Knowledge Mining
Accelerating Pharmaceutical Literature Review Through Knowledge Graph Automation
The published biomedical literature grows by hundreds of thousands of articles per year. Knowledge graph systems that extract structured entity-relationship representations from text at scale transform literature review from a months-long manual exercise into a continuous, automated evidence monitoring capability — enabling research teams to stay current with an evidence base no manual process can track.
Knowledge Mining
Cross-lingual Knowledge Mining in Multinational Research
Multinational pharmaceutical research generates documents in dozens of languages — clinical summaries in Japanese, adverse event narratives in German, regulatory correspondence in French. Cross-lingual knowledge mining is now feasible at scale, but requires specific design choices that differ from monolingual systems.
Knowledge Mining
Incremental Knowledge Mining: Keeping Ontologies Current
A knowledge graph is only as valuable as it is current. As source data changes, ontologies are updated, and new evidence emerges, the graph must evolve continuously. Designing for incremental mining from the start is far less costly than retrofitting it later.
Knowledge Mining
Curation vs. Automation: Finding the Right Balance in Biomedical NLP
The debate between fully automated knowledge extraction and manual curation is a false dichotomy. The productive question is how to allocate human expert attention where it generates the most value — and design automation to handle everything else reliably.
Knowledge Mining
From Raw Data to Knowledge Graph: A Step-by-Step Walkthrough
The journey from a collection of raw pharmaceutical data sources to a queryable, AI-ready knowledge graph involves five distinct stages, each with its own technical and organisational requirements. This walkthrough maps the full pipeline with the decisions and validation steps that make the difference between a prototype and a production system.
Knowledge Mining
Mining Legacy Clinical Databases Without Disrupting Operations
Most pharmaceutical organisations have years or decades of valuable clinical and safety data in legacy relational databases that were never designed for semantic querying. Extracting structured knowledge from these systems without disrupting ongoing operations requires a careful read-only integration approach.
Knowledge Mining
Relation Extraction for Drug–Disease Knowledge Graphs
Identifying entities in biomedical text is only the first step. The real value comes from extracting the relationships between them — drug-indication, drug-contraindication, adverse drug reaction, mechanism of action — and assembling those relationships into a navigable knowledge graph.
Knowledge Mining
Named Entity Recognition in Biomedical Text: Beyond Off-the-Shelf Models
General-purpose NER models trained on news or Wikipedia text consistently underperform on biomedical documents. This piece explains the specific linguistic characteristics of clinical and pharmaceutical text that require specialised models — and the options for building or adapting them without prohibitive cost.
Knowledge Mining
Mining Structured Knowledge from Unstructured Clinical Notes
Between 60 and 80 percent of clinically valuable information in most healthcare organisations lives in free-text notes, discharge summaries, and narrative reports — completely inaccessible to structured analytics. Natural language processing combined with ontology-grounded extraction is now mature enough to change that at scale.