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In the life sciences industry, where data is vast, complex, and often siloed, the ability to use that data, beyond govern it, is critical. For research, clinical, or medical innovation, the goal isn't simply to keep track of data; it's to unlock it. That’s why the combination of ontologies, knowledge graphs, and knowledge discovery platforms is reshaping the way data is managed and, more importantly, used.
In this blog, we explore how these components go beyond traditional data catalogs, and why starting with a knowledge discovery platform is the key to accelerating insight and innovation.
Note: often the words “semantic knowledge platform”, “semantic integration & knowledge exploration platform” and “knowledge discovery platform” are used interchangeably. They all refer to the same concept: platforms that leverage semantics (ontologies + knowledge graphs) to enable intuitive data exploration and insight generation.
Data catalogs have long been promoted as essential for governance. This is for good reason: they inventory data assets, enforce compliance, and support lineage tracking. However, for life sciences professionals who want to ask questions, connect dots, and make decisions, traditional catalogs often fall short. They tell you what exists, but not how it connects (nor interpret the context), and certainly not how to use it.
That’s where semantic technologies make the difference.
By starting with a knowledge discovery platform built on a semantic knowledge graph, life sciences organizations build capabilities supercharging the work of data catalogs: they gain the ability to ask meaningful questions and receive contextual answers.
1. Ontologies: enabling shared understanding
At the foundation of this ecosystem is the ontology. An ontology serves as the semantic glue that connects concepts across disparate datasets. The use of controlled vocabularies, thesaurus and ultimately ontologies, will provide the standardization of datasets and comprehensive framework for reasoning. In life sciences, resources like MONDO or NCIt, enable consistency across clinical, genomic, and scientific domains.
This shared understanding is what enables data to be not just cataloged but also connected and explored.
2. Data catalogs: a strong foundation, made smarter through a knowledge graph
A data catalog is a centralized, organized inventory of data assets from across an organization. When a knowledge graph is in place, building and maintaining a data catalog becomes not only easier but smarter.
A catalog enriched by a knowledge graph and driven by ontologies can:
In other words, a data catalog integrated with a knowledge graph transforms from static inventory into an intelligent guide to what data exists and how it can be used.
3. Knowledge graphs: making data interoperable and actionableTraditional catalogs often fail to meet end-user requirements because they lack semantics. A knowledge graph fills that gap by connecting entities and their relationships across structured and unstructured data, for example genes, diseases, clinical trials, publications, and internal experimental results.
Users see data relationships visually, making it easier to analyze patterns and identify novel insights like:
4. Knowledge discovery platforms: the front door to all of this
Building on the foundation of an ontology, a knowledge discovery platform brings everything together by combining a knowledge graph and an intuitive user interface into a single environment. If a knowledge graph is already in place, the platform builds on top of it; if not, it includes one as part of the solution. If a data catalog is available, the platform also integrates with that, pulling in data and metadata to enrich the discovery experience. As a result, a knowledge discovery platform always serves as the gateway for users to explore, interpret, and apply data meaningfully.
Unlike static catalogs, a knowledge discovery platform:
This is what makes the platform more than a tool: it transforms data into a strategic asset that drives research priorities.
For companies that have a data catalog in place, too often, their employees are told "it’s in the catalog" only to hit a wall of unclear metadata, or disconnected datasets. The result? Lost time trying to use the very data that’s been so carefully governed.
For the life sciences organizations that are considering investing in a data catalog, or already have one, we recommend keeping the catalog for best-in class governance, though always combined with a knowledge discovery platform to find, use, and derive insights from this data.
A knowledge discovery platform turns that frustration into empowerment. It doesn’t replace governance; in fact, it complements and enhances it. By embedding semantic relationships into a metadata layer, organizations can:
If you're a business user, ask your IT team: Can we invest in a knowledge discovery platform so that I have more autonomy to find, access, and use available data? In that way, I no longer am dependent on data scientists or IT, and we avoid a lengthy process.
And if you're in IT or governance, then consider how this shift can make your data assets not only compliant, but also valuable: How can I empower my internal customers to re(use) my assets in a self-service manner?
The future of life sciences research lies not in better governance alone, but in better access. By starting with a knowledge discovery platform built on a robust, ontology-powered knowledge graph, organizations create a living, evolving ecosystem where governance, usability, and insights go hand in hand.
It’s time to move from "what data do we have?" to "what does this data tell us?" That’s the power of semantic knowledge discovery.
Our latest eBook has been written specifically for professionals in the life sciences industry to provide an insightful overview of semantic technology. This comprehensive guide introduces the foundational concepts and modern applications of semantic technology, providing essential insights for organizations aiming to leverage this powerful tool.
