Knowledge Organization in AI-Era Biology

Biological knowledge organization systems (KOSs), such as taxonomies, controlled vocabularies, ontologies, and knowledge graphs, were traditionally developed to classify and represent existing knowledge. Today, these systems are increasingly integrated with ML, NLP, and LLMs. They are no longer only resources that researchers search or retrieve information from. In many cases, they have become part of the computational process through which scientific knowledge is analyzed and discovered.

In 2025, while preparing for my candidacy, I conducted a thematic review to understand the transition into AI-assisted science and how the roles of biological KOSs have changed alongside scientific and technological development. Existing classifications of KOSs could not fully describe what is happening in AI-augmented biological research. A simple ontology may be structurally sophisticated but still rely heavily on manual expert curation. Another system may automatically integrate large amounts of biological data but have relatively limited reasoning ability. More recently, some systems combine ontologies, knowledge graphs, and AI models to generate predictions or even scientific hypotheses.

To capture these differences, I developed the Knowledge Organization Analysis Framework (KOAF), placing KOSs on a single scale from simple to complex across three dimensions:

(1) Functional sophistication: from basic terminology disambiguation and data standardization to AI-driven knowledge discovery.

(2) Automation in system construction: from completely manual expert curation to highly automated knowledge extraction and integration.

(3) Reasoning and inference capability: from simple information retrieval to logical reasoning, cross-domain inference, and eventually AI-supported hypothesis generation.

I then applied this framework to representative studies published between 2003 and 2025.

The patterns showed earlier biological KOSs primarily helped researchers establish common terminology and organize heterogeneous data (what traditional systematics and phylogenetics do). Formal ontologies such as Gene Ontology later provided richer semantic structures that machines could process and reason over. More recent systems however, increasingly combine ontologies and knowledge graphs with AI models to integrate knowledge across domains, identify previously hidden relationships and support scientific discovery.

To me, the most interesting part of this transition is that KOSs are beginning to move beyond their traditional role as passive knowledge repositories. When an ontology or knowledge graph becomes part of an AI system that generates a prediction, suggests a biological relationship, or contributes to a new hypothesis, the organization of knowledge itself begins to influence what can be discovered.

In the paper, we describe these advanced KOSs as increasingly functioning as epistemic agents in scientific research.

This also raises new questions. If AI models rely on organized domain knowledge to perform scientific reasoning, how should this knowledge be structured and validated? How much of scientific discovery comes from the AI model, and how much comes from the underlying knowledge organization system? And when these systems contribute to new scientific claims, how can we ensure their transparency, accountability, and trustworthiness?

The full paper, “Knowledge Organization in AI-Era Biological Sciences: A Thematic Review,” is published in Knowledge Organization. https://doi.org/10.31083/KO46546

An interactive visualization of the representative studies mapped to the KOAF framework is available at: https://qiaoyiliu.github.io/KOAF/targetpaper3D.html

The full list of literature mapped to KOAF:

Title	Author(s)	Year
eVOC: A Controlled Vocabulary for Unifying Gene Expression Data	Kelso et al.	2003
The Unified Medical Language System (UMLS): integrating biomedical terminology	Bodenreider	2004
The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration	Smith et al.	2007
Integrating phenotype ontologies across multiple species	Mungall et al.	2010
Darwin Core: An Evolving Community-Developed Biodiversity Data Standard	Wieczorek et al.	2012
The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery	Dumontier et al.	2014
Semantics in Support of Biodiversity Knowledge Discovery: An Introduction to the Biological Collections Ontology and Related Ontologies	Walls et al.	2014
Knowledge Discovery from Biomedical Ontologies in Cross Domains	Shen & Lee	2016
Neuro-symbolic representation learning on biological knowledge graphs	Alshahrani et al.	2017
The DisGeNET knowledge platform for disease genomics: 2019 update	Piñero et al.	2020
Combining lexical and context features for automatic ontology extension	Althubaiti et al.	2020
BiOnt: Deep Learning using Multiple Biomedical Ontologies for Relation Extraction	D. Sousa & Couto	2020
Evolving knowledge graph similarity for supervised learning in complex biomedical domains	R. T. Sousa et al.	2020
Towards Semantic Integration for Explainable Artificial Intelligence in the Biomedical Domain:	Pesquita	2021
Extracting a Knowledge Base of Mechanisms from COVID-19 Papers	Hope et al.	2021
The Human Phenotype Ontology in 2021	Köhler et al.	2021
Biomedical knowledge graph embeddings for personalized medicine: Predicting disease-gene associations	Vilela et al.	2023
The Gene Ontology knowledgebase in 2023	The Gene Ontology Consortium et al.	2023
Building a knowledge graph to enable precision medicine	Chandak et al.	2023
Gene-associated Disease Discovery Powered by Large Language Models	Chang et al.	2024
A framework for integrating biomedical knowledge in Wikidata with open biological and biomedical ontologies and MeSH keywords	Turki et al.	2024
An LLM-based Knowledge Synthesis and Scientific Reasoning Framework for Biomedical Discovery	Wysocki et al.	2024
UniEntrezDB: Large-scale Gene Ontology Annotation Dataset and Evaluation Benchmarks with Unified Entrez Gene Identifiers	Miao et al.	2024
Unifying Large Language Models and Knowledge Graphs: A Roadmap	Pan et al.	2024
TaxonGPT: Taxonomic Classification Using Generative Artificial Intelligence	H. Huang et al.	2024
Building a literature knowledge base towards transparent biomedical AI	Y. Huang et al.	2025
Network for knowledge Organization (NEKO): An AI knowledge mining workflow for synthetic biology research	Xiao et al.	2025