Abstract: In various examples there is a computer-implemented method of database construction. The method comprises storing a knowledge graph comprising nodes connected by edges, each node representing a topic. Accessing a topic type hierarchy comprising a plurality of types of topics, the topic type hierarchy having been computed from a corpus of text documents. One or more text documents are accessed and the method involves labelling a plurality of the nodes with one or more labels, each label denoting a topic type from the topic type hierarchy, by, using a deep language model; or for an individual one of the nodes representing a given topic, searching the accessed text documents for matches to at least one template, the template being a sequence of words and containing the given topic and a placeholder for a topic type; and storing the knowledge graph comprising the plurality of labelled nodes.

Abstract: Examples of the present disclosure describe systems and methods for cross-provider topic conflation. In aspects, a request relating to one or more topics may be received by a content surfacing platform. One or more data sources of multiple content providers may be searched for documents relating to the topic(s). Document content (e.g., document metadata and sentences, phrases, and other word content within the document) relating to the topic(s) may be extracted from the documents of the various content providers. The document content may be classified and/or separated into subparts. The subparts may be clustered and/or conflated by topic, thereby removing duplicated data while preserving the unique information in each subpart. The conflated topics may be stored in a single knowledge base, such as an enterprise knowledge graph, and/or presented in response to the request.

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, by a user-based mining system, to determine a plurality of entity names. 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: The techniques disclosed herein enable systems to enhance the efficiency and functionality of knowledge base systems through automated generation of knowledge base content such as topic definitions using a large language model. This is accomplished by utilizing a summarization module that processes incoming requests pertaining to a knowledge base topic. In response to a request, the summarization module can retrieve information related to the topic and generate an instruction directing a large language model to generate a natural language output. By generating the instruction from the specific context of the knowledge base, the disclosed techniques can ensure that outputs received from the large language model are consistent and relevant. In addition, content that was generated based on privileged information such as an access-controlled document can receive the same access controls to maintain information security.

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, by an enterprise named entity recognition (ENER) model, to determine a plurality of entity names. 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: Techniques for establishing a network connection with a client device and responding to composite requests from the client device. For a composite request identifying a first action associated with a first data plugin component and a second action associated with a different second data plugin component, the first action is performed by executing a first portion of the data plugin component and transmitting corresponding response data in a first transmission to the client device via the network connection, and the second action is performed by executing a second portion of the data plugin component and transmitting corresponding response data in a second transmission separate from the first transmission to the client device via the network connection.

Abstract: Aggregation of content based on user-generated topics is provided. Users may associate one or more topics with content items stored across various workloads and repositories. A topic may be a word or phrase of the user's choice, and may be utilized for discoverability of information and aggregation of content items. Topics and content items associated with topics may be acted on (e.g., a user may add or delete topics to associate with a content item, associate or disassociate content items with a topic, embed a set of content items or a stream of content items associated with a topic into other experiences, follow topics, etc.). Content items identified as related to a specific topic may be automatically suggested as possible content items of interest to the user. Additionally, when a user follows a topic, the user may be notified of any changes that occur to the topic.

Abstract: A data processing system implements receiving a request for user-centric topic recommendations from a computing device of a user in response to a user input in an application indicating that the user is attempting to assign a tag to a first content item in the application; obtaining a first set of user-centric topic recommendations from a first topic datastore based on a relevance ranking assigned to each of the topic recommendations; providing the first set of user-centric topic recommendations to the computing device; causing the computing device to display the first set of user-centric topic recommendations; receiving one or more second requests for user-centric topic recommendations; obtaining one or more second sets of user-centric topic recommendations from the first topic datastore based on the query string and the relevance ranking; and causing the computing device of the user to display the one or more second sets of user-centric topic recommendations.

Abstract: Examples described herein generally relate to a computer system for generating a knowledge graph storing a plurality of entities and to displaying a topic page for an entity in the knowledge graph. The computer system performs a mining of source documents within an enterprise intranet to determine a plurality of entity names. The computer system generates an entity record within the knowledge graph for a mined entity name based on an entity schema and the source documents. The entity record includes attributes aggregated from the source documents. The computer system receives a curation action on the entity record from a first user. The computer system updates the entity record based on the curation action. The computer system displays an entity page including at least a portion of the attributes to a second user based on permissions of the second user to view the source documents.

Abstract: A data processing system implements receiving a request for user-centric topic recommendations from a computing device of a user in response to a user input in an application indicating that the user is attempting to assign a tag to a first content item in the application; obtaining a first set of user-centric topic recommendations from a first topic datastore based on a relevance ranking assigned to each of the topic recommendations; providing the first set of user-centric topic recommendations to the computing device; causing the computing device to display the first set of user-centric topic recommendations; receiving one or more second requests for user-centric topic recommendations; obtaining one or more second sets of user-centric topic recommendations from the first topic datastore based on the query string and the relevance ranking; and causing the computing device of the user to display the one or more second sets of user-centric topic recommendations.

Abstract: A computing system comprises, in one example, a display system, a user profile update system configured to update a user profile for a given user, a data extraction system configured to analyze a data source and to extract, from the data source, at least one work attribute associated with the given user, and a surfacing system. The surfacing system comprises a suggestion generator component configured to generate a user profile suggestion based on the at least one work attribute, and a display system controller configured to control the display system to generate a user interface display, with user input mechanisms, that displays the user profile suggestion and to detect a user interaction with the user input mechanisms. The user profile update system updates the user profile based on the detected user interaction.

Abstract: Systems and methods for providing notification delivery based on utilization of bloom filters are provided. A collaboration system obtains subscriber information for each user of a collaboration system, whereby the subscriber information corresponds to one or more features of content that are relevant to each user. The collaboration system hashes the subscriber information to generate a bloom filter for each user. The collaboration system receives an article to be published, whereby the article comprises a set of features. The set of features is hashed to obtain a hash set. The hashing of the set of features is performed using same hashing functions as that used to generate the bloom filter. The collaboration system compares the hash set to the bloom filter to identify a match, whereby the match indicates a feature of the article matches the subscriber information. The collaboration system generates a list of recipients based on the match.

Abstract: In various examples there is a computer-implemented method of database construction. The method comprises storing a knowledge graph comprising nodes connected by edges, each node representing a topic. Accessing a topic type hierarchy comprising a plurality of types of topics, the topic type hierarchy having been computed from a corpus of text documents. One or more text documents are accessed and the method involves labelling a plurality of the nodes with one or more labels, each label denoting a topic type from the topic type hierarchy, by, using a deep language model; or for an individual one of the nodes representing a given topic, searching the accessed text documents for matches to at least one template, the template being a sequence of words and containing the given topic and a placeholder for a topic type; and storing the knowledge graph comprising the plurality of labelled nodes.

Abstract: In various examples there is a computer-implemented method of database construction. The method comprises storing a knowledge graph comprising nodes connected by edges, each node representing a topic. Accessing a topic type hierarchy comprising a plurality of types of topics, the topic type hierarchy having been computed from a corpus of text documents. One or more text documents are accessed and the method involves labelling a plurality of the nodes with one or more labels, each label denoting a topic type from the topic type hierarchy, by, using a deep language model; or for an individual one of the nodes representing a given topic, searching the accessed text documents for matches to at least one template, the template being a sequence of words and containing the given topic and a placeholder for a topic type; and storing the knowledge graph comprising the plurality of labelled nodes.

Abstract: Extracting and surfacing information corresponding to individual logical topics from enterprise data stores that are separated across multiple geographic regions. A clustering service creates, by utilizing machine learning toolkits that are agnostic to the region in which data is stored, individual topics that have references to multiple shards of data that are stored in different geographic regions. The clustering service also shards the knowledge base state according to the regions from which pieces of data for the particular logical topic was extracted. For example, a first shard containing information extracted from a first document may be stored in a first region whereas a second shard containing information extracted from a second document may be stored in a second region.

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, by a plurality of knowledge mining toolkits, to determine a plurality of entity names. The plurality of entity names are linked based on entity metadata by traversing various relationships between people, files, sites, groups, associated with entities. 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: Systems and methods for facilitating an enterprise user to obtain an answer to a user question within an enterprise based on an enterprise knowledge graph are provided. In particular, an enterprise server may receive the user question from the enterprise user, determine a suggested topic associated with the user question based on the enterprise knowledge graph by transforming the user question into a semantic representation to identify a plurality of similar entities within the enterprise knowledge graph, and determine whether a relevant question-and-answer (Q&A) pair linked to the suggested topic exists based on the enterprise knowledge graph. In response to a determination that the relevant Q&A pair does not exist, the enterprise server may determine a predicted answer to the user question and update the enterprise knowledge graph.

Abstract: Mining of a set of enterprise source documents within an enterprise intranet is performed, by a plurality of knowledge mining toolkits, to determine a plurality of entity names. A plurality of entity records are generated within a knowledge graph for mined entity names from the entity names based on an entity schema and ones of the set of enterprise source documents associated with the mined entity names. Pattern recognition is applied to an active document using an enterprise named entity recognition (ENER) system to identify potential entity names within the document that match a respective one of a plurality of entity records in the knowledge graph. One or more matching entity names are annotated within the document with information from the knowledge graph for the respective ones of the plurality of entity records. The annotated information is displayed with the active document.

Abstract: Examples of the present disclosure describe systems and methods for cross-provider topic conflation. In aspects, a request relating to one or more topics may be received by a content surfacing platform. One or more data sources of multiple content providers may be searched for documents relating to the topic(s). Document content (e.g., document metadata and sentences, phrases, and other word content within the document) relating to the topic(s) may be extracted from the documents of the various content providers. The document content may be classified and/or separated into subparts. The subparts may be clustered and/or conflated by topic, thereby removing duplicated data while preserving the unique information in each subpart. The conflated topics may be stored in a single knowledge base, such as an enterprise knowledge graph, and/or presented in response to the request.

Abstract: Techniques for establishing a network connection with a client device and responding to composite requests from the client device. For a composite request identifying a first action associated with a first data plugin component and a second action associated with a different second data plugin component, the first action is performed by executing a first portion of the data plugin component and transmitting corresponding response data in a first transmission to the client device via the network connection, and the second action is performed by executing a second portion of the data plugin component and transmitting corresponding response data in a second transmission separate from the first transmission to the client device via the network connection.