STARTUP RADAR improvement performance test and validation

Bringing AI to corporate innovation and facilitating internal team communication around innovation scouting and monitoring.

Linknovate brings AI to corporate innovation and facilitates internal team communication around innovation scouting and monitoring. Through data mining and ML we help our clients detect innovation activity of competitors, partners, providers and newcomers (e.g. startups).

The STARTUP RADAR tool automatically extracts semantic relations between entities from unstructured and heterogeneous data sources. For our business exploitation use case, we aim at detecting 3 critical events: Mergers & acquisitions, funding events and launch of products. An additional feature in the design of our radar is locating “Similar To” companies mainly based in their know-how and products. Current providers usually rely on financial figures, type of funding round (e.g. series A), # employees, geolocation, etc. But some of the most exciting info for the user relies on what these companies actually do and know. 

Furthermore, very few existing systems utilize underlying item attributes for explaining recommendations to users. We plan to exploit the structured & unstructured info about companies as item attributes to justify our recommendations with meaningful explanations (Explainable AI).

This pilot aims to test and enhance STARTUP RADAR (startup data module) and INNOSCOUT (search engine) recommendation capabilities to provide our clients with state-of-the-art startup insights. In this regard, we will explore the possibility of OpenAIRE Graph and OpenAIRE Monitor usage to enrich our tools.

Finally, once we validate that the STARTUP RADAR tool is achieving a superior recommendation feature and a human understandable explanation of these company similarities, we would integrate this tool within our discovery engine product (INNOSCOUT), and explore the possibility to offer its capabilities over API.



    1. Exploring and testing the potential enrichment of our datasets in STARTUP RADAR & INNOSCOUT (our backbone search engine) with OpenAIRE graph. Gather technical feedback from OpenAIRE on the use of the graph and its pros and cons for deployment in production. 
    2. Acquiring feedback from OpenAIRE about the feasibility of use of OpenAIRE Monitor to enhance our data analytic capabilities. 
    3. Studying and receiving feedback to operate as provider via API of OpenAIRE and/or Resilience in order to help them with data such as: 
    • Curated Startup/SME list
    • Indicators for individual funders such as VC investment
    • Relationships between organizations: investments, merges and acquisitions
    • Other Startup/SME info in our database that they think that may be useful

Work Plan

WP1 – Project Management, Objectives & Pilot plan. M1-8

Efforts (PM): 1

WP2 – Exploration and Evaluation of technical viability in the use of OpenAIRE tools and potential enhancements in STARTUP RADAR & INNOSCOUT products in LINKNOVATE. M2-M5. 

Outputs [Milestone1]: Feasibility analysis for technical advancement of STARTUP RADAR and/or INNOSCOUT, thanks to EOSC tests and technical feedback gathered.

Efforts (PM): 2.

WP3 – Validation & Testing. Exploitation, Communication & Dissemination. M4-M9

Outputs: Business Model exploration for STARTUP RADAR – OpenAIRE and/or Reliance and viability analysis.

Milestone 2: Test & Validation of STARTUP RADAR beta version with use-case owners

Efforts (PM): 4

Business Partner

EOSC Service provider

Supporting project