Scientific Knowledge Management
Providing the foundational technology stack that transforms fragmented data into a unified, machine-interpretable engine for innovation
The scientific knowledge gap
Modern R&D generates massive volumes of data, yet most of it remains trapped in disconnected silos, spreadsheets, and the minds of experts. When researchers retire or projects shift, critical institutional knowledge disappears.
Traditional tools find keywords, not scientific meaning. This lack of context forces Pharmaceutical and Material Science teams to recreate work, slows down multidisciplinary collaboration, and prevents AI from being truly effective. The result? Stalled innovation and a “knowledge gap” that gets wider as complexity grows.
A semantic backbone for enhanced discovery
To master this complexity, organizations are moving beyond simple data storage toward Semantic Knowledge Ecosystems. Graphwise provides the essential layer that sits atop existing systems to connect lab results, documents, and expert reasoning into a machine-interpretable network.
- Unified knowledge across silos: Integrate disparate systems via shared ontologies without disruptive data migrations.
- Semantic search & contextual discovery: Search by scientific concept, not just keywords. Understand hierarchies, synonyms, and domain relationships.
- Institutional memory: Model expert assumptions and decisions, ensuring they remain searchable and reusable for future teams.
- AI-ready, high-quality innovation foundations: Provide structured, high-quality data that eliminates AI hallucinations and enables explainable results.
Our Strategic Moat
The Graphwise contribution
We provide the knowledge management infrastructure required to transform raw data into a trusted semantic backbone, empowering clients and partners to build sophisticated solutions on a governed, scalable foundation.
Semantic Layer Components
Standardize metadata and harmonize vocabularies across structured and unstructured sources. We enable the transition from keyword matching to meaning-based discovery.
Key Benefit:
Cross-domain context and interoperability
Explore one use case in practice
Justify your Investment
Semantic intelligence isn’t just a “nice-to-have”—it delivers measurable business value:
Reduction in Manual Effort
Knowledge graphs eliminate the “data hunt” by automatically connecting concepts and structuring data from diverse protocols.
Over 75% time saved on generating complex clinical summaries and scientific reports.
Why it matters: It directly lowers labor costs and frees up Ph.D.-level experts for innovation rather than administrative “data cleaning.”
Regulatory Responses
In regulated industries, speed is a compliance requirement. Our solution enables instant knowledge reuse by matching new inquiries to semantically related historical answers.
- Reduction in regulatory query turnaround from 2 days to under 1 hour.
- Why it matters: Why it matters: Faster responses reduce the risk of compliance breaches, prevent costly project delays, and improve your standing with global health authorities.
faster Hypothesis evaluation
Accelerate the “Time-to-Insight” by surfacing patterns that traditional search misses. A semantic layer connects disparate datasets (e.g., genes, proteins, and literature) to expedite target identification.
- 1000% faster hypothesis generation and evaluation, and target discovery cycles
- Why it matters: Shortening R&D timelines by even a few months can equate to millions in “first-to-market” value and significantly cut the cost of failed leads.
Success Stories
See what Our Customers and Partners do with Graphwise
customer success story
Oxford Drug Discovery Institute – Accelerating Alzheimer’s Breakthroughs
See the full storyProblem
Alzheimer’s Research UK Oxford Drug Discovery Institute faced the classic knowledge management pain point: vast but disconnected data spread across dozens of biomedical databases.
Solution
By leveraging GraphDB and a rich Linked Life Data Inventory, the Oxford Drug Discovery Institute consolidated 30+ structured sources into a unified platform with advanced analytics like gene-disease association and AI-derived novelty scoring.
Results
- Accelerated drug discovery process by streamlining screening & target analysis
- R&D time & costs reduced from months to weeks or days
- high-confidence target prioritization
Problem
A research lab at Queen’s University faced a months-long manual validation backlog of over 2,000 potential cancer targets identified through genome-wide screens.
Solution
Through the AI Accelerator Program, Graphwise and the lab integrated GraphDB and the Linked Life Data Inventory to unify 200+ datasets and 80 million scientific publications into a searchable knowledge graph
Results
- Reduced validation and research timelines from months to days
- 500% faster insight discovery
- Increased confidence in target selection with explainable, traceable results
on-demand webinar
The Missing Layer Between Scientific Data and Breakthrough Ideas
R&D teams spend too much time searching systems instead of making breakthroughs. Graphwise and Datavid show how a semantic backbone unifies fragmented research data without requiring a costly infrastructure overhaul.
Explore Our Success Stories:
Accelerating Alzheimer’s Breakthroughs
ODDI used GraphDB & LLDI to accelerate target analysis and decision-making by consolidating diverse data sources.
Read more
Accelerating Cancer Research with AI-Powered Target Discovery
Using GraphDB & LLDI, a Queen’s University research lab accelerated cancer research streamlining candidate validation from months to days.
Read moreReady to turn fragmented knowledge into strategic advantage?
Whether you are an organization looking to operationalize your expertise or a partner building next-generation scientific tools, Graphwise provides the semantic engine to get you there.
