Salesforce, Inc.

Salesforce Com, Inc.

Aaron Michael Taylor

Henry Forrest Leanna Wallace

John Randolph Frank

Andrew Richard Gallant

Max Kleiman-Weiner

Daniel Adam Roberts

Thomas Michael DuBois

Kevin John Doyle

Jason Rastrick Briggs

Cogan Dwayne Culver

Keith Michael Gabryelski

Andrew Wilson Haskell

Geoffrey Ira Milstein

Emily Brooks Pavlini

David Zachary Maze

Abdi-Hakin Dirie

Logan Eli Zoellner

David Johnson

Tyler Balensiefer

An unsupervised multi-hop search across a corpus of documents in a database or other data resource permits the identification of relationships between two entities mentioned in the corpus in cases where the two entities are not co-mentioned within any documents in the corpus (or not mentioned within document(s) with sufficient frequency or proximity to infer the relationship). The search can employ a beam search algorithm anchored by word embeddings and an A* graph traversal to calculate semantic distance between the entities as different paths through the corpus for different entity co-mentions are evaluated.

Multi-hop search for entity relationships

An entity-based platform stores a database of entity mentions and related, entity-based information obtained by processing text in a corpus of documents such as content from a public network or content from a document management system. The platform may analyze changes to a previously-processed document in order to align mentions in the modified document to mentions in a prior document and/or to identify new mentions not present in the previously-processed document. As the underlying documents in the corpus change over time, this advantageously permits sustained use of knowledge graphs and other entity-based representations or analysis linked to specific entity mentions.

Stable identification of entity mentions

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.

Systems and method for investigating relationships among entities

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.

Knowledge operating system

Growing community of inventors

Williamstown, MA, United States of America

Aaron Michael Taylor