22. A system, the system comprising: one or more processors; and memory storing instructions that when executed by at least some of the processors effectuate operations comprising: obtaining a semantic similarity graph that comprises more than 500 nodes and more than 1000 weighted edges, each node representing a document of a corpus, and each edge weight indicating an amount of similarity between a pair of documents corresponding to the respective nodes connected by the respective edge; after obtaining the semantic similarity graph, obtaining a n-gram indicating a request that edge weights affected by the n-gram are to be increased or decreased; expanding the n-gram to produce a set of expansion n-grams, wherein expanding the n-gram comprises: determining which documents in at least part of the corpus contain the n-gram to form a first set of documents; determining which documents in at least part of the corpus do not contain the n-gram to form a second set of documents, the first set of documents and the second set of documents each including more than 20 documents; selecting a set of candidate n-grams from the first set of documents, the set of candidate n-grams having more than five n-grams; determining an amount of times each candidate n-gram occurs in the first set of documents to form a first amount; determining an amount of times each candidate n-gram occurs in the second set of documents to form a second amount; determining, for each of the candidate n-grams, a candidate n-gram score based on the first amount and the second amount, wherein the candidate n-gram scores tends to increase or decrease as a ratio of the first amount to the second amount increases or decreases; and selecting expansion n-grams based on the candidate n-gram scores, the expansion n-grams and n-gram collectively forming an adjustment n-gram set; adjusting edge weights of the semantic similarity graph of edges between pairs of documents in which members of the adjustment n-gram set co-occur in response to determining that the respective documents contain a member of the adjustment n-gram set wherein the expansion n-grams are inferred to be conceptually related to the obtained n-gram indicating the request, and wherein the expansion n-grams cause the adjustment of edge weights to be a more comprehensive response to the request than an adjustment based solely on the obtained n-gram indicating the request; and storing the adjusted weights in memory.
22. A system, the system comprising: one or more processors; and memory storing instructions that when executed by at least some of the processors effectuate operations comprising: obtaining a semantic similarity graph that comprises more than 500 nodes and more than 1000 weighted edges, each node representing a document of a corpus, and each edge weight indicating an amount of similarity between a pair of documents corresponding to the respective nodes connected by the respective edge; after obtaining the semantic similarity graph, obtaining a n-gram indicating a request that edge weights affected by the n-gram are to be increased or decreased; expanding the n-gram to produce a set of expansion n-grams, wherein expanding the n-gram comprises: determining which documents in at least part of the corpus contain the n-gram to form a first set of documents; determining which documents in at least part of the corpus do not contain the n-gram to form a second set of documents, the first set of documents and the second set of documents each including more than 20 documents; selecting a set of candidate n-grams from the first set of documents, the set of candidate n-grams having more than five n-grams; determining an amount of times each candidate n-gram occurs in the first set of documents to form a first amount; determining an amount of times each candidate n-gram occurs in the second set of documents to form a second amount; determining, for each of the candidate n-grams, a candidate n-gram score based on the first amount and the second amount, wherein the candidate n-gram scores tends to increase or decrease as a ratio of the first amount to the second amount increases or decreases; and selecting expansion n-grams based on the candidate n-gram scores, the expansion n-grams and n-gram collectively forming an adjustment n-gram set; adjusting edge weights of the semantic similarity graph of edges between pairs of documents in which members of the adjustment n-gram set co-occur in response to determining that the respective documents contain a member of the adjustment n-gram set wherein the expansion n-grams are inferred to be conceptually related to the obtained n-gram indicating the request, and wherein the expansion n-grams cause the adjustment of edge weights to be a more comprehensive response to the request than an adjustment based solely on the obtained n-gram indicating the request; and storing the adjusted weights in memory. 27. The system of claim 22 , wherein the obtained graph is a semantic similarity graph comprising vectors as nodes and edges with weights, the vectors being generated by: generating, for each document of the corpus, a list of all the unique document n-grams occurring in the document; counting, for each document of the corpus, the number of times each unique document n-gram occurs in the document and the number of times each unique document n-gram occurs in each of the other documents of the corpus to form a third count and a fourth count, respectively; calculating, for each document n-gram, a document n-gram relation score; removing from each list of each document of the corpus a portion of the lowest ranking document n-grams based on their respective document n-gram relation score; and determining, for each document of the corpus, a vector based on the document n-gram relation score of each n-gram of the respective list, wherein the weighted similarity comprises a similarity value that is based on angles between the vectors of two nodes and that is above a threshold.