# Understanding Horizon 2020 Data: A Knowledge Graph-Based Approach
## 1 Introduction  
Horizon 2020 (H2020) is a recently completed EU funding programme for research and innovation, which was running from 2014 to 2020 with a 680 billion budget ([[https://ec.europa.eu/programmes/horizon2020/](https://ec.europa.eu/programmes/horizon2020/)]([https://ec.europa.eu/programmes/horizon2020/](https://ec.europa.eu/programmes/horizon2020/)) (accessed on 27 November 2021)). Its main aim was to ensure research and innovation funding for multi-national collaboration projects as well as for individual researchers and SMEs. A wealth of data about funded H2020 projects can be retrieved through CORDIS (Community Research and Development Information Service, [[https://cordis.europa.eu](https://cordis.europa.eu)]([https://cordis.europa.eu](https://cordis.europa.eu)) (accessed on 27 November 2021)), the major public repository and portal of European Commission, which offers diverse information on EU-funded research projects and their results. Specifically, CORDIS provides valuable information about the projects' factsheets and results, public reports and deliverables, communication and exploitation material, as well as links to related external sources (such as websites and open access content). The problem of finding the most appropriate call to prepare and eventually submit a research proposal, as well as that of finding the most promising partners to collaborate with in such endeavors, while also taking into account the required complementarity of competences, certainly requires a large amount of manual inspection of both the aforementioned wealth of data and the hundreds of associated documents corresponding to call descriptions and deliverables already produced in the context of the H2020 programme. To properly address this issue, we perform a deep analysis of all these data and develop an approach that enables users to obtain answers and insights about the issues under consideration. Specifically, we represent, analyze, and understand the H2020 data existing in the CORDIS repository ([[https://data.europa.eu/data/datasets/cordish2020](https://data.europa.eu/data/datasets/cordish2020) projects]([https://data.europa.eu/data/datasets/cordish2020](https://data.europa.eu/data/datasets/cordish2020) projects) (accessed on 27 November 2021)), and accordingly build on robust Machine Learning (ML),Natural Language Processing (NLP) methods and tools, as well as a graph database, to perform state-of-the-art text mining and graph data analytics. The proposed methodology seamlessly integrates text mining techniques with graph theory to enable users to ask questions about the underlying data. A comparative assessment of the proposed methodology against existing ones, which is presented in detail in Section 2.4, reveals that it does not merely provide visual statistical aggregations of data, or keyphrases extracted from multiple documents; instead, it builds on prominent text mining techniques to retrieve information about the topics of H2020 projects, thus providing insights about latest research trends and potential collaborators, it constructs and exploits a semantically rich knowledge graph that meaningfully models all the connections between data and enables the users to perform diverse types of queries, it utilizes hybrid keyphrase extraction techniques to capture more context through extracting keyphrases consisting of multiple terms, and it is fully unsupervised. Motivated by the fact that the preparation of a winning research proposal is a complex and intense task, our aim was to extract meaningful new knowledge and insights from the H2020 related data, and accordingly provide the evidence that organizations need in this task.