## 5 Conclusions and Future Work  
In recent years, one of the main challenges of political marketing discussed in the literature review is the access to and management of massive social data, whose difficulty is mainly associated to the heterogeneity of the data sources caused by the massive use of social networking sites and many other digital platforms. This fact produces late (and less informed) decision making, having to spend a large amount of time in the information management and extraction processes. Moreover, it also symbolizes ignorance of the electorate, human cost, and economic cost for political marketers. To contribute to the solution of the above-mentioned challenges, the research work described in this work presents an ontological model (with the main concepts involved in the political marketing domain) which sustains a KG envisioned to facilitate the integration and reconciliation of data coming from different sources. Then, a framework to automatically populate the KG with data gathered from social media sites and news sites is proposed. The ontological model was built by following the well-known Ontology Development 101 methodology taking into account the criteria and needs of political marketing in political campaign scenarios. Therefore, once populated, the ontology, which was baptized as PMont (Political Marketing Ontology), provides answers for each of the key questions raised, starting with the most important one: what does the electorate demand? PMont enabled the semantic storage and processing of available information about the electorate and the candidates through the possibility of consuming data from different data sources. In order to have a gateway to integrate new data to the ontological model in the form of a graph, an automated solution was created based on ML and NLP techniques to process Spanish texts enabling the possibility to (i) extract and collect meaningful data from semi-structured and unstructured digital media sources, (ii) deal with massive data, and, finally, (iii) populate the KG previously defined upon the ontological model of the political marketing domain. The proposed automation system foresees the following components for its optimal implementation: (i) source connection and data collection, (ii) information extraction module, (iii) ontology population and validation, and (iv) the resulting KG. The final result is a KG populated with validated, accurate, consistent, and reliable information. The KG was evaluated through 18 quality requirements on the dimensions of accessibility, contextual quality, intrinsic quality, and representational quality, which ensure the optimal integration of knowledge to be used as market intelligence for defining political campaign strategies. There are possibilities for further development and management of this resulting knowledge graph, starting with a more robust validation process. A large corpus of text from news sites and social media sites is to be gathered and manually annotated to check the performance of the proposed automatic population framework in terms of traditional information retrieval metrics such as precision, recall, and F1-measure. On the other hand, the application of machine and deep learning techniques can be leveraged for semantic analysis of information to infer new knowledge and make better decisions during political campaigns. Another important improvement that can be applied as future work is the elimination of the relational database layer prior to the ontology population stage; this will help to consolidate a real-time system. The end-user usefulness of the proposed system lays on the development and implementation of a visualization tool for the resulting political marketing KG and GUI (Graphical User Interface), a user interface for the system. These graphic elements will be important for defining a complete decision-making dashboard, benefiting the political campaign manager in rapid response to voter demand or crisis management.