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: A secondary location is configured as a recovery service for a primary location of the service. The secondary location is maintained in a warm state that is configured to replace the primary location in a case of a failover. During normal operation, the secondary location is automatically updated to reflect a current state of the primary location that is actively servicing user load. Content changes to the primary location are automatically reflected to the secondary location. System changes applied to the primary location are automatically applied to the secondary location. For example, removing/adding machines, updating machine/role assignments, removing adding/database are automatically applied to the secondary location such that the secondary location substantially mirrors the primary location. After a failover to the secondary location, the secondary location becomes the primary location and begins to actively service the user load.

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: 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: 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: 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.

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: 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: A secondary location is configured as a recovery service for a primary location of the service. The secondary location is maintained in a warm state that is configured to replace the primary location in a case of a failover. During normal operation, the secondary location is automatically updated to reflect a current state of the primary location that is actively servicing user load. Content changes to the primary location are automatically reflected to the secondary location. System changes applied to the primary location are automatically applied to the secondary location. For example, removing/adding machines, updating machine/role assignments, removing adding/database are automatically applied to the secondary location such that the secondary location substantially mirrors the primary location. After a failover to the secondary location, the secondary location becomes the primary location and begins to actively service the user load.

Abstract: Examples described herein generally relate to a computer system including a knowledge graph storing a plurality of entities. The computer system generates an Aho Corasick trie including an entity name for each of the plurality of entities in the knowledge graph. The computer system compares a document viewed by a user to a plurality of templates defining potential entity names to identify extracts of the document matching at least one of the plurality of templates. The computer system applies the document to the Aho Corasick trie to determine potential entity names within the document that each match a respective one of the plurality of entities in the knowledge graph. The computer system annotates one or more matching entity names within the document with information from the knowledge graph for the respective ones of the plurality of entities to show, for example, a topic card providing information about the respective entities.

Abstract: Examples described herein generally relate to a computer system including a knowledge graph storing a plurality of entities. The computer system compares source documents within an enterprise intranet to a plurality of templates defining potential entity attributes to identify extracts matching at least one of the plurality of templates. The computer system parses the extracts according to respective templates of the plurality of templates that match the extracts to determine instances. The computer system performs incremental clustering on a number of the instances to determine potential entity names. The computer system queries the knowledge graph with the potential entity names to obtain a set of candidate entity records. The computer system links the potential entity names with at least partial matching ones of the set of candidate entity records to define updated matching candidate entity records. The computer system updates the knowledge graph with the updated matching candidate entity records.

Abstract: A secondary location is configured as a recovery service for a primary location of the service. The secondary location is maintained in a warm state that is configured to replace the primary location in a case of a failover. During normal operation, the secondary location is automatically updated to reflect a current state of the primary location that is actively servicing user load. Content changes to the primary location are automatically reflected to the secondary location. System changes applied to the primary location are automatically applied to the secondary location. For example, removing/adding machines, updating machine/role assignments, removing adding/database are automatically applied to the secondary location such that the secondary location substantially mirrors the primary location. After a failover to the secondary location, the secondary location becomes the primary location and begins to actively service the user load.

Abstract: Document tags are rapidly indexed using a text based index and a graph index. A tag signal is received. A tag and a type of the tag that are located in the tag signal are stored in a data store. The tag is indexed as a tag document in the text based index. One or more relationships between the tag and a content document are managed in the graph index.

Abstract: Examples described herein generally relate to a computer system including a knowledge graph storing a plurality of entities. The computer system generates an Aho Corasick trie including an entity name for each of the plurality of entities in the knowledge graph. The computer system compares a document viewed by a user to a plurality of templates defining potential entity names to identify extracts of the document matching at least one of the plurality of templates. The computer system applies the document to the Aho Corasick trie to determine potential entity names within the document that each match a respective one of the plurality of entities in the knowledge graph. The computer system annotates one or more matching entity names within the document with information from the knowledge graph for the respective ones of the plurality of entities to show, for example, a topic card providing information about the respective entities.

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: Examples described herein generally relate to a computer system including a knowledge graph storing a plurality of entities. The computer system compares source documents within an enterprise intranet to a plurality of templates defining potential entity attributes to identify extracts matching at least one of the plurality of templates. The computer system parses the extracts according to respective templates of the plurality of templates that match the extracts to determine instances. The computer system performs incremental clustering on a number of the instances to determine potential entity names. The computer system queries the knowledge graph with the potential entity names to obtain a set of candidate entity records. The computer system links the potential entity names with at least partial matching ones of the set of candidate entity records to define updated matching candidate entity records. The computer system updates the knowledge graph with the updated matching candidate entity records.