Knowledge Graphs for Innovation Ecosystems
This work addresses the problem of modeling complex, confidential innovation data for researchers and institutions, but it is incremental as it builds on existing knowledge graph methods.
The paper tackles the challenge of representing innovation ecosystems as knowledge graphs by proposing an ontology and data sources, and demonstrates its application at Universidad Politecnica de Madrid to enable advanced data analysis and insights.
Innovation ecosystems can be naturally described as a collection of networked entities, such as experts, institutions, projects, technologies and products. Representing in a machine-readable form these entities and their relations is not entirely attainable, due to the existence of abstract concepts such as knowledge and due to the confidential, non-public nature of this information, but even its partial depiction is of strong interest. The representation of innovation ecosystems incarnated as knowledge graphs would enable the generation of reports with new insights, the execution of advanced data analysis tasks. An ontology to capture the essential entities and relations is presented, as well as the description of data sources, which can be used to populate innovation knowledge graphs. Finally, the application case of the Universidad Politecnica de Madrid is presented, as well as an insight of future applications.