# Understanding Horizon 2020 Data: A Knowledge Graph-Based Approach
## 2 Background
### Analyzing CORDIS and Other Textual Datasets  
We have identified a few recent works in the literature that adopt alternative approaches to analyze the CORDIS dataset. In the work described in [19], authors apply graph social networks analysis using various measures to discover insights about countries/organizations that have acquired funding from the European Commission for projects related to green energy and sustainable technologies. Although their approach showcases interesting graph measures and results, it does not utilize any NLP techniques to retrieve information about the topics of these projects, as is the case in the approach described in this paper; this information would, for instance, provide insights about latest research trends and potential collaborators. By also using CORDIS data, the work presented in [20] builds on a known statistical keyphrase extraction algorithm for candidate keyphrases, along with sentence similarity word embeddings, to provide a list of insights and challenges upon the user input. However, this approach does not allow one to find similar organizations or deliverables or persons to collaborate by considering a list of topics specified by the user. On the contrary, the approach proposed in this paper builds on a semantically rich knowledge graph, which meaningfully models all the connections between data, and enables users to pose such questions. The Corpus Viewer System [21] adopts an approach that is very similar to the work described in this paper; it utilizes various NLP practices to construct a knowledge graph of projects funded by the European Commission and their associated topics. Nonetheless, there are some major differences: (i) their approach relies on topic modelling, where each topic consists of a single term, whereas our approach utilizes keyphrase extraction to capture more context through extracting keyphrases consisting of multiple terms; (ii) their approach is not fully unsupervised, since it relies on pre-supplied taxonomies as training data, in order to perform text classification among the documents found in the dataset; (iii) apart from graph visualizations, their tool offers geographical visualizations for some pre-defined questions, which rely on aggregating statistical results from the CORDIS dataset. The work described in [22] proposes a document summarization and visualization framework based on both statistical and semantic analysis of textual and visual contents, aiming to highlight relevant terms in a document using some features (such as font size, color, etc.) showing the importance of a term compared to other ones. This framework builds on the combination of textual and visual features, which are stored in the Neo4j graph database, to improve the user knowledge acquisition by means of a synthesized visualization. This work demonstrates that with the help of semantic analysis and the combination of textual and visual features it is possible to improve the user knowledge acquisition by means of a synthesized visualization. However, compared to our approach, it does not build a knowledge graph to enable the user performing diverse types of queries and visualizing their results. OLGAVIS (On-Line Graph Analysis and Visualization for Bibliographic Information Network) [23] is an approach for visualizing connections in research bibliography. It builds a research collaboration knowledge graph where publications, venues, authors, and their affiliations are modelled using the Neo4j graph database. This approach also analyzes the data using various graph queries, and then forwards these results into the visualization layer. This layer allows users to ask questions about the data, and then filter or group or aggregate results. Compared to our approach, OLGAVIS provides more sophisticated visualization functionalities; however, it does not utilize any text mining method to extract topics related to the various entities of the knowledge graph.