Towards InnoGraph: A Knowledge Graph for AI Innovation

Researchers seeking to comprehend the state-of-the-art innovations in a particular field of study must examine recent patents and scientific articles in that domain. Innovation ecosystems consist of interconnected information about entities such as researchers, institutions, projects, products, and technologies. However, representing such information in a machine-readable format is challenging because concepts like "knowledge" are not easily represented. Nonetheless, even a partial representation of innovation ecosystems provides valuable insights. Therefore, representing innovation ecosystems as knowledge graphs (KGs) would enable advanced data analysis and generate new insights. To this end, we propose InnoGraph, a framework that integrates multiple heterogeneous data sources to build a Knowledge Graph of the worldwide AI innovation ecosystem.

[1]  Russell J. Funk,et al.  Papers and patents are becoming less disruptive over time , 2023, Nature.

[2]  Yu Yang,et al.  PKG: A Personal Knowledge Graph for Recommendation , 2022, SIGIR.

[3]  Heather A. Piwowar,et al.  OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts , 2022, ArXiv.

[4]  Alex D Wade The Semantic Scholar Academic Graph (S2AG) , 2022, WWW.

[5]  P. Rajpurkar,et al.  AI in health and medicine , 2022, Nature Medicine.

[6]  Helge Holzmann,et al.  Refcat: The Internet Archive Scholar Citation Graph , 2021, ArXiv.

[7]  A. Ashta,et al.  Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance , 2021 .

[8]  Doug Downey,et al.  SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts , 2021, AKBC.

[9]  Pantelis Koutroumpis,et al.  Digital instruments as invention machines , 2020, Commun. ACM.

[10]  Sandro La Bruzzo,et al.  OpenAIRE Research Graph Dump , 2020 .

[11]  Diego Reforgiato Recupero,et al.  AI-KG: An Automatically Generated Knowledge Graph of Artificial Intelligence , 2020, SEMWEB.

[12]  Jihu Wang,et al.  SoftKG: Building A Software Development Knowledge Graph through Wikipedia Taxonomy , 2020, 2020 IEEE World Congress on Services (SERVICES).

[13]  Jie Tang,et al.  Mapping the technology evolution path: a novel model for dynamic topic detection and tracking , 2020, Scientometrics.

[14]  J. Bhattacharya,et al.  NIH funding and the pursuit of edge science , 2020, Proceedings of the National Academy of Sciences.

[15]  Steffen Staab,et al.  Knowledge graphs , 2021, Commun. ACM.

[16]  Dominika Tkaczyk,et al.  Crossref: The sustainable source of community-owned scholarly metadata , 2020, Quantitative Science Studies.

[17]  Alberto Tejero,et al.  Knowledge Graphs for Innovation Ecosystems , 2020, ArXiv.

[18]  Michael Färber,et al.  The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data , 2019, SEMWEB.

[19]  Iz Beltagy,et al.  SciBERT: A Pretrained Language Model for Scientific Text , 2019, EMNLP.

[20]  Ahmed Elgammal,et al.  Art, Creativity, and the Potential of Artificial Intelligence , 2019, Arts.

[21]  Sören Auer,et al.  Open Research Knowledge Graph: Next Generation Infrastructure for Semantic Scholarly Knowledge , 2019, K-CAP.

[22]  Saeed Asadi Bagloee,et al.  Applications of Artificial Intelligence in Transport: An Overview , 2022 .

[23]  Katy Börner,et al.  Skill discrepancies between research, education, and jobs reveal the critical need to supply soft skills for the data economy , 2018, Proceedings of the National Academy of Sciences.

[24]  J. Füller,et al.  Innovation Analytics: Leveraging Artificial Intelligence in the Innovation Process , 2018, Business Horizons.

[25]  Oren Etzioni,et al.  Incorporating Ethics into Artificial Intelligence , 2017, The Journal of Ethics.

[26]  Samir Guglani Knowledge , 2016, The Lancet.

[27]  Péter Jacsó,et al.  Academic Search Engines: A Quantitative Outlook , 2015, Online Inf. Rev..

[28]  Marko Grobelnik,et al.  Event registry: learning about world events from news , 2014, WWW.

[29]  Georgios Gousios,et al.  GHTorrent: Github's data from a firehose , 2012, 2012 9th IEEE Working Conference on Mining Software Repositories (MSR).

[30]  Michael Ley,et al.  DBLP - Some Lessons Learned , 2009, Proc. VLDB Endow..

[31]  Jie Tang,et al.  ArnetMiner: extraction and mining of academic social networks , 2008, KDD.

[32]  Jie Tang,et al.  Social Network Extraction of Academic Researchers , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[33]  Michael Ley,et al.  The DBLP Computer Science Bibliography: Evolution, Research Issues, Perspectives , 2002, SPIRE.

[34]  Andrey Tagarev,et al.  Domain-Specific Modeling: A Food and Drink Gazetteer , 2017, Trans. Comput. Collect. Intell..

[35]  José Luis Ortega,et al.  Microsoft Academic Search: the multi-object engine , 2014 .

[36]  PATENTS AND INNOVATION : TRENDS AND POLICY CHALLENGES ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT , 2004 .