Almost every cellular process requires the interactions of pairs or larger complexes of proteins. The organization of genes into networks has played an important role in characterizing the functions of individual genes and the interplay between various cellular processes. The Gene Ontology (GO) project has integrated information from multiple data sources to annotate genes to specific biological process. Recently, the semantic similarity (SS) between GO terms has been investigated and used to derive semantic similarity between genes. Such semantic similarity provides us with a new perspective to predict protein functions and to generate functional gene networks. In this chapter, we focus on investigating the semantic similarity between genes and its applications. We have proposed a novel method to evaluate the support for PPI data based on gene ontology information. If the semantic similarity between genes is computed using gene ontology information and using Resniks formula, then our results show that we can model the PPI data as a mixture model predicated on the assumption that true protein-protein interactions will have higher support than the false positives in the data. Thus semantic similarity between genes serves as a metric of support for PPI data. Taking it one step further, new function prediction approaches are also being proposed with the help of the proposed metric of the support for the PPI data. These new function prediction approaches outperform their conventional counterparts. New evaluation methods are also proposed. In another application, we present a novel approach to automatically generate a functional network of yeast genes using Gene Ontology (GO) annotations. An semantic similarity (SS) is calculated between pairs of genes. This SS score is then used to predict linkages between genes, to generate a functional network. Functional networks predicted by SS and other methods are compared. The network predicted by SS scores outperforms those generated by other methods in the following aspects: automatic removal of a functional bias in network training reference sets, improved precision and recall across the network, and higher correlation between a genes lethality and centrality in the network. We illustrate that the resulting network can be applied to generate coherent function modules and their associations. We conclude that determination of semantic similarity between genes based upon GO information can be used to generate a functional network of yeast genes that is comparable or improved with respect to those that are directly based on integrated heterogeneous genomic and proteomic data.