Abstract: A technique suggests resources within a particular environment. The technique operates by receiving a query via a search interface presentation, and using a prefix tree data structure to retrieve candidate resource descriptors that match the query. The candidate resource descriptors are data items that describe different types of candidate resources. The technique further includes: generating feature information that describes the candidate resource descriptors; using a machine-trained ranking model to generate scores associated with the candidate resource descriptors, based on the feature information; sorting the candidate resource descriptors based on the scores, to provide sorted resource descriptors; and generating output information that represents a selected set of resource descriptors drawn from the sorted resource descriptors, for presentation via the search interface presentation.

Abstract: A data processing system implements receiving a textual context inserted into a user interface element; receiving an indicator inserted into the user interface element after the textual context, the indicator indicating a desire to tag a topic from a plurality of topics included in a knowledge base; receiving one or more textual character inserted into the user interface element after the indicator; encoding, using a machine-learning (ML) model, the received textual context to generate at least one representation reflecting one or more meanings of the received textual context; decoding, using the ML model, the at least one representation to generate a plurality of tokens in response to the one or more meanings of the received textual context, the plurality of tokens corresponding with the at least one textual character and at least one of the topics of the plurality of topics; identifying one or more topics from the plurality of topics as recommended topics; and providing the identified recommended topics for

Abstract: A computing system obtains computer-readable text comprising an identifier for an entity and an additional word. The computing system computes relevance scores for entries for entities in a computer-implemented knowledge graph upon the text, titles for the entities in the knowledge graph, and descriptions of the entities in the knowledge graph. The computing system determines whether the identifier for the entity is an acronym or a non-acronym. The computing system identifies an entry for the entity in the knowledge graph based upon the relevance scores and whether the identifier for the entity is an acronym or a non-acronym. The computing system generates a link between the identifier for the entity in the text and the entry for the entity in the knowledge graph, where data from the entry for the entity in the knowledge graph is presented to a user upon the link being selected.

Abstract: A computing system obtains computer-readable text comprising an identifier for an entity and an additional word. The computing system computes relevance scores for entries for entities in a computer-implemented knowledge graph upon the text, titles for the entities in the knowledge graph, and descriptions of the entities in the knowledge graph. The computing system determines whether the identifier for the entity is an acronym or a non-acronym. The computing system identifies an entry for the entity in the knowledge graph based upon the relevance scores and whether the identifier for the entity is an acronym or a non-acronym. The computing system generates a link between the identifier for the entity in the text and the entry for the entity in the knowledge graph, where data from the entry for the entity in the knowledge graph is presented to a user upon the link being selected.

Abstract: A topic-processing system processes topics in a set of documents in a two-stage manner. In the first stage, the system recognizes candidate topics in the set of documents using a machine-trained named-entity recognition (NER) model, to produce original NER information. In a second stage, the system aggregates the original NER information over the set of documents, to produce aggregated information. The system then ranks the candidate topics in the set of candidate topics based on the aggregated information using a machine-trained classification model, to produce a set of ranked topics. The system then selects a set of final topics from the set of ranked topics, e.g., by excluding ranked topics having scores below a prescribed threshold value. A production system presents supplemental information regarding selected final topics, where those final topics are identified by the topic-processing system.

Abstract: A computing system obtains computer-readable text comprising an identifier for an entity and an additional word. The computing system computes relevance scores for entries for entities in a computer-implemented knowledge graph upon the text, titles for the entities in the knowledge graph, and descriptions of the entities in the knowledge graph. The computing system determines whether the identifier for the entity is an acronym or a non-acronym. The computing system identifies an entry for the entity in the knowledge graph based upon the relevance scores and whether the identifier for the entity is an acronym or a non-acronym. The computing system generates a link between the identifier for the entity in the text and the entry for the entity in the knowledge graph, where data from the entry for the entity in the knowledge graph is presented to a user upon the link being selected.

Abstract: Examples described herein generally relate to a computer system including a knowledge graph storing a plurality of entities. A mining of a set of enterprise source documents within an enterprise intranet is performed using singular value decomposition (SVD) to determine a plurality of entity names. Using SVD, relevant and trending entity names are accumulated, aggregated, and ranked. An entity record is generated within a knowledge graph for a mined entity name from the linked entity names based on an entity schema and ones of the set of enterprise source documents associated with the mined entity name. The entity record includes attributes aggregated from the ones of the set of enterprise source documents associated with the mined entity name.

Abstract: A method for manipulating a hierarchical model comprising a set of linked nodes, each represented by a graphic object on a display, is provided. The method comprises storing a node registry including data identifying nodes in the hierarchical model as well as the relationship between the nodes, displaying a representation of the hierarchical model to a user, and detecting a user selection of at least one displayed node. The method further includes detecting the position of a pointer on said display, displaying to a user, in dependence on the pointer position, an indication of a plurality of potential drop zones of a target node, each representing a potential target operation on the target node, detecting selection of one of said potential drop zones of said target node, and performing the target operation represented by the selected drop zone so as to add the at least one selected node in a particular relationship to said target node in said hierarchical model.