## 1 Introduction
In other words, while OL deals with T-BOX's conceptual model, OP focuses on adding new assertions to the A-BOX. Then, as the domain of discourse under consideration evolves so should the ontology; the task of extending and adapting an existing ontology with additional concepts, semantic relations, and rules is usually referred to as OE [16; 17]. OE encompasses the processes that involve the increase, maintenance, and scalability of the ontology from its initial structure [18]. OP identifies the instances, relationships, and properties of an ontology with discovered knowledge. Unstructured sources such as text documents are the dominant online knowledge resources that contain a large number of facts expressed either explicitly or implicitly [15]. A fast and low-cost OP process becomes extremely important for the development of knowledge systems [19]. In this work, we propose Integory, an automatic KG population framework to gather voter insights from both semi-structured and unstructured sources. The framework is built upon a previously defined ontology for the political marketing domain [20]. Semantic technologies were integrated to provide a formal representation of political marketing concepts such as candidate proposals, electorate opinion, and political news. The KG population process encompasses three main tasks, namely, (i) source connection and data collection, (ii) information extraction, and finally, (iii) population process and ontology validation. Natural Language Processing (NLP) and Machine Learning (ML) techniques were leveraged to process the gathered data and to fulfil the instantiation of the KG. All these components constitute a novel tool for the discovery and management of new knowledge within the highly competitive and dynamic politics domain. With all, the main contributions of this work are as follows:  
* We present the Political Marketing Ontology (PMont), a shareable conceptual model involving the most relevant elements in the political marketing domain, includingthree main operative knowledge items from political campaigns: candidate, electorate, and media. The ontology is an evolution of our previous work [20]. * Different unstructured, semi-structured, and structured data sources are exploited to populate the knowledge base by using a variety of information extraction techniques. New data sources can be added seamlessly. * Data enrichment and analysis in semantic and linguistic levels for Spanish texts through NLP and ML techniques contributed to the validation and classification of the opinions, proposals, and news media publications. * We propose an automatic KG population framework that homogenizes, collects, and relates the data of political marketing concepts with the possibility of inferring new knowledge for decision making. * As a result of the work, a validated and populated KG for political marketing purposes was obtained. The rest of the manuscript is organized as follows. In Section 2, background information about political marketing is provided along with a comprehensive analysis of KG enrichment through OP. The proposed KG population framework is described in detail in Section 3. Quality properties of the KG produced by the proposed framework are evaluated and discussed in Section 4. Finally, conclusions and future work are put forward in Section 5.