Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for structuring data in a knowledge graph. A data management system determines known concepts that are related to a data snippet. The data management system determines cosine similarity values indicating an intrinsic similarity between the data snippet and the known concepts, as well as pertinence values indicating a measure of topical similarity between the data snippet and the known concepts. The data management system determines, based on the cosine similarity values and the pertinence values, that the data snippet is related to a first known concept, and in response, assigns a concept identifier for the first known concept to the data snippet. Score indicating a strength of connection between the concepts added to the knowledge graph are determined and used to derive insights between the concepts.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for structuring data in a knowledge graph. A data management system determines known concepts that are related to a data snippet. The data management system determines cosine similarity values indicating an intrinsic similarity between the data snippet and the known concepts, as well as pertinence values indicating a measure of topical similarity between the data snippet and the known concepts. The data management system determines, based on the cosine similarity values and the pertinence values, that the data snippet is related to a first known concept, and in response, assigns a concept identifier for the first known concept to the data snippet. Score indicating a strength of connection between the concepts added to the knowledge graph are determined and used to derive insights between the concepts.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for structuring data in a knowledge graph. A data management system determines known concepts that are related to a data snippet. The data management system determines cosine similarity values indicating an intrinsic similarity between the data snippet and the known concepts, as well as pertinence values indicating a measure of topical similarity between the data snippet and the known concepts. The data management system determines, based on the cosine similarity values and the pertinence values, that the data snippet is related to a first known concept, and in response, assigns a concept identifier for the first known concept to the data snippet. Score indicating a strength of connection between the concepts added to the knowledge graph are determined and used to derive insights between the concepts.

Abstract: A machine-implemented method of relaying information nodes in an information network, comprising the steps of: processing a plurality of data objects according to a predefined dictionary containing a plurality of information units and a plurality of correlation-indicating elements to defect in the plurality of data objects the presence of a correlation between respective information units; establishing an information network with a plurality of information nodes and links between the information nodes, said information nodes being related to said information units and said links being related to said detected correlations; and analyzing a link connectivity state of said information network to find a path across information nodes that represent an inference or a set of inferences being input by a query searched by a user.

Abstract: A machine-implemented method of relating information nodes in an information network, comprising the steps of: processing a plurality of data objects according to a predefined dictionary containing a plurality of information units and a plurality of correlation-indicating elements to defect in the plurality of data objects the presence of a correlation between respective information units; establishing an information network with a plurality of information nodes and links between the information nodes, said information nodes being related to said information units and said links being related to said detected correlations; and analyzing a link connectivity state of said information network to find a path across information nodes that represent an inference or a set of inferences being input by a query searched by a user.

Abstract: A machine-implemented method of relaying information nodes in an information network, comprising the steps of: processing a plurality of data objects according to a predefined dictionary containing a plurality of information units and a plurality of correlation-indicating elements to defect in the plurality of data objects the presence of a correlation between respective information units; establishing an information network with a plurality of information nodes and links between the information nodes, said information nodes being related to said information units and said links being related to said detected correlations; and analyzing a link connectivity state of said information network to find a path across information nodes that represent an inference or a set of inferences being input by a query searched by a user.