Harness the synergy of global research knowledge, ontologies, and your own data
The Dimensions Knowledge Graph, powered by metaphactory, simplifies and accelerates the generation of actionable insights across the entire pharma value chain. Built on more than 32 billion structured statements, it delivers a wealth of scientific knowledge derived from global research and public datasets. Its underlying semantic model gives you the tools you need to additionally connect your internal knowledge and delivers a layer of trust and explainability for AI algorithms and applications built on top of the graph.
Data in the knowledge graph is drawn not only from the Dimensions’ interconnected scientific research database and public data sources like STRING or UMLS, but also from rich domain ontologies that inform and power precise and consistent data annotation. These ontologies comprise entities extracted from scientific text, as well as relationships between entities–for example complex cause-and-effect relations between agents such as proteins and drugs. This ensures that incoming data, whether internal or external, is semantically harmonized, enabling discoverability and interoperability, as well as trust in the extracted insights for decision-making.
Endless knowledge at your fingertips. Imagine the possibilities.
With the Dimensions Knowledge Graph, data becomes contextualized and FAIR (Findable, Accessible, Interoperable, Reusable), and is transformed into actionable knowledge that is accessible across the entire organization. Ontology-informed data enable consistent annotation at scale, making previously inaccessible insights discoverable and ready to drive innovation.
- Fast-track target discovery and reduce research costs: Enabling company-wide access to interconnected internal and external data can allow researchers to create hypotheses and validate them against global research knowledge and literature, accelerating time-to-market for new drugs and therapies.
- Streamline processes from R&D to clinical trials to market access: Research and lab data can become available to manufacturing plants, helping streamline knowledge transfer and the planning of clinical trials, manufacturing, distribution and market access processes.
- Reuse existing data and knowledge, both internal and external: A unified semantic layer makes data FAIR, supports the automation of data collection and integration, and enables reusability of data, ontologies and vocabularies, across your organization.
- Speed up drug safety review processes and optimize risk control: Existing research findings can be leveraged as supporting metadata in review processes mandated by regulatory agencies, thus speeding up approval processes.
- Leverage the power of knowledge graphs with LLMs: The underlying knowledge graph can be combined with Large Language Models (LLMs) for improved relation extraction at optimal cost, but also to drive internal end-user interfaces for knowledge discovery.
- Enable and accelerate complex discovery workflows: Data is transformed into actionable knowledge available company-wide and can support research initiatives and business decisions or drive forecasts and prediction.
- Leverage chemical and biomedical ontologies: Ontologies powering the Dimensions Knowledge Graph help you transform siloed, unstructured data into machine-readable, standardized formats, allowing your scientists to integrate and compare information internally and with external databases.
Learn more and request a demo
To learn more about the Dimensions Knowledge Graph and request a demo to see the solution in action, please visit metaphacts.com/dimensions-knowledge-graph