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AI topics Knowledge Graph

The main objective of this thesis is to build and maintain a platform for "Tracking AI Innovation and hype in companies".

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Keywords: AI topics, Data linking, Knowledge graphs

Achieving this requires building a semantic model for topics and constructing a framework that captures the underlying meaning and relationships between different AI-related subjects. This model facilitates effective topic classification and analysis within the given context.

The identification of AI topics is accomplished by leveraging various data sources such as Wikipedia, Wikidata, and the Cooperative Patent Classification (CPC) system. Additionally, the inclusion of information from Stack Overflow can be considered as an optional source for topic identification.

Extracting relevant information about the identified topics involves gathering pertinent details, characteristics, and attributes associated with each topic. This information contributes to a comprehensive understanding of the topics and supports further analysis and evaluation.

Linking the acquired knowledge and consolidating it within a unified Knowledge Graph (KG) is a crucial step. For instance, an effective approach would be linking patents to the Research Organization Registry (ROR), Wikipedia, and other pertinent sources, enabling the establishment of associations between patents and specific organizations or companies. This linking process can be facilitated by utilizing various tools, further enhancing the capabilities of knowledge integration and representation.

Work to be done:

  • Build a semantic model for AI topics.
  • Identifying the different AI topics.
  • Extract the data.
  • Link and analyze the data.