1. A method for conceptual analysis of a document, the method comprising: accessing, by a processor, a concept graph that includes a plurality of nodes and edges, each node representing a concept and each edge representing a known relation between two concepts; and computing, by the processor, a relevance of the document to concepts in the concept graph, the computing comprising: receiving a priori information about the document including concepts extracted from the document, probabilities of each of the concepts extracted from the document being located in a pool of concepts extracted from the document, and confidence scores corresponding to each of the concepts extracted from the document, wherein the concepts extracted from the document include a subset of the concepts in the concept graph; and combining the a priori information and the concept graph to generate a posteriori information that includes a reverse index that indicates a likelihood that the document is related to each of the concepts in the concept graph, the reverse index accessible to a computer system for determining a relevance of the document to a query received from an agent external to the computer system, the combining including calculating a relevance of the document to concepts in the graph not extracted from the document based on a degree of association in the concept graph between the concepts extracted from the document and the concepts in the graph not extracted from the document.
1. A method for conceptual analysis of a document, the method comprising: accessing, by a processor, a concept graph that includes a plurality of nodes and edges, each node representing a concept and each edge representing a known relation between two concepts; and computing, by the processor, a relevance of the document to concepts in the concept graph, the computing comprising: receiving a priori information about the document including concepts extracted from the document, probabilities of each of the concepts extracted from the document being located in a pool of concepts extracted from the document, and confidence scores corresponding to each of the concepts extracted from the document, wherein the concepts extracted from the document include a subset of the concepts in the concept graph; and combining the a priori information and the concept graph to generate a posteriori information that includes a reverse index that indicates a likelihood that the document is related to each of the concepts in the concept graph, the reverse index accessible to a computer system for determining a relevance of the document to a query received from an agent external to the computer system, the combining including calculating a relevance of the document to concepts in the graph not extracted from the document based on a degree of association in the concept graph between the concepts extracted from the document and the concepts in the graph not extracted from the document. 3. The method of claim 1 , further comprising, based on the combining, outputting a threshold number of concepts from the concept graph having the highest likelihood.