Misinformation and rumors have become rampant on online social platforms with adverse consequences for the real world. Fact-checking efforts are needed to mitigate the risks associated with the spread of digital misinformation. However, the pace at which information is generated online limits the capacity to fact-check claims at the same rate using current journalistic practices. Computational approaches may be a key for achieving scalable fact checking. To this end, this dissertation introduces network science and machine learning methods for fact checking by leveraging information in large knowledge bases, commonly known as knowledge graphs (KGs). We consider two variations of the fact-checking task. The first consists in assessing the truthfulness of a statement of fact as simple as a (subject, predicate, object) triple, where the subject entity is related to the object entity by the predicate relation. We show that a broad class of triples pertaining to generic relationships among entities in the real world, e.g., (Indianapolis, capitalOf, Indiana), can be checked effectively by finding a shortest path connecting their subject and object entities in the KG under appropriately designed semantic proximity metrics. We also extend this approach by considering multiple paths, following ideas from network flow theory. Evaluation on a range of facts related to entertainment, sports and more reveals that our methods are effective in discerning true statements from false ones, often outperforming state-of-the-art algorithms. The second task consists in computing a relevance score that expresses the degree to which a person is associated with different professions or nationalities. For example, we wish to determine which of Scientist, Philosopher or Writer best describes Aristotle. We introduce a supervised learning approach for assessing such relevance by extracting useful features from the KG. Results show that our approach is effective, despite the limited information in the graph. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]