IP LodB capitalizes on LOD database thinking, to build bridges between patented information and scientific knowledge whilst focusing on scientists who codify new knowledge and connected organizations, including those who apply patents in new products and services.

IP LodB will produce intellectual property rights (IPR) linked open data (LOD) map (IPR LOD Map), and test the linkability of the European patent (EP) LOD database by increasing the uniqueness of data using different harmonisation techniques and constructing the IP LodB database. To achieve this, the project multidisciplinary team combines competences in IPR protection, network theory and data analysis, data science programming, and more conventional social science research design and data analysis.

The project aims to: 1) show the linkability potential between the European Patent Office (EPO) LOD database and other LOD databases, where EP LOD represents the nucleus (hub) of IPR LOD databases; 2) compare the extent of connections between entries in the EP LOD database with the Springer LOD database, specifically: a) by testing linking potential of URI’s of inventors in EP LOD database with authors’ names in Springer LOD database and b) by linking URI’s of applicants with URI’s with company (affiliation) names in Springer SciGraph LOD database.

The project’s goal is to integrate patented information with other scientometric and micro econometric indicators, to increase the user friendliness of EP LOD data. In so doing, we 1) reduce the gap between IPR savvy companies and less-savvy (smaller) IPR entities who (will) use IPR LOD data; 2) provide the basis for better IPR management decisions as we enrich the patent data; 3) provide improved solutions for LOD data publishers and researchers; and 4) provide additional context to patent data and thus increase the role of patents in the European economy.

To achieve this the project is targeting a wide audience, also with diligently designed dissemination channels, and its dedication to open access solutions.

Our scientific contribution is to offer a methodological solution for disambiguation problems for entities involved in the innovation process, e.g., patent offices, public policy makers, scientists, research institutions, innovative firms, and high-tech entrepreneurs. According to LOD principles, these entities should have unambiguous identifiers and a trail of connections between entities over time, and in the future this will probably be the case. However, starting from where we are now, we propose to fill the existing knowledge gap with an ingenious application of social network analysis (SNA) theory and methods.