From Code to Concept: A Semantic Approach to AI Innovation Discovery in Open Source Software Repositories | Zendy

Inovalija | Zendy; Dumitru Roman | Zendy; Federico Belotti | Zendy; Vladimir Alexiev | Zendy; Luis Rei | Zendy; Roberto Avogadro | Zendy; Babak Khalilvandian | Zendy; Boyan Bechev | Zendy; Catalina Alexandra Chinie | Zendy; Iulia Ciurea | Zendy; Janez Brank | Zendy; Cosmin Udroiu | Zendy; Ahmet Soylu | Zendy; Matteo Palmonari | Zendy

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From Code to Concept: A Semantic Approach to AI Innovation Discovery in Open Source Software Repositories

Author(s) -

Inovalija,

Dumitru Roman,

Federico Belotti,

Vladimir Alexiev,

Luis Rei,

Roberto Avogadro,

Babak Khalilvandian,

Boyan Bechev,

Catalina Alexandra Chinie,

Iulia Ciurea,

Janez Brank,

Cosmin Udroiu,

Ahmet Soylu,

Matteo Palmonari

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3590135

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Artificial Intelligence (AI) is a transformative force driving innovation, yet tracking AI-related advancements remains challenging due to the rapid pace of development and unstructured data from platforms like GitHub. This paper proposes an AI-driven approach to innovation detection, leveraging GitHub as a data source to systematically identify and link AI projects to organizations. Key contributions include a domain-specific taxonomy comprising 7,490 AI topics, a modular pipeline for semantic annotation and entity linking, and a trend detection framework based on Singular Spectrum Analysis (SSA). A knowledge graph is constructed to represent relationships among AI topics, projects, and companies, thereby enabling structured innovation tracking. The approach addresses challenges such as data sparsity and noise, demonstrating strengths in semantic annotation and topic categorization. Results highlight the potential for accurately detecting AI innovations and linking them to organizational entities, offering valuable insights for researchers, companies, and policymakers. This work contributes a scalable, automated approach for AI innovation tracking, with future directions focusing on refining entity linking and expanding the knowledge graph to capture emerging trends.

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