What is AcademicGraph?
AcademicGraph is an AI-native knowledge graph for scholarly discovery, intellectual property, and knowledge translation. It models the complete research ecosystem, mapping every node in the knowledge chain from knowledge producer to knowledge user. Researchers, research groups, laboratories, institutions, publications, patents, funding programmes, and spinout companies are interconnected within a structured semantic ontology.
Millions of curated data points, drawn from scholarly literature and the open web, operate as a unified intelligence layer, purpose-built to move research beyond publication towards discovery, translation, and impact.
Unique Value
Scholarly knowledge is distributed across hundreds of databases, repositories, and institutional systems, almost entirely in raw form. AcademicGraph has been purpose-built for modern agentic AI workflows, providing a structured, ontology-grounded representation of the research ecosystem that enables accurate, explainable, and context-rich retrieval rather than returning raw publications associated with a keyword or DOI.
Governed by two enduring principles, accuracy and relevance, AcademicGraph is anchored by a deliberate knowledge horizon from 2020 onward, designed to surface what is most relevant today and in the years ahead rather than simply archiving the past.
The goal is to create a hyper-realistic picture of what is actually happening across the research ecosystem. Rather than relying on large data dumps and shallow coverage, AcademicGraph maps every laboratory, every research group, and every funding line across Australasia; not a sample, not a proxy.
Beyond its native academic taxonomy, the graph integrates dozens of global standard taxonomies and field-specific classification frameworks spanning research, clinical, engineering, space, quantum, and financial domains, ensuring every concept is placed within the precise disciplinary context to which it belongs.
A regional-first deployment strategy achieves comprehensive institutional coverage before expanding into new geographies. Full coverage across Australasia encompasses universities, research institutes, funding organisations, laboratories, research groups, and researchers throughout Australia and New Zealand.
That depth is not a regional limitation. For global users, it provides a microscopic view of one of the world's most distinctive and productive research ecosystems.
AcademicGraph is built to connect the people, capabilities, intellectual property, and funding that drive research impact, rather than simply indexing publications.
What is a Graph and Why?
AcademicGraph is more than a bibliographic record of research. It is a structured representation of the full research ecosystem, capturing publications, patents, funding, policy influence, spinouts, and the organisational structures that surround research activity.
This requires more than a traditional database. Research is modelled as a complex system, where information is represented as a network of relationships rather than rows and columns.
In this system, entities include researchers, publications, patents, institutions, research groups, laboratories, funding bodies, and spinout companies. Together, they represent the full value chain from knowledge producer to knowledge user, and everything in between.
What gives the graph its power is not just the entities themselves, but the relationships between them. These connections carry meaningful context, including citations, collaborations, funding links, policy influence, and institutional affiliations.
The Data Model
The heart of AcademicGraph is its data model, which maps the Scholarly Knowledge Model: a structured system of interlinked concepts, entities, relationships, affiliations, organisations, funding programmes, and intellectual property. Most data undergoes semantic distillation before entering the graph, ensuring every node and relationship carries verified, contextualised meaning rather than raw metadata.
The graph places data in context through linking and semantic annotation, providing a unified framework for knowledge integration, research intelligence, and analytical discovery.
The main entities are organised into the following categories, all represented as interconnected nodes and relationships.
The Knowledge Pipeline
AcademicGraph is not a repository or data aggregator. It processes millions of publications, patents, and records but does not present them in raw form. There are dozens of tools built for literature survey and raw record retrieval. AcademicGraph is built for what comes after: extracting what those records contain, mapping how they connect, and surfacing the intelligence within them.
Data enters from hundreds of open repositories, shared registries, and curated open web sources. At this stage it is raw, fragmented, and inconsistent, the same form in which it exists across every other database in the world. What happens next is what makes AcademicGraph different.
Semantic Distillation and Concept Extraction
Before any data enters the graph, it passes through a semantic distillation process. Machine learning models extract core concepts, entities, and relationships from each source document, reducing noise and isolating what is meaningful. The output is not a copy of the original record. It is a structured, verified representation of the knowledge it contains.
Vector Embedding
Extracted concepts are encoded as vector embeddings, enabling semantic similarity and contextual proximity to be understood across disciplines and taxonomic boundaries.
Ontology Mapping
Each concept and entity is mapped to AcademicGraph's semantic ontology and positioned against dozens of global standard and domain-specific taxonomies, spanning research, clinical, engineering, space, quantum, and financial domains.
The Graph
Vector embeddings, semantic concepts, and taxonomy classifications are integrated into a knowledge graph as interconnected nodes and relationships. Researchers, patents, funding programmes, research outputs, organisations, laboratories, and research groups are linked through a structured network that reflects the complexity of the research ecosystem.
The result is a living map of the research ecosystem, where knowledge can be explored through relationships, proximity, capability, funding, collaboration, and impact rather than as isolated records.
Access
AcademicGraph is not directly accessible as a standalone product. Its intelligence is surfaced through AcademicFellows, the research collaboration and capability mapping platform powered by AcademicGraph.
AcademicFellows is free for individual researchers. Anyone can access it through their institutional ID. No separate account. No password. For more details visit Access.
Researchers can explore their knowledge profile, surface connections across disciplines and institutions, identify peers working in adjacent areas, and discover relevant funding opportunities through a single institutional login. Coverage spans New Zealand, with Australian institutions being added progressively.
AcademicFellows currently represents the first access layer built on AcademicGraph. Further pathways and integrations are in development across research, industry, government, and investment.
FAQs
What is AcademicGraph?
AcademicGraph is an AI-native knowledge graph built for scholarly discovery, intellectual property, and knowledge translation. It connects researchers, institutions, research groups, laboratories, publications, patents, funding programmes, and spinout companies within a structured semantic ontology. It is the intelligence layer that powers AcademicFellows and the foundation for future research, industry, and investment applications.
What is AcademicFellows?
AcademicFellows is a research collaboration and capability mapping platform powered by AcademicGraph. It makes expertise, capabilities, funding opportunities, and research connections visible across the ecosystem. Every researcher has a knowledge profile showing their research concepts, collaborations, affiliations, outputs, and how their work connects to the wider research ecosystem.
What is the difference between AcademicGraph and AcademicFellows?
AcademicGraph is the knowledge graph and intelligence layer. AcademicFellows is the collaboration and capability mapping platform built on top of it. AcademicGraph processes, structures, and connects research knowledge. AcademicFellows makes that knowledge navigable and actionable for researchers, institutions, and industry partners.
How is AcademicFellows different from other research discovery platforms?
Most research discovery platforms index records, surface citations, and return search results. AcademicFellows is built around knowledge translation. It maps what publications contain, who produced them, how expertise connects across institutions, and where it leads in terms of collaboration, capability, and funding. The result is not a list of records, but a living map of capability across the research ecosystem.
Why does AcademicGraph focus on recent knowledge and specific regions?
AcademicGraph follows a regional-first strategy, covering universities, research institutes, departments, research groups, laboratories, funding programmes, patents, and spinouts within an ecosystem before expanding into new geographies. It is designed to surface what is most relevant today and in the years ahead while preserving the context needed to understand emerging capability and research direction.
What is a researcher knowledge profile?
A knowledge profile is a structured representation of a researcher's work within AcademicGraph, built from publicly available publication data. It reflects core research concepts, affiliations, collaborative networks, outputs, patents, projects, and funding, showing not just what a researcher has published, but what they work on, who they work with, and how their expertise connects across the ecosystem.
Why does AcademicFellows use federated access rather than a standard login?
Effective research collaboration depends on trust, identity, and institutional context. Federated identity ensures profiles are tied to verified institutional identities, reducing impersonation, duplicate accounts, and automated abuse. The same credentials used to access university systems provide access to the network without a separate account or password. Federated access is the gold standard for global research infrastructure.
What is semantic distillation?
Semantic distillation is the process by which AcademicGraph extracts core concepts, entities, and relationships from source documents using machine learning models. Rather than storing a copy of the original record, the graph holds a structured, verified representation of the knowledge it contains.