Constructing enterprise-specific knowledge graphs

One or more computer storage media storing computer-useable instructions that, when executed by one or more processors, cause the one or more processors to perform a method for constructing an enterprise-specific knowledge graph, the method comprising:

• receiving enterprise data from an enterprise knowledge repository, a first portion of the enterprise data being structured data and a second portion of the enterprise data being unstructured data, the structured data having a first plurality tuples, each of the first plurality of tuples including a structured-data-tuple <subject> element, a structured-data-tuple <predicate> element, and a structured-data-tuple <object> element arranged in a <subject><predicate><object> format;
• identifying a relationship corresponding to each of the plurality of structured-data-tuples that matches one of a plurality of predefined relationships included in a predefined relationship taxonomy;
• extracting a second plurality of tuples from the unstructured data, each of the second plurality of tuples including an unstructured-data-tuple <subject> element, an unstructured-data-tuple <predicate> element, and an unstructured-data-tuple <object> element arranged in the <subject><predicate><object> format;
• determining that each unstructured-data-tuple of a first portion of the second plurality of tuples refers to a relationship that matches one of the plurality of predefined relationships included in the predefined relationship taxonomy and each unstructured-data-tuple of a second portion of the second plurality of tuples refers to a relationship that does not match one of the plurality of predefined relationships included in the predefined relationship taxonomy;
• identifying one of the plurality of predefined relationships included in the predefined relationship taxonomy that matches each unstructured-data-tuple of the first portion of the second plurality of tuples;
• sequentially clustering the relationships referenced by the second portion of the second plurality of tuples such that similar relationships are grouped together;
• attributing an assigned relationship to at least part of the unstructured-data-tuples of the second plurality of tuples based upon the sequential clustering;
• constructing the enterprise-specific knowledge graph from, at least in part, the structured-data-tuples of the first plurality of tuples and their respective identified relationships, the unstructured-data-tuples of the first portion of the second plurality of tuples and their respective identified relationships, and at least part of the unstructured-data-tuples of the second portion of the second plurality of tuples and their respective assigned relationships;
• and
• enriching the enterprise-specific knowledge graph by adding a third plurality of tuples that is determined to be missing from the enterprise-specific knowledge graph based upon existing patterns.

A computer system comprising:

• one or more processors;
• and
• one or more computer storage media storing computer-useable instructions that, when used by the one or more processors, cause the one or more processors to:
• (a) receive enterprise data from at least one source having enterprise-specific information associated therewith, a first portion of the received enterprise data being structured data and a second portion of the received enterprise data being unstructured data, the structured data having a first plurality of text fields, each text field of the first plurality of text fields corresponding to a structured-data-tuple;
• (b) canonicalize the structured data by:
• (1) identifying a relationship corresponding to each structured-data-tuple;
• and
• (2) mapping the relationship to a relationship included in a predefined relationship taxonomy;
• (c) canonicalize the unstructured data by:
• (3) extracting a plurality of unstructured-data-tuples from the unstructured data, each of the plurality of unstructured-data-tuples having a <subject> element, a <predicate> element and an <object> element, the <subject> and <object> elements representing entities and the <predicate> element representing a correlation between the <subject> and <object> elements;
• (4) mapping the <subject> and <object> elements of each unstructured-data-tuple to entity categories included in a predefined entity taxonomy;
• (5) determining a first portion of the <predicate> elements that can be mapped to the predefined relationship taxonomy and a second portion of the <predicate> element that cannot be mapped to the predefined relationship taxonomy;
• (6) mapping each <predicate> element of the first portion of the <predicate> elements to a relationship included in the predefined relationship taxonomy;
• (7) sequentially clustering the second portion of the <predicate> elements to group similar <predicate> elements together;
• and
• (8) attributing an assigned relationship to at least part of the second portion of the <predicate> elements based on the sequential clustering;
• and
• (d) construct an enterprise-specific knowledge graph from, at least in part, the structured-data-tuples and their respective identified relationships, the unstructured-data-tuples containing the first portion of the <predicate> elements and their respective identified relationships, and at least a portion of the unstructured-data-tuples containing the second portion of the <predicate> elements and their respective assigned relationships.