Knowledge Graphs (KGs) like Wikidata or DBpedia allow to store, process and visualize knowledge facts about real-world entities (nodes) and the interrelations between them (edges). These KGs are often constructed by incorporating knowledge from semi-structured heterogeneous data resources such as Wikipedia. They allow users to visualize consolidated knowledge from heterogeneous data resources such as personal websites and social media channels in a unified and clustered graph. In this work, we propose several contributions to the fields of Knowledge Graph construction and enrichment. The overarching contribution of this thesis is to introduce new methods to increase the coverage of certain entities in a KG. The four main technical contributions of this thesis are as follows: (1) We introduce a comparative study that compares the state-of-the-art profile matching methods on Online Social Network (OSNs), by analyzing the most recent research papers in this context. (2) We propose new techniques to refine specific entities such as academic entities (e.g, authors) and social event entities (e.g., festivals). For academic entities, we introduce new methods to identify their corresponding Online Social Network (OSN) links such as Facebook links. To address this objective, we investigate methods for matching OSN user profiles based on novel features, such as life events and biographies, that have not been leveraged by previous approaches. For social events, we introduce in addition a new approach to evaluate the overall public sentiment related to these events over time collected from OSNs. We evaluate the performance of our methods on several real-world datasets and show that they outperform the state of the art and produce high-quality results. (3) We introduce a number of measures to explore the user profile scope on multiple OSNs. Through these measures, we analyze three axes: (a) the user profile attributes, (b) user profile content, and (c) user social network. (4) We introduce a novel user profile matching method to interlink users across multiple OSNs, that leverages two fundamental matching features: (a) life events and (b) profile biographies. Life events (e.g., graduation) are used to improve the content matching process, and biographies (a short description that OSN users write about themselves) are used to improve the attribute matching process. In conclusion, we show how leveraging data from multiple OSNs is important to complete missing information of many entities inside KGs.