@TechReport{InnoGraph-AI-Taxonomy,
  author       = {Vladimir Alexiev and Boyan Bechev and Alexandr Osytsin},
  title        = {The InnoGraph Artificial Intelligence Taxonomy: A Key to Unlocking AI-Related Entities and Content},
  institution  = {Ontotext Corp},
  year         = 2023,
  type         = {whitepaper},
  month        = dec,
  url          = {https://www.ontotext.com/knowledgehub/white_paper/the-innograph-artificial-intelligence-taxonomy/},
  keywords     = {InnoGraph, Artificial Intelligence, Topics, Taxonomy, InnoGraph},
  date         = {2023-12},
  url_Zenodo   = {https://zenodo.org/records/15113095},
  abstract     = {InnoGraph is a holistic Knowledge Graph of innovation based on Artificial Intelligence (AI). AI is the underpinning of much of the world's innovation, therefore it has immense economic and human improvement potential. With the explosive growth of Machine Learning (ML), Deep Learning (DL) and Large Language Models (LLM), it is hard to keep up with all AI development, but also this is a valuable effort. A key to discovering AI elements is to build a comprehensive taxonomy of topics: AI techniques, application areas (verticals). We describe our approach to developing such a taxonomy by integrating and coreferencing data from numerous sources. Potential InnoGraph Datasets and Users - Importance of Topics and A Holistic Approach - Example: Github Topics - Kinds of Topics. Core Topics: Wikipedia Articles: - Wikipedia Categories - Category Pruning. Collaborative Patent Classification: Application Areas: - PatBase Browser - CPC Semantic Data at EPO - Finding All CPC AI Topics - CPC Snowballing - CPC for Application Area Topics. Other Topic Datasets: - ACM CCS - AIDA FAT - AMiner KGs - ANZSRC FOR - arXiv Areas - China NSFC - EU CORDIS EuroSciVoc - Crunchbase Categories - CSO - JEL - MESH - MSC - OpenAlex Topics - SemanticScholar FOS - StackExchange Tags. Conclusion and Future Work: - Acknowledgements - References},
}

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AI is the underpinning of much of the world's innovation, therefore it has immense economic and human improvement potential. With the explosive growth of Machine Learning (ML), Deep Learning (DL) and Large Language Models (LLM), it is hard to keep up with all AI development, but also this is a valuable effort. A key to discovering AI elements is to build a comprehensive taxonomy of topics: AI techniques, application areas (verticals). We describe our approach to developing such a taxonomy by integrating and coreferencing data from numerous sources. Potential InnoGraph Datasets and Users - Importance of Topics and A Holistic Approach - Example: Github Topics - Kinds of Topics. Core Topics: Wikipedia Articles: - Wikipedia Categories - Category Pruning. Collaborative Patent Classification: Application Areas: - PatBase Browser - CPC Semantic Data at EPO - Finding All CPC AI Topics - CPC Snowballing - CPC for Application Area Topics. Other Topic Datasets: - ACM CCS - AIDA FAT - AMiner KGs - ANZSRC FOR - arXiv Areas - China NSFC - EU CORDIS EuroSciVoc - Crunchbase Categories - CSO - JEL - MESH - MSC - OpenAlex Topics - SemanticScholar FOS - StackExchange Tags. Conclusion and Future Work: - Acknowledgements - References","bibtex":"@TechReport{InnoGraph-AI-Taxonomy,\n author = {Vladimir Alexiev and Boyan Bechev and Alexandr Osytsin},\n title = {The InnoGraph Artificial Intelligence Taxonomy: A Key to Unlocking AI-Related Entities and Content},\n institution = {Ontotext Corp},\n year = 2023,\n type = {whitepaper},\n month = dec,\n url = {https://www.ontotext.com/knowledgehub/white_paper/the-innograph-artificial-intelligence-taxonomy/},\n keywords = {InnoGraph, Artificial Intelligence, Topics, Taxonomy, InnoGraph},\n date = {2023-12},\n url_Zenodo = {https://zenodo.org/records/15113095},\n abstract = {InnoGraph is a holistic Knowledge Graph of innovation based on Artificial Intelligence (AI). AI is the underpinning of much of the world's innovation, therefore it has immense economic and human improvement potential. With the explosive growth of Machine Learning (ML), Deep Learning (DL) and Large Language Models (LLM), it is hard to keep up with all AI development, but also this is a valuable effort. A key to discovering AI elements is to build a comprehensive taxonomy of topics: AI techniques, application areas (verticals). We describe our approach to developing such a taxonomy by integrating and coreferencing data from numerous sources. Potential InnoGraph Datasets and Users - Importance of Topics and A Holistic Approach - Example: Github Topics - Kinds of Topics. Core Topics: Wikipedia Articles: - Wikipedia Categories - Category Pruning. Collaborative Patent Classification: Application Areas: - PatBase Browser - CPC Semantic Data at EPO - Finding All CPC AI Topics - CPC Snowballing - CPC for Application Area Topics. 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