Wikipedia, the multilingual, free content encyclopedia has evolved as the largest and the most popular general reference work on the Internet. Since the time of commencement of Wikipedia, crowd sourcing of articles has been one of the most salient features of this open encyclopedia. It is obvious that enormous amount of work and expertise goes in the creation of a self-content article. However, it has been observed that the infobox type information in Wikipedia articles is often incomplete, incorrect and missing. This is due to the human intervention in creating Wikipedia articles. Moreover, the type of the infoboxes in Wikipedia plays a vital role in the determination of RDF type inference in the Knowledge Graphs such as DBpedia. Hence, there arouses a necessity to have the correct infobox type information in the Wikipedia articles. In this paper, we propose an approach of predicting Wikipedia infobox type information using both word and network embeddings. Furthermore, the impact of using minimalistic information such as Table of Contents and Named Entity mentions in the abstract of a Wikipedia article in the prediction process has been analyzed as well.