Abstract: Provided is a process of refining Boolean queries, the process including: obtaining a query; searching a corpus of documents based on the query; selecting narrowing terms that pertain to respective subsets of the responsive documents; selecting broadening terms related to the query; instructing the user's computing device to present a graphical user interface comprising: graphical representations of the narrowing terms; graphical representations of the broadening terms; and one or more user inputs by which the user refines the query by adding a selected narrowing term or a selected broadening term; obtaining a user selection of a broadening term or a narrowing term; forming a refined query based on the user selection; searching at least part of the corpus based on the refined query to identify refined responsive documents; and instructing the user's computing device to present an updated graphical user interface with information about the refined responsive documents.

Abstract: Provided is a process, including: obtaining a clustered graph, wherein each of the nodes has a plurality of respective node attributes other than an identifier of the node; obtaining a designation of a given node attribute from among the plurality of node attributes; identifying a first subset of nodes of the graph as having anomalous values of the given node attribute by comparing values of the given node attribute in the first subset to a distribution of the given node attribute; identifying a second subset of nodes of the graph as having representative values of the given node attribute by comparing values of the given node attribute in the second subset to the distribution of the given node attribute; and sending instructions to a client device to display a representation of the graph.

Abstract: Provided is a process including: obtaining a corpus having a plurality of document collections, each of which is associated with features; for a given document collection, computing a pertinence score for each feature; ranking the features based on the features' pertinence scores; selecting a first set of features based on a first coverage score thereof and a threshold; re-ranking the first set of features based on the features' relevance to the document collection; and selecting a second set of features from the first set of features based on a second coverage score thereof and the threshold, the second set of features being used for summarizing the document collection.

Abstract: Provided is a process including: obtaining a semantic similarity graph having nodes corresponding to documents in an analyzed corpus and edges indicating semantic similarity between pairs of the documents; for at least a plurality of nodes in the graph, evaluating accuracy of the edges based on neighboring nodes and an external corpus by performing operations including: identifying the neighboring nodes based on adjacency to the respective node in the graph; selecting documents from an external corpus based on references in the selected documents to entities mentioned in the documents of the neighboring nodes; and determining how semantically similar the respective node is to the selected documents.

Abstract: Provided is a process of enhancing or suppressing measures of relationships between documents based on the relationships arising from text pertaining to selected topics, the process including: obtaining a corpus of documents; obtaining a set of topics by self-extracting topics according to the tokens present in text of the documents or manual provision; receiving a selected topic among the set of topics, the selection indicating that relationships between the documents are to be enhanced or suppressed in virtue of the relationships arising from text relating to the selected topic; forming a relationship graph of the documents, wherein: the relationships between pairs of the documents are determined based on co-occurrence of n-grams in the pairs of the documents, and wherein the relationships are enhanced or suppressed in response to co-occurring n-grams being in the respective set of n-grams of the selected topic.

Abstract: Provided is a process including: obtaining a first graph comprising nodes and edges, each of the first-graph edges linking two of the first-graph nodes and denoting semantic similarity of unstructured text in documents corresponding to the two linked first-graph nodes; for each of the first-graph nodes, selecting nodes for a second graph from attributes of the unstructured text documents to which the first-graph node corresponds, wherein the attributes are entities mentioned in the unstructured text documents, and wherein each of the second-graph nodes corresponds to a respective selected attribute; and for each pair of the second-graph nodes, determining a respective edge weight indicating similarity between a first entity corresponding to a first node of the respective pair and a second entity corresponding to a second node of the respective pair.

Abstract: Provided is a process of modifying semantic similarity graphs representative of pair-wise similarity between documents in a corpus, the method 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; obtaining an n-gram indicating 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; adjusting edge weights of edges between pairs of documents in which members of the expanded n-gram set co-occur.

Abstract: Provided is a process including: obtaining a graph comprising nodes and edges, each of the edges having a value indicating an amount of similarity between objects corresponding to the two linked nodes; selecting a parameter for influencing the graph; assessing each of the nodes based on the selected influencing parameter, wherein assessing comprises, with respect to each adjacent node in the graph sharing an edge with the node: determining the value indicating the amount of similarity between the object corresponding to the node and the object corresponding to the adjacent node; and determining a score related to the edge shared with the node, the score determined based on the similarity-amount value and a value of the selected influencing parameter for the node, such that edges are removed, weakened, added, or strengthened; and preparing, based on the graph, instructions to display at least part of the graph.

Abstract: Provided is a process of refining Boolean queries, the process including: obtaining a query; searching a corpus of documents based on the query; selecting narrowing terms that pertain to respective subsets of the responsive documents; selecting broadening terms related to the query; instructing the user's computing device to present a graphical user interface comprising: graphical representations of the narrowing terms; graphical representations of the broadening terms; and one or more user inputs by which the user refines the query by adding a selected narrowing term or a selected broadening term; obtaining a user selection of a broadening term or a narrowing term; forming a refined query based on the user selection; searching at least part of the corpus based on the refined query to identify refined responsive documents; and instructing the user's computing device to present an updated graphical user interface with information about the refined responsive documents.

Abstract: Provided is a process including: obtaining a semantic similarity graph having nodes corresponding to documents in an analyzed corpus and edges indicating semantic similarity between pairs of the documents; for at least a plurality of nodes in the graph, evaluating accuracy of the edges based on neighboring nodes and an external corpus by performing operations including: identifying the neighboring nodes based on adjacency to the respective node in the graph; selecting documents from an external corpus based on references in the selected documents to entities mentioned in the documents of the neighboring nodes; and determining how semantically similar the respective node is to the selected documents.