1. A computer-implemented method for performing a search engine query across one or more electronic databases in response to user input, the search engine query utilizing a knowledge graph constructed by extracting and relating multiple information nodes, the computer-implemented method comprising: receiving, from a user interface, a search query; accessing, from one or more electronic databases, a knowledge graph; generating, by an electronic processor, search engine query results based on the accessed knowledge graph, wherein the knowledge graph is constructed by: accessing, from the one or more electronic databases through an electronic communication network, a plurality of data objects, wherein the plurality of data objects are from a plurality of homogenous fields; accessing, from the one or more electronic databases, a plurality of pre-programmed information units, wherein said plurality of pre-programmed information units are configurable through a dictionary taxonomy to define a semantic field of research; processing, by the electronic processor, said plurality of data objects with syntactic and semantic analysis, based at least in part on said pre-programmed information units, to create a plurality of information units and a plurality of correlation-indicating elements, wherein said plurality of correlation-indicating elements contains at least values relating to strengths of the correlations between the plurality of information units; generating, by the electronic processor, a knowledge base based at least in part on the information units and the plurality of correlation-indicating elements, wherein said generating is performed with a computational linguistics algorithm, wherein said generating is performed automatically, wherein said generating is further performed without a predefined, manually curated ontology; representing said knowledge base graphically in a knowledge graph based at least in part on the plurality of information units and the plurality of correlation-indicating elements, wherein the knowledge graph depicts a plurality of information nodes and a plurality of links between the plurality of information nodes, said plurality of information nodes comprising said information units and said plurality of links based at least in part on the plurality of correlation-indicating elements; analyzing, by the electronic processor, the plurality of information nodes in the knowledge graph with a stochastic algorithm, wherein said plurality of information nodes are linked to one another either directly or indirectly through one or more links, the stochastic algorithm comprising: generating a non-symmetric vector comprising a plurality of probabilities, each probability proportional to a dependency between each pair of information nodes in the plurality of information nodes; and symmetrizing said non-symmetric vector by taking an average number of steps of two directions between each pair of information nodes in the plurality of information nodes; determining ranking measures between the plurality of information nodes, wherein each ranking measure represents a measure of similarity between each pair of information nodes and indicates indirect relations among the information units corresponding to each pair of information nodes, wherein said ranking measure is further based on the probability of selecting the average number of steps between each pair of information nodes as determined by the stochastic algorithm; and modifying lengths of the links in the knowledge graph based on the determined ranking measure, wherein the generated search engine query results comprise one or more correlations between two or more information nodes, wherein the one or more correlations are ordered based on the modified lengths of the links in the knowledge graph; and providing the ordered search engine query results to the user.