1. A method, comprising: creating a conceptual inverted index (CII) based on conceptual indices (CIs), the CII including CII entries, each of which corresponds to a separate concept in a concept graph, the creating including for each CII entry: with respect to the concept corresponding to the CII entry, populating the CII entry with: pointers to documents selected from the CIs having likelihoods of being related to the concept that are greater than a threshold value; and the corresponding likelihoods of the documents; receiving a query that includes one of the concepts in the concept graph as a search term; searching the CII for the search term; and generating query results from the searching, the query results including at least a subset of the pointers to documents; creating an explanations index that indicates for each of the documents pointed to by the pointers included in the query results, a likelihood that each of one or more concepts extracted from the document is related to the search term; and generating an explanation of the query results based on the explanations index, the explanation including a summary of a relevance of the query results to the query, wherein each of the CIs is associated with a corresponding one of the documents and includes a CI entry for each concept in the concept graph, and each of the CI entries specifies a value indicating a likelihood that the one of the documents is related to the concept in the concept graph.
1. A method, comprising: creating a conceptual inverted index (CII) based on conceptual indices (CIs), the CII including CII entries, each of which corresponds to a separate concept in a concept graph, the creating including for each CII entry: with respect to the concept corresponding to the CII entry, populating the CII entry with: pointers to documents selected from the CIs having likelihoods of being related to the concept that are greater than a threshold value; and the corresponding likelihoods of the documents; receiving a query that includes one of the concepts in the concept graph as a search term; searching the CII for the search term; and generating query results from the searching, the query results including at least a subset of the pointers to documents; creating an explanations index that indicates for each of the documents pointed to by the pointers included in the query results, a likelihood that each of one or more concepts extracted from the document is related to the search term; and generating an explanation of the query results based on the explanations index, the explanation including a summary of a relevance of the query results to the query, wherein each of the CIs is associated with a corresponding one of the documents and includes a CI entry for each concept in the concept graph, and each of the CI entries specifies a value indicating a likelihood that the one of the documents is related to the concept in the concept graph. 5. The method of claim 1 , wherein the query results only include pointers to documents having likelihoods greater than a second threshold of being related to the concept included in the search term.