2025 - PMC ncbi.nlm.nih.gov. Checked 21 October 2026.
  6. [2410.06010] A large collection of bioinformatics question-query pairs over federated knowledge graphs: methodology and applications arxiv.org. Checked 21 October 2026.
  7. A large collection of bioinformatics question–query pairs over federated knowledge graphs: methodology and applications - PMC ncbi.nlm.nih.gov. Checked 21 October 2026.
  8. Does SPARQL federation work in the real world? A case study over large biological SPARQL endpoints | Ruben Taelman rubensworks.net. Checked 21 October 2026.
  9. Jerven Tjalling Bolleman | People search | SIB Swiss Institute of Bioinformatics sib.swiss. Checked 21 October 2026.