When applying clustering to the capability matrix of the entire database, the natural relationships between every term can be observed. As described, the most related terms appear together in this clustering. For example, terms related to sugar, straw, waster or wood, show a high relationship in terms of usage. This serves as a validation and proof that in fact, the clustering makes sense. It is interesting to discuss two different phenomena that resulted from this analysis. First, most terms are clustered because of their scientific similarity, this happens particularly to feedstock terms. Secondly, some other terms such as outputs, are clustered in a not so linear form. For example, biogas, ethanol, and other types of fuels are also clustered. This second clustering can be due to a variety of factors, one possibility is the fact that they are researched using the same feedstocks or processing technologies. Another possibility is the fact that they are clustered for being intensely researched, and the “the goal” of most technological assets.