TU Wien alumni and entrepreneur Linda Andersson is now the CEO of the start-up Artificial Researcher-IT GmbH. In 2018, together with her co-founders launched the product idea ‘Artificial Researcher in Science’, which received the Commercial Viability Award from the Austrian Angel Investors Association. Linda has 15 years of experience in the text mining industry and worked with different aspects associated with scientific literature text mining. Parallel to her academic studies, Linda has been working in the industry, designing different domain-specific patent text mining solutions. Her PhD Thesis, “The Essence of Patent Text Mining,” was a product of close collaboration with the text mining industry. For her PhD research she was awarded, in 2017, the most promising PhD research prize by the i²c Innovation Incubator Center, TU Wien.

Title: Open Data: An Important Source for Intellectual Property Awareness
"Artificial Researcher IT GmbH is a deep tech start-up that provides a holistic approach to the up-and-coming AI solutions required by industry as well as academia by offering a palette of services and software tools built on our Natural Language Processing expertise and domain know-how. We develop high-end tools that classify texts, populate ontologies, realize automatic term recognition, all of them contributing to different types of professional search applications. Besides the classic publication channels (Elsevier, Springer) that researchers use to present their scientific results, more and more institutions, and organizations (not least the European Commission and national funding agencies) recommend or even require that publicly funded research is disseminated Open Access (OA). These types of publications in combination with other technical documents, such as patents, are de facto less exploited within broader academic community when searching for and mapping existing knowledge. Considering that academic research is a source to many innovations, students and researchers must become aware of the worth of their technology (Intellectual Property awareness) in relation to already published knowledge, without restricting their search to classic publication channels. Open Data is relevant to us for several reasons: i) we use it to develop lower cost services prototypes, ii) allowing us to integrate user feedback and to offer fair price models to users providing their feedback iii) which brings Open Access data closer to IP industry stakeholders. We show-case, in two examples, how our technology can be applied to Open Access data. The first one realizes a semantic enrichment of the COVID-19 Open Research Dataset, creating a benchmark data set for further downstream knowledge analysis applications. The second example uses Open Access data to develop tools for data set identification in scientific articles, contributing towards assessing to which degrees these data sets satisfy the FAIR principles for data sharing. Even though Open (Access) data is free, we would like to emphasize that the cost obtaining, normalizing/processing the data and hosting the infrastructure are not. We are a company that builds a business model around O(A) Data by the development of new efficient text mining tools. The availability of Open Data shifts focus from paying for access to raw data to the use of services that help professionals extract knowledge."