In this paper I describe a complex network analysis of Wikipedia. I conduct two experiments. In the first 10 different networks are constructed from Wikipedia entries related to the chosen domain. The goal of the first experiment is to extract domain knowledge in terms of identifying entries that are centrally positioned and entries that are strongly related. I apply complex networks analysis on all acquired networks and examine the networks’ structure. Additionally, centrality measures are employed in order to find centrally positioned entries in the network. And finally, I identify communities and find which entries are densely connected according to the network structure. In the second experiment I construct two different types of networks – superset and subset networks in which nodes are connected amongst themselves on the basis of keyword similarities between the texts of the respective entries representing nodes. These networks are then compared with the original network, of the type used in the first experiment, and through this process centrality measures are reviewed according to their ability to extract concepts semantically related to the text. I also present and analyze the extracted keywords used in the experiment.