Information Modelling, Management and Ontologies
Summary
Information modelling establishes formal structures—classes, attributes and relationships—to represent entities and events within a domain. It underpins data system design and ensures coherent representation of business rules, metadata schemas and lifecycle policies. Information management extends this by governing data capture, storage, quality, access rights and retention, informed by descriptive and administrative metadata. Ontologies build atop these foundations to define shared vocabularies, enable semantic integration and support machine interpretation. By encoding concepts, taxonomies and logical constraints in a computable form, ontologies facilitate knowledge graphs, automated reasoning and interoperability across heterogeneous systems. Together, these disciplines drive global initiatives—from smart cities and precision medicine to disaster response and industrial automation—by delivering robust, explainable and reusable semantic infrastructures with tangible applications in decision support, data governance and cross-domain discovery.
Research from Nature Portfolio
A domain-aware ontology was devised to automate disaster management tasks according to an established responsibility matrix. The model integrates government agencies, actions and decision rules in a shared ontology, uses a rule layer for task distribution and employs semantic reasoners to support financial assistance decisions, streamlining risk mitigation, response coordination and recovery workflows.
A health knowledge graph was generated directly from electronic medical records by extracting clinical concepts and employing probabilistic models—logistic regression, naïve Bayes and noisy-OR networks. The resulting disease–symptom graph matched or exceeded manual resources in evaluation, showing that automated, data-driven graph assembly can attain high precision with minimal manual curation.
An antibiotic resistance knowledge graph for Escherichia coli was constructed by integrating ten public sources and resolving hundreds of inconsistencies. Iterative link prediction coupled with wet-lab validation uncovered novel resistance genes, demonstrating that hypothesis generation from a curated graph can accelerate discovery and guide experimental verification.
Information Modelling, Management and Ontologies publication trend
The graph below shows the total number of articles in information modelling, management and ontologies across all publications each year (not limited to Nature Index journals).
Technical terms
Information model: A formal representation of entities, attributes and relationships used to structure data and business rules.
Metadata: Data about data that describes structure (structural), content (descriptive) or governance (administrative) information.
Ontology: A computable specification of domain concepts, their relations and constraints, enabling shared semantics and reasoning.
Knowledge graph: A graph-based model of entities and interrelations, often underpinned by an ontology for semantic interpretation.
Automated reasoning: Algorithmic inference techniques applied to ontologies to check consistency, derive hierarchies and infer implicit knowledge.
References
- Disaster management ontology- an ontological approach to disaster management automation. Scientific Reports (2023).
- Learning a Health Knowledge Graph from Electronic Medical Records. Scientific Reports (2017).
- Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes. Nature Communications (2022).
- LOT: An industrial oriented ontology engineering framework. Engineering Applications of Artificial Intelligence (2022).
- Ontology engineering methodologies for the evolution of living and reused ontologies: status, trends, findings and recommendations. The Knowledge Engineering Review (2020).
- Cowl: Pushing OWL 2 over the Edge. Internet of Things (2025).
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