Understanding Horizon 2020 Data: A Knowledge Graph-Based Approach

Abstract

This paper aims to meaningfully analyse the Horizon 2020 data existing in the CORDIS repository of EU, and accordingly offer evidence and insights to aid organizations in the formulation of consortia that will prepare and submit winning research proposals to forthcoming calls. The analysis is performed on aggregated data concerning 32,090 funded projects, 34,295 organizations participated in them, and 87,067 public deliverables produced. The modelling of data is performed through a knowledge graph-based approach, aiming to semantically capture existing relationships and reveal hidden information. The main contribution of this work lies in the proper utilization and orchestration of keyphrase extraction and named entity recognition models, together with meaningful graph analytics on top of an efficient graph database. The proposed approach enables users to ask complex questions about the interconnection of various entities related to previously funded research projects. A set of representative queries demonstrating our data representation and analysis approach are given at the end of the paper.

Y-Rank: A Multi-Feature-Based Keyphrase Extraction Method for Short Text

“…Combined with graph theory, the semantic model [16][17][18][19][20][21][22][23][24][25] selected rich sources of semantic features, including semantic features from different knowledge graphs or prior knowledge [16][17][18][19][20][21], and semantic features embedded from different pre-trained wordembedding models [22][23][24][25]. However, these graph construction methods lack diversity.…”

“…Calculate and update the statistical feature scores of the candidates. Y-Rank traverses the set of candidates and calculates and updates the corresponding keyphrase statistical feature score by summing the statistical feature score of non-stop words in the candidates (lines [18][19][20][21][22].…”

Horizon 2020 Project Analysis by Using Topic Modelling Techniques in the Field of Transport

“…In other publications various text mining techniques are used to identify some main research topics. For example, Giarelis and Karacapilidis (2021) analyze the Horizon 2020 framework by processing around 30,000 projects and 80,000 publications related to the projects. The authors use text mining and a graphbased approach to identify hidden information and to discover the latent links between the documents.…”

“…The Horizon 2020 framework, the largest EU research and innovation initiative, is crucial in advancing science, promoting international cooperation, and solving relevant societal concerns. The framework aimed to encourage the realization of sustainable and innovative research projects from 2014 to 2020 contributing to the economy and society (Giarelis and Karacapilidis, 2021). Moreover, the Cordis portal is a huge database of the Horizon 2020 projects of various fields, and this vast amount of information provides the opportunity to retrieve valuable insights into specific scientific fields (Giarelis and Karacapilidis, 2021).…”

“…The framework aimed to encourage the realization of sustainable and innovative research projects from 2014 to 2020 contributing to the economy and society (Giarelis and Karacapilidis, 2021). Moreover, the Cordis portal is a huge database of the Horizon 2020 projects of various fields, and this vast amount of information provides the opportunity to retrieve valuable insights into specific scientific fields (Giarelis and Karacapilidis, 2021). While many scientists participated in the projects, few of them tried to identify potential development approaches that help to create an impact through the analysis of the project data.…”