This thesis is the result of a project whose objective has been to develop and deploy an autonomous fault diagnosis system of software-defined networking (SDN) through Big Data analytics tools and semantic web approaches such as Linked Data. To do so, a system that recollects and process SDN data, as well as a diagnosis orches- tration and visualization system both of the SDN data and the performed diagnoses. The use case where our system has been deployed is based on a simulated simulated SDN network environment and controlled by a SDN controller as Opendaylight. The developed prototype will collect data from this environment, it will perform a processing of that network data, in such way we can detect symptoms of the network based on defined parameters for the case study. Then, we will introduce symptoms in a diagnosis module by phases, the first of them, to generate a initial set of hypothesis when a symptom is detected and the second, to give a final conclusion collecting more information from the environment. Furthermore, the prototype has a visualization system based on web technologies such as Polymer or D3.js, that allows a network operator or a user a better network management and its diagnosis. As a result, we have a complete system be able to monitor and diagnose a telecommunications network based on SDN that can be interoperable with other applications or modules thanks to the semantic approach given.