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: 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: 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: 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: 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 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: 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: 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.