1. A computer-implemented method for characterizing content of documents by conceptual relationships, comprising: applying natural language processing (NLP) to content in a plurality of documents to identify topics and subjects; applying analytic analysis to the topics and subjects to identify a conceptual relationships of the content in the plurality of documents; partitioning the content in each of the plurality of documents into a first structured hierarchy, preserving at least one structure in each document inherent in the each document; and providing access to content through a first index based upon utilizing the first structured hierarchy and through a second index utilizing a second structured hierarchy; and wherein the content is characterized by optimizing a vector space model representation of the documents, the optimization performed by a system capable of answering questions, where: the content from the plurality of documents is ingested by the system; natural language processing is applied to the content in the plurality of documents to identify terms, topics, subjects and concepts; the content is partitioned according to a semantic parse distance to identify a context for partitioned content; the content and context is represented, by the system, utilizing a vector space model; entries in the vector space model are eliminated based on a difference criteria; and an iterative genetic algorithm is applied to optimize features of the vector space model.