One graph across the warehouse and the papers.
The client’s structured data sat in the warehouse, while most of what its scientists knew about a disease, drug or biomarker sat in internal publications and reports, with no link between them. We built a knowledge graph across both, so a question about any one of those things leads to everything connected to it.
FIGURES ARE APPROXIMATE AND ROUNDED. EXACT COUNTS AND NAMES ARE WITHHELD TO PROTECT THE CLIENT.
A simplified picture of how it connects. Click any node, or start from a question. Copper nodes are entities, gold are warehouse tables, brown are publications.
A biomarker. It ties the genomic test rows that report it to the internal papers that discuss it.
We began with the questions people bring to the data, then defined a small set of entity types to answer them: diseases, biomarkers, therapies, trials.
Warehouse columns are mapped to those entities, and the same entities are pulled out of the publications. Names are matched through standard vocabularies, so different spellings of one thing land on one node.
Each link stores its source, whether a table and column or a page in a document, so any answer can be traced and checked.
Answering that kind of question used to take three people and a dozen files. Now it takes one lookup, with the sources attached.