Open Knowledge Maps

Rather than returning a ranked list of papers that must be read sequentially to understand a field, Open Knowledge Maps generates a visual knowledge map showing the main research areas within a topic at a glance, with relevant papers and concepts clustered and connected within each area.

This bird's-eye view of a literature landscape enables dramatically faster literature orientation than traditional search engines.

The platform is built on natural language processing and machine learning applied to academic literature databases, automatically clustering papers by topic similarity and identifying the key themes and sub-fields within any research area.

Users simply enter a research topic or question, and within seconds receive a knowledge map showing how the literature is organizedwhich areas are active, which are adjacent, and where the conceptual clusters sit in relation to each other.

Each cluster within the map is clickable and reveals the underlying papers and key concepts associated with that theme.

Open Knowledge Maps has expanded its capabilities through specialized tools developed for specific research use cases. The Streamgraph visualization shows how research topics evolve and shift in prominence over time, enabling researchers to identify emerging trends and declining areas of interest within their field.

Custom Clustering allows users to group research results according to user-defined thematic categories, useful for researchers who want to organize literature according to their own conceptual framework rather than the algorithm's default clustering.