# Understanding Horizon 2020 Data: A Knowledge Graph-Based Approach
## 4 Conclusions  
Adopting a knowledge graph-based approach, the work described in this paper builds on prominent ML and NLP tools and methods to analyse the H2020 dataset appearing in the CORDIS repository. As described in detail in the previous section, 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. Figure 6: Visualization of a specific person and some of the associated projects, deliverables, and keyphrases. We argue that the proposed approach can reveal hidden knowledge, trigger insights, and accordingly extract evidence to aid organisations in the highly complex and knowledge-intense task of the formulation of future research project proposals and related consortia. As elaborated in detail in [25], the proposed approach builds on the dual character of graphs, i.e., on one hand, providing a simple, flexible, and extensible data structure, and on the other, being one of the most deep-rooted form of representing human knowledge. In addition, the proposed approach contributes to an important line of research that has to do with the interplay of graph analytics and machine learning, or computing statistics over graphs [26]. Nowadays, the proposed approach can be very useful for organisations that are getting prepared to submit their proposals to the recently launched calls of the Horizon Europe funding programme. It has already undergone a preliminary assessment and validation round, with the aid of five experienced practitioners in project proposals' building (from three different European organisations). The feedback collected was highly positive in terms of usefulness and ease-of-use. In any case, a future work direction concerns a carefully planned assessment, also aiming to capture additional queries of major importance. A second work direction concerns the elaboration of a deep learning approach, which would maximize the accuracy of the proposed approach's NER subtask and require less amount of post-processing effort by human experts. In this direction, we plan to assess and eventually integrate in our approach word embeddings-based clustering techniques that incorporate the textual content with latent feature vector representations of words appearing in the text to improve the quality of topic detection [30]. Aiming to augment the visualization of the generated results, a third work direction concerns the development of alternative views, such as the hierarchical tree-based view elaborated in [31] and the combined table-tree view of aggregated graph features described in [32]. Finally, a fourth work direction concerns the potential incorporation of approaches that aim to decrease the number of necessary queries and consequently lower the search time in the context under consideration [33]. Conceptualization, N.G. and N.K. ; methodology, N.G. and N.K. ; software, N.G. ; investigation, N.G. ; resources, N.K. ; data curation, N.G. ; writing--original draft preparation, N.G. ; writing--review and editing, N.G. and N.K. ; visualization, N.G. ; supervision, N.K. ; project administration, N.K. ; funding acquisition, N.K. All authors have read and agreed to the published version of the manuscript. The work presented in this paper is supported by the inPOINT project ([[https://inpoint-project.eu/](https://inpoint-project.eu/)]([https://inpoint-project.eu/](https://inpoint-project.eu/)) (accessed on 27 November 2021)), which is co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH--CREATE--INNOVATE (Project id: T2 EDK-04389). Not applicable.