Abstract: A journal of operations persistently stores a collection of user and machine interactions with documents, concepts within documents, and relationships/mentions among such documents and concepts. A knowledge graph or other visual expression can be used to provide an interactive format for user interactions with this information, and to visually display the accumulated information in the journal of operations on user's device of choice. Such accumulations are often called aggregations or aggregates. Both human users and computer users such as automated recommendation engines, machine learning algorithms, and the like can interact with the journal of operations to facilitate computer-assisted discovery and organization of data around a topic of interest to the human user. The resulting journal of operations and aggregations of those operations such as a knowledge graph or other visual expression(s) also provides a useful medium for sharing knowledge with other users and collaborating on investigation of a topic.

Abstract: A user interface of an entity-centric knowledge discovery system presents an entity profile including a mix of structured and unstructured data relating to an entity. As a user refines the entity profile based on information gathered from various sources, the changing entity profile can be used as a substantially continuously updating query to search, retrieve, and rank new and pertinent information specifically relevant to the profiled entity. The platform described herein provide an active loop for refining an entity description and searching for additional information in which human input and machine-based algorithms can cooperate to more quickly build a comprehensive description of an entity of interest.

Abstract: A journal of operations persistently stores a collection of user and machine interactions with documents, concepts within documents, and relationships/mentions among such documents and concepts. A knowledge graph or other visual expression can be used to provide an interactive format for user interactions with this information, and to visually display the accumulated information in the journal of operations on user's device of choice. Such accumulations are often called aggregations or aggregates. Both human users and computer users such as automated recommendation engines, machine learning algorithms, and the like can interact with the journal of operations to facilitate computer-assisted discovery and organization of data around a topic of interest to the human user. The resulting journal of operations and aggregations of those operations such as a knowledge graph or other visual expression(s) also provides a useful medium for sharing knowledge with other users and collaborating on investigation of a topic.

Abstract: A journal of operations persistently stores a collection of user and machine interactions with documents, concepts within documents, and relationships/mentions among such documents and concepts. A knowledge graph or other visual expression can be used to provide an interactive format for user interactions with this information, and to visually display the accumulated information in the journal of operations on user's device of choice. Such accumulations are often called aggregations or aggregates. Both human users and computer users such as automated recommendation engines, machine learning algorithms, and the like can interact with the journal of operations to facilitate computer-assisted discovery and organization of data around a topic of interest to the human user. The resulting journal of operations and aggregations of those operations such as a knowledge graph or other visual expression(s) also provides a useful medium for sharing knowledge with other users and collaborating on investigation of a topic.

Abstract: Connections among two different entities are identified by processing a collection of documents to identify one or more documents that contain co-occurring mentions of each of the two different entities. This relationship may be graphically displayed in a user interface with an icon or the like for each of the two entities, along with a graphical link interconnecting the entities. The graphical link can be an active element of the user interface that responds to user interactions by providing access to evidence within the collection of documents that substantiates the connection between the two entities. In one aspect, a search input form field in a user interface may be used to explicitly request documents that substantiate a relationship between two entities. In another aspect, a user may ground entity mentions by explicitly selecting documents that mention an entity of interest.

Abstract: A journal of operations persistently stores a collection of user and machine interactions with documents, concepts within documents, and relationships/mentions among such documents and concepts. A knowledge graph or other visual expression can be used to provide an interactive format for user interactions with this information, and to visually display the accumulated information in the journal of operations on user's device of choice. Such accumulations are often called aggregations or aggregates. Both human users and computer users such as automated recommendation engines, machine learning algorithms, and the like can interact with the journal of operations to facilitate computer-assisted discovery and organization of data around a topic of interest to the human user. The resulting journal of operations and aggregations of those operations such as a knowledge graph or other visual expression(s) also provides a useful medium for sharing knowledge with other users and collaborating on investigation of a topic.

Abstract: A user interface of an entity-centric knowledge discovery system presents an entity profile including a mix of structured and unstructured data relating to an entity. As a user refines the entity profile based on information gathered from various sources, the changing entity profile can be used as a substantially continuously updating query to search, retrieve, and rank new and pertinent information specifically relevant to the profiled entity. The platform described herein provide an active loop for refining an entity description and searching for additional information in which human input and machine-based algorithms can cooperate to more quickly build a comprehensive description of an entity of interest.

Abstract: A user interface of an entity-centric knowledge discovery system presents an entity profile including a mix of structured and unstructured data relating to an entity. As a user refines the entity profile based on information gathered from various sources, the changing entity profile can be used as a substantially continuously updating query to search, retrieve, and rank new and pertinent information specifically relevant to the profiled entity. The platform described herein provide an active loop for refining an entity description and searching for additional information in which human input and machine-based algorithms can cooperate to more quickly build a comprehensive description of an entity of interest.

Abstract: A journal of operations persistently stores a collection of user and machine interactions with documents, concepts within documents, and relationships/mentions among such documents and concepts. A knowledge graph or other visual expression can be used to provide an interactive format for user interactions with this information, and to visually display the accumulated information in the journal of operations on user's device of choice. Such accumulations are often called aggregations or aggregates. Both human users and computer users such as automated recommendation engines, machine learning algorithms, and the like can interact with the journal of operations to facilitate computer-assisted discovery and organization of data around a topic of interest to the human user. The resulting journal of operations and aggregations of those operations such as a knowledge graph or other visual expression(s) also provides a useful medium for sharing knowledge with other users and collaborating on investigation of a topic.

Abstract: A journal of operations persistently stores a collection of user and machine interactions with documents, concepts within documents, and relationships/mentions among such documents and concepts. A knowledge graph or other visual expression can be used to provide an interactive format for user interactions with this information, and to visually display the accumulated information in the journal of operations on user's device of choice. Such accumulations are often called aggregations or aggregates. Both human users and computer users such as automated recommendation engines, machine learning algorithms, and the like can interact with the journal of operations to facilitate computer-assisted discovery and organization of data around a topic of interest to the human user. The resulting journal of operations and aggregations of those operations such as a knowledge graph or other visual expression(s) also provides a useful medium for sharing knowledge with other users and collaborating on investigation of a topic.

Abstract: A user interface of an entity-centric knowledge discovery system presents an entity profile including a mix of structured and unstructured data relating to an entity. As a user refines the entity profile based on information gathered from various sources, the changing entity profile can be used as a substantially continuously updating query to search, retrieve, and rank new and pertinent information specifically relevant to the profiled entity. The platform described herein provide an active loop for refining an entity description and searching for additional information in which human input and machine-based algorithms can cooperate to more quickly build a comprehensive description of an entity of interest.

Abstract: A user interface of an entity-centric knowledge discovery system presents an entity profile including a mix of structured and unstructured data relating to an entity. As a user refines the entity profile based on information gathered from various sources, the changing entity profile can be used as a substantially continuously updating query to search, retrieve, and rank new and pertinent information specifically relevant to the profiled entity. The platform described herein provide an active loop for refining an entity description and searching for additional information in which human input and machine-based algorithms can cooperate to more quickly build a comprehensive description of an entity of interest.

Abstract: A user interface of an entity-centric knowledge discovery system presents an entity profile including a mix of structured and unstructured data relating to an entity. As a user refines the entity profile based on information gathered from various sources, the changing entity profile can be used as a substantially continuously updating query to search, retrieve, and rank new and pertinent information specifically relevant to the profiled entity. The platform described herein provide an active loop for refining an entity description and searching for additional information in which human input and machine-based algorithms can cooperate to more quickly build a comprehensive description of an entity of interest.