A curation system of rice trait ontology with reliable interoperation by LLM and PubAnnotation Article Swipe
Javeed Muhammad Ahmad
,
Yawen Liu
,
Jin Dong Kim
,
Xinzhi Yao
,
Pierre Larmande
,
Jingbo Xia
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s44342-025-00058-z
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s44342-025-00058-z
Background Ontology frameworks are essential for organizing complex biological knowledge, such as genes, phenotypes, and pathways, and for ensuring consistent data annotation and retrieval. In biological research, ontologies like the Gene Ontology (GO) and crop-specific trait ontologies (TO) for Oryza sativa (rice) standardize terminology across studies, supporting cross-study comparison and hypothesis generation. However, ontology annotations usually rely on expert manual review of the literature, a process that is accurate but time-consuming, labor-intensive, and difficult to scale as biological data grows. Manual approaches are also prone to inconsistencies and errors. The emergence of large language models (LLMs) such as ChatGPT, DeepSeek, and KIMI, along with curated databases like Rice-Alterome and PubAnnotation, offers new opportunities for semi-automated ontology curation. This study explores how these technologies can be integrated to develop an efficient literature-based curation system for rice trait ontology. Methods We developed a curation system that integrates Rice-Alterome—a comprehensive database of rice genomic variations, mutations, and sentence-level literature evidence linked to GO and TO terms with PubAnnotation, an open-source platform for collaborative text annotation. LLMs (DeepSeek and KIMI) were integrated via APIs to automate the extraction, annotation, and validation of trait-related information via prompt engineering. The system was evaluated through use cases designed to demonstrate its performance and functionality compared to manual curation. Results The proposed system substantially enhanced the retrieval and organization of literature evidence compared to manual methods. The integrated platform, available through a dedicated website, connects Rice-Alterome, PubAnnotation, and LLMs to streamline ontology curation and evidence discovery. This framework reduces the time domain experts need to locate and validate relevant information and provides interactive tools for users to add, merge, or refine trait annotations. The LLM-driven prompt-based querying also improved the identification of implicit or missing information that may be overlooked during manual curation. Conclusions Integrating LLMs with Rice-Alterome and PubAnnotation offers a promising solution for automating rice trait ontology curation. This approach accelerates evidence collection and enhances data consistency and accessibility. Future extensions of this framework will target additional crops such as wheat and maize and focus on refining LLM-based retrieval and annotation mechanisms for broader agricultural genomics applications.
Related Topics
Concepts
Ontology
Interoperation
Computer science
Terminology
Annotation
Open Biomedical Ontologies
Process ontology
Ontology-based data integration
Data science
Data curation
Biological database
Process (computing)
Information retrieval
Ontology alignment
Upper ontology
Data integration
Controlled vocabulary
Ontology language
Suggested Upper Merged Ontology
World Wide Web
Information system
Semantic Web
Semantics (computer science)
OWL-S
Data management
Trait
Database
Semantic integration
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s44342-025-00058-z
- https://link.springer.com/content/pdf/10.1186/s44342-025-00058-z.pdf
- OA Status
- diamond
- References
- 27
- OpenAlex ID
- https://openalex.org/W7108220544
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7108220544Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s44342-025-00058-zDigital Object Identifier
- Title
-
A curation system of rice trait ontology with reliable interoperation by LLM and PubAnnotationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-12-02Full publication date if available
- Authors
-
Javeed Muhammad Ahmad, Yawen Liu, Jin Dong Kim, Xinzhi Yao, Pierre Larmande, Jingbo XiaList of authors in order
- Landing page
-
https://doi.org/10.1186/s44342-025-00058-zPublisher landing page
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https://link.springer.com/content/pdf/10.1186/s44342-025-00058-z.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1186/s44342-025-00058-z.pdfDirect OA link when available
- Concepts
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Ontology, Interoperation, Computer science, Terminology, Annotation, Open Biomedical Ontologies, Process ontology, Ontology-based data integration, Data science, Data curation, Biological database, Process (computing), Information retrieval, Ontology alignment, Upper ontology, Data integration, Controlled vocabulary, Ontology language, Suggested Upper Merged Ontology, World Wide Web, Information system, Semantic Web, Semantics (computer science), OWL-S, Data management, Trait, Database, Semantic integrationTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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27Number of works referenced by this work
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