Abstract: Techniques for extracting and ranking multiple topic descriptions based on source contexts and subsequently selecting individual topic descriptions to surface based on recipient contexts. More specifically, a mining platform may extract, from a set of source documents making up a corpus, topic descriptions for various topics that are relevant to an enterprise. The mining platform may further rank the extracted topic descriptions based on a source context of those documents from which individual topic descriptions are extracted. Subsequently, when users access enterprise documents including term-usage instances of topics for which one or more topic descriptions have been extracted and ranked, a description serving module may select a topic description that is contextually appropriate for a recipient view the enterprise documents.

Abstract: An entity interlinkage platform may receive a corpus of enterprise documents and user profile data that corresponds to an individual user. The entity interlinkage platform utilizes topic extraction models to extract entities from the corpus and further utilizes the user profile data to generate a knowledge graph that includes interlinkages between the extracted entities. The entity interlinkage platform may identify a multitude of topic descriptions and corresponding topic terms from source documents that an access control list permits an individual user to access. Then, based on the user profile data, the entity interlinkage platform may generate a knowledge graph that is tailored for the individual user's specific purposes within the enterprise. For example, the knowledge graph may be generated based on knowledge graph preferences (which may be defined by the individual user) indicating preferred topic description types and/or levels of detail in association with specific topics and/or categories of topics.

Abstract: Techniques for extracting and ranking multiple topic descriptions based on source contexts and subsequently selecting individual topic descriptions to surface based on recipient contexts. More specifically, a mining platform may extract, from a set of source documents making up a corpus, topic descriptions for various topics that are relevant to an enterprise. The mining platform may further rank the extracted topic descriptions based on a source context of those documents from which individual topic descriptions are extracted. Subsequently, when users access enterprise documents including term-usage instances of topics for which one or more topic descriptions have been extracted and ranked, a description serving module may select a topic description that is contextually appropriate for a recipient view the enterprise documents.

Abstract: Techniques for extracting and ranking multiple topic descriptions based on source contexts and subsequently selecting individual topic descriptions to surface based on recipient contexts. More specifically, a mining platform may extract, from a set of source documents making up a corpus, topic descriptions for various topics that are relevant to an enterprise. The mining platform may further rank the extracted topic descriptions based on a source context of those documents from which individual topic descriptions are extracted. Subsequently, when users access enterprise documents including term-usage instances of topics for which one or more topic descriptions have been extracted and ranked, a description serving module may select a topic description that is contextually appropriate for a recipient view the enterprise documents.

Abstract: Systems and methods are provided for detecting a driving action that mitigates risk.

Abstract: Techniques for extracting and ranking multiple topic descriptions based on source contexts and subsequently selecting individual topic descriptions to surface based on recipient contexts. More specifically, a mining platform may extract, from a set of source documents making up a corpus, topic descriptions for various topics that are relevant to an enterprise. The mining platform may further rank the extracted topic descriptions based on a source context of those documents from which individual topic descriptions are extracted. Subsequently, when users access enterprise documents including term-usage instances of topics for which one or more topic descriptions have been extracted and ranked, a description serving module may select a topic description that is contextually appropriate for a recipient view the enterprise documents.

Abstract: An entity interlinkage platform may receive a corpus of enterprise documents and user profile data that corresponds to an individual user. The entity interlinkage platform utilizes topic extraction models to extract entities from the corpus and further utilizes the user profile data to generate a knowledge graph that includes interlinkages between the extracted entities. The entity interlinkage platform may identify a multitude of topic descriptions and corresponding topic terms from source documents that an access control list permits an individual user to access. Then, based on the user profile data, the entity interlinkage platform may generate a knowledge graph that is tailored for the individual user's specific purposes within the enterprise. For example, the knowledge graph may be generated based on knowledge graph preferences (which may be defined by the individual user) indicating preferred topic description types and/or levels of detail in association with specific topics and/or categories of topics.

Abstract: Systems and methods are provided for detecting a driving action that mitigates risk.

Abstract: Systems and methods are provided for detecting a driving action that mitigates risk.

Abstract: Systems and methods are provided for detecting a driving action that mitigates risk.

Abstract: Systems and methods are provided for detecting a driving action that mitigates risk.