Networks of Experts

Find Expertise and Networks of Experts

Discover Knowledge, Streamline Collaboration, Drive Innovation

Most large enterprises are challenged when it comes to rapidly finding the best available experts on a given subject. They cannot rely on self-declared expertise in user profiles of their Enterprise Social Network, too often out of date, incomplete or exaggerated. Finding the true experts requires looking at their work: the footprint they leave in publications, project reports, patent filings, Enterprise Social Media content, emails, HR data and schedules, etc.

The Challenge of Data Complexity and Volume

If you need to find a network of experts, e.g. for a drug repositioning project, you will be looking for experts on the drug in question and on its Mechanism of Action (MOA), medical experts, geneticists, biochemists, etc. But where do you even start looking? Of course, this analysis requires Natural Language Processing (NLP) capabilities to “understand” what topics people have written about, rather than simply searching for keywords. Machine Learning (ML) algorithms help find similar user profiles and similar contents, increasing the precision of expert discovery and ranking.


The information to be treated covers a wide range of subjects: medical, pharmaceutical, biological, chemical, biochemical, genetic, etc. It may deal with diseases, genes, drugs, active agents, and mechanisms of action. A lot of the information is textual, but there may be structured information, like molecule structures, formulae, curves, diagrams, etc.


A research-intensive bio-pharma company has to deal with a vast number of highly technical and scientific data and documents, produced in-house and by others: medical databases, research papers, trial reports, internal notes, emails etc. This volume can reach about 500 million documents while experts may be in the range of 70,000 people and beyond.

Pharma Expertise Network

How Sinequa Insight platform Can Help?

The Sinequa Cognitive Search & Analytics platform can solve this problem: It does not rely on declarations but can sift through tons of text and data, identifying authors and concepts – beyond the actual words used in queries and documents. It will do so in safeguarding contents against unauthorized access. Cognitive Search & Analytics can also verify whether an author is consulted on his fields of expertise via email, and gage the volume of publications and correspondence. It can thus map implicit social networks of experts, and even create links between them.

Pain Points

High data volumes
Heterogeneity of sources
Highly technical content
Structured and unstructured data
Access security


Find expert networks, expertise and related documents in a few clicks
Map expert graphs
Index millions of documents and billions of records


Improve cooperation and accelerate research
Catalyze drug repositioning and shorten Time-to-Market
Optimize clinical trials
Respect information governance and security

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