Abstract: Provided is a process including: sampling of nodes of a first graph; and forming a second graph comprising nodes, each of which corresponds to at least one entity mentioned in unstructured text documents. Forming the second graph comprises, for each pair of the nodes of the second graph, setting a relationship between the pair, the pair comprising (i) a first node corresponding to a first entity and (ii) a second node corresponding to a second entity, by: identifying, in the first graph, a node corresponding to an unstructured text document in which the first entity is mentioned; and setting the relationship between the pair based on a determination of whether or which of the sampled nodes comprises a node satisfying a set of criteria, the criteria comprising (i) having a relationship with the identified node and (ii) corresponding to an unstructured text document in which the second entity is mentioned.

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 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 including obtaining a corpus having a plurality of time-stamped documents; detecting topics in the unstructured natural language text; determining temporal-metrics of the topics based on time-stamps of documents to which the topics pertain; and predicting based on the temporal-metrics, whether a given topic among the plurality of topics will appear in future time-stamped documents.

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 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 of adjusting a visualization of a graph in response to user interactions with the visualization, the process including: obtaining a graph; causing a visualization of the graph to be presented on one or more displays having a display area; receiving a request for a lens be applied to the visualization; selecting a first portion of the graph based on the first portion being presented within the region specified by the lens; and transforming the first portion of the graph.

Abstract: Provided is a process including: obtaining a clustered graph, the clustered graph having three or more clusters, each cluster having a plurality of nodes of the graph, the nodes being connected in pairs by one or more respective edges; determining visual attributes of cluster icons based on amounts of nodes in clusters corresponding to the respective cluster icons; determining positions of the cluster icons in a graphical visualization of the clustered graph; obtaining, for each cluster, a respective subset of nodes in the respective cluster; determining visual attributes of node icons based on attributes of corresponding nodes in the subsets of nodes, each node icon representing one of the nodes in the respective subset of nodes; determining positions of the node icons in the graphical visualization based on the positions of the corresponding cluster icons of clusters having the nodes corresponding to the respective node icons; and causing the graphical visualization to be displayed.

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 graph to be visually represented in a graphical user interface of a client computing device; obtaining a set of text labels each corresponding to a respective collection of the nodes; determining a two dimensional or higher layout of the icons in the visual representation; segmenting the field of view into a plurality of segments; determining which icons are disposed within each of the segments of the field of view; determining positions of the text labels in the visual representation relative to one or more icons representing nodes in the respective collection of nodes based on the segment of the field of view in which the one or more icons representing nodes in the respective collection are disposed; and causing the visual representation to be displayed.

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.

Abstract: A system and method for measuring a performance of an entity and for predicting its future performance is disclosed. Raw information about the entity is collected from internal and external sources. The information is cleaned to exclude false positives. Using performance categories, the information is organized and transformed into meaningful data for the performance analysis engine. The information is normalized by scaling the meaningful data by industry type. A performance score is calculated by the performance analysis engine based on the normalized data. Further, a competitive relationship score is calculated based on the performance score and the normalized data. These scores are reported in a user interface displaying the performance of the entity and are used as inputs, among other factors, to a predictive analysis engine that assesses the future performance of the entities.

Abstract: A system and method for measuring a performance of an entity and for predicting its future performance is disclosed. Raw information about the entity is collected from internal and external sources. The information is cleaned to exclude false positives. Using performance categories, the information is organized and transformed into meaningful data for the performance analysis engine. The information is normalized by scaling the meaningful data by industry type. A performance score is calculated by the performance analysis engine based on the normalized data. Further, a competitive relationship score is calculated based on the performance score and the normalized data. These scores are reported in a user interface displaying the performance of the entity and are used as inputs, among other factors, to a predictive analysis engine that assesses the future performance of the entities.