Smirnova, Alisa Audiffren, Julien Cudré-Mauroux, Philippe 2018 In this chapter, we discuss approaches leveraging distant supervision for relation extraction. We start by introducing the key ideas behind distant supervision as well as their main shortcomings. We then discuss approaches that improve over the basic method, including approaches based on the at-least-one-principle along with their extensions for handling false negative labels, and approaches leveraging topic models. We also describe embeddings-based methods including methods leveraging convolutional neural networks. Finally, we discuss how to take advantage of auxiliary information to improve relation extraction. application/pdf https://folia.unifr.ch/global/documents/307723 https://folia.unifr.ch/documents/307723/files/cud_dsk.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-63962-8_285-1 info:eu-repo/semantics/openAccess License undefined Encyclopedia of Big Data Technologies. - 2018, p. 1–7 info:eu-repo/classification/udc/004 Distant supervision from knowledge graphs http://purl.org/coar/resource_type/c_3248