A research knowledge graph built for interdisciplinary discovery
Zenoka aligns scholarly standards with local research meaning and resolves the identities behind each output. Specialist graphs can then be federated without forcing complex expertise into one simplistic hierarchy.
In delivery, The disciplines and institutional systems determine how research identity is resolved. Ontology bridges and graph orchestration respect local governance. Discovery is developed around the questions the university needs to answer.
- Research identity and output resolutionConnect researchers to their affiliations and the grants or projects supporting them. Publications and datasets remain linked to facilities and historical change across source systems.Explore the capability
- Interdisciplinary ontology bridgesAlign disciplinary models through explicit competency questions and contextual mappings that preserve methodological distinctions.Explore the capability
- Federated expertise discoveryExpose collaboration patterns and capability clusters through relevant evidence. Schools and research groups retain ownership of their models.Explore the capability
Connect scholarship without collapsing disciplinary meaning
Research institutions need to discover expertise without flattening disciplinary meaning. People and grants provide one view of capability. Outputs and methods show how that capability has been exercised, while datasets and facilities reveal the infrastructure behind it. Partnerships extend the picture beyond the institution. Disciplines may represent similar subjects differently for sound methodological reasons. Zenoka uses research information architecture and identity resolution to connect the record. Ontology alignment and federated graphs enable discovery while preserving those distinctions.
We help institutions connect this capability to research and education strategy. Partnership and infrastructure choices can draw on the same evidence, as can impact and responsible-AI planning. Scholarly value remains the primary test. Student and researcher experience show how the service will work in practice, while interdisciplinarity and equity reveal who may benefit or be excluded. Assurance and technical dependency establish the operational boundary. Cost and the ability to sustain the service complete the case. Roadmaps begin with priority decisions and communities rather than institution-wide data consolidation.
Operating-model work establishes who stewards shared research information and where academic authority remains decisive. Contribution and review processes protect quality. Privacy and ethics set boundaries on use, while the relationship between central services and disciplinary ownership determines who may change meaning. Shared infrastructure can grow through credible governance without taking specialist evidence away from schools and research groups.
Researchers remain connected to their outputs and grants. Methods and facilities give datasets context as partnerships cross disciplinary or system boundaries.
Resolve the research ecosystem
Our data-engineering experts connect researchers to their affiliations and projects. Funders and publications show how the work was supported and communicated. Datasets and facilities add the research resources involved, while historical changes preserve the record over time. Institutional and external sources are reconciled with confidence and provenance visible. Name similarity or an incomplete profile cannot silently become false attribution.
Quantitative analysis can use that resolved ecosystem to examine collaboration and funding patterns. Skills and publication evidence show where capability is established, while data reuse and infrastructure demand reveal how research resources circulate. Partnership patterns extend the view beyond the institution. Network methods identify bridging communities and hidden concentration. Forecasting supports capacity and investment planning, while evaluation explores outcomes. Selection effects remain explicit, as do differences by discipline or career stage and limits in data coverage.
Measures require governed populations and calculation rules before they can be compared. This applies to interdisciplinary activity and impact, as well as access or research strength. We build decision-specific views rather than a universal ranking. Each view shows its uncertainty and underlying evidence, together with the aspects that the measure cannot represent.
Bridge disciplines through questions, not keywords
Competency questions establish which relationships need to be shared across disciplines. Candidate ontology mappings are then assessed structurally and logically. Evidence supports the proposed correspondence, while expert judgement determines whether it is valid in context. Contextual similarity remains distinct from exact equivalence. Interdisciplinary discovery becomes possible without flattening concepts on which research validity depends.
Federate specialist knowledge
Schools and research groups can retain their domain models and permissions. Their release cycles also remain under appropriate local ownership. Shared identifiers and mappings create points of connection, while registries and query contracts support institution-wide discovery. Leaders gain a coherent view of collaboration and capability without imposing one master hierarchy on the research community.
Adoption can begin with a concrete institutional question. One workflow may locate facilities for a research team. Another may build a thematic partnership, understand doctoral capacity, or support a funder requirement. Benefits and limitations are evaluated in that workflow before the architecture extends. Training and incentives are delivery requirements from the outset. Data quality and scholarly review receive the same treatment rather than being deferred to change management.
Let questions travel farther than disciplines
A research graph can connect a strategic theme to the people and methods relevant to it. Outputs and datasets show existing evidence, while facilities and partners reveal the available delivery network. Grants and reviewed expertise establish where capability has been supported and validated. Statistical and network analysis show whether that capability is established or emerging. They can also reveal where it is disconnected or constrained. Advisers then compare recruitment with infrastructure investment, or partnership with funding and convening options, with the evidence exposed.
Strategic monitoring keeps this picture current. External funding and policy developments may change institutional opportunity. Technology and research developments may change the capability required. A new call can be mapped to evidence of existing expertise and collaboration. Semantic alignment broadens discovery beyond exact terminology, while disciplinary interpretation and research-information rights continue to govern the response.
The same composition can support student research opportunities and equipment planning. Open-science programmes and societal-impact evidence can draw on the shared context where permissions allow. Ethical and contractual responsibilities set the boundaries, together with institutional authority. Integration creates a navigable evidence environment for decision and collaboration without implying that research quality can be inferred from connectivity alone.
Reveal the collaborations the institution cannot yet see
Research strategy improves when institutional questions are interpreted through disciplinary meaning. Scholarly evidence can then be examined as a network without losing the context behind each pattern.
Define the strategic research question
Clarify the priority research themes and the funding opportunities connected to them. Societal outcomes establish why the work matters. Capability gaps can then be assessed against the evidence needed for an investment or partnership decision.
Explore the related serviceConnect the research landscape
Resolve researchers to their affiliations and the grants supporting their work. Outputs remain connected to methods and facilities, along with the datasets produced or used. Disciplinary mappings and provenance-aware identities reveal potential partners without erasing context.
Explore the related serviceFind patterns worth acting on
Use network analysis to expose clusters and the bridges between them. Portfolio measures reveal material gaps, while geospatial reach shows where capability is distributed. Forecasting and interactive discovery help leaders identify emerging opportunities worth acting on.
Explore the related service
Leaders and researchers can identify credible collaborations and investments while retaining the disciplinary evidence and responsible expertise behind each connection.
