# Understanding Horizon 2020 Data: A Knowledge Graph-Based Approach
## 3 The Proposed Approach  
This section describes in detail the proposed approach towards representing and analyzing the Horizon 2020 data. The novelty of our work lies in the utilization and orchestration of keyphrase extraction and named entity recognition models, together with meaningful graph analytics on top of a highly performant graph database, to inform the process of preparation of research proposals for funding acquisition. The proposed approach enables users to ask complex questions about the organizations, projects, deliverables, topics, and people involved in already funded research projects, as well as about the way that all these entities are meaningfully interconnected. This is an advantage over previous approaches that merely analyze projects or organizations via a list of topics or via a statistical distribution of funds or projects over countries or research topics.  
As mentioned in prominent works (e.g., [25; 26; 27]), there are multiple reasons to opt for a graph database over a relational database (e.g., SQL). The two basic ones are that: (i) joins are expensive, and therefore, reasoning about a graph's paths becomes very costly; (ii) global properties are no easy to compute with classical queries based on logical methods [25]. In addition, graph databases allow our graph model to be stored natively. Other analytic tools such as Apache Spark store graph representations in a relational schema by converting the graph model into tables connected by multiple foreign keys. After this complex graph representation is built, these tools employ multiple JOIN statements, which lead to memory bottlenecks and poor performance. On the contrary, graph databases, and in our case Neo4j, provide a scalable solution that allows multiple hops across billions of nodes and edges with increased performance. Furthermore, thanks to the flexible schema nature of graph databases, we can store heterogeneous node types containing different types of data, thus unifying diverse data sources into a common model. Finally, graph databases permit us to easily adapt the model if and whenever needed, without the additional re-modelling required in traditional approaches.  
As illustrated in Figure 1, we propose a pipeline approach comprising four basic tasks, namely (i) data collection (Section 3.2), (ii) unsupervised keyphrase extraction and person extraction (Section 3.3), (iii) knowledge graph construction (Section 3.4), and (iv) graph data analysis and visualization (Section 3.5). It is noted here that our overall approach is presented in pseudocode form in Appendix A, Algorithm A1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approach** & **Graph** & **Knowledge** & **Text Mining** & **Word** & **Fully** & **Data** \\  & **Database** & **Graph** & **Method** & **Embeddings** & **Unsupervised** & **Visualization** \\ \hline
[19] & - & - & - & - & ✓ & ✓ \\
[20] & - & - & Keyphrase Extraction & ✓ & - & - \\
[21] & - & ✓ & Topic Modelling & - & - & ✓ \\
[22] & ✓ & - & Topic Modelling & - & - & ✓ \\
[23] & ✓ & ✓ & - & - & ✓ & ✓ \\
[24] & ✓ & ✓ & - & - & ✓ & ✓ \\ The proposed approach & ✓ & ✓ & Keyphrase Extraction & - & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of key characteristics of various approaches.