BACKGROUND

The present invention relates generally to data analysis and, more particularly, to automated resolution of over-specification and under-specification in a knowledge graph.

Big Data is a field concerned with analyzing, extracting information from, or otherwise dealing with large data sets that are too complex for traditional data-processing application software. Various computing modeling tools and techniques are available for analyzing and extracting information from data sets. Graph models and relational databases are often utilized to extract information from data sets for a particular domain (i.e., sphere of activity or knowledge). One tool utilized by analysts is a knowledge graph. The term knowledge graph as used herein refers to a graph that integrates information into an ontology and applies a reasoner to derive new knowledge. A knowledge graph comprises a network of entities that are relevant to a specific domain or to an organization.

SUMMARY

In an aspect of the invention, a computer-implemented method includes: determining, by a computing device, that a size of an object cluster of a knowledge graph meets a threshold value indicating under-specification of a knowledge base of the knowledge graph; determining, by the computing device, sub-classes for objects of the knowledge graph; re-initializing, by the computing device, the knowledge graph based on the sub-classes to generate a refined knowledge graph, wherein the size of the object cluster is reduced in the refined knowledge graph; and generating, by the computing device, an output based on information determined from the refined knowledge graph. Advantageously, the method enables automatic identification of under-specification without the need for an analyst with specialized knowledge of the knowledge graph domain.

In embodiments, the determining the sub-classes for the knowledge graph includes determining parent-child relationships within a latent hierarchical structure of the knowledge base. Such methods advantageously utilize the identification of latent hierarchical structure in the knowledge base to automatically address under-specification of the knowledge graph domain.

In other embodiments, the determining the sub-classes for the knowledge graph includes determining the sub-classes for the knowledge graph based on peer nodes. Such methods advantageously utilize the identification of latent peer-to-peer relationships in the knowledge base to automatically address under-specification of the knowledge graph domain.

In another aspect of the invention, there is a computer program product including a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a computing device to cause the computing device to: generate a knowledge graph from a knowledge base; determine that a size of an object cluster of the knowledge graph meets a threshold value indicating under-specification of the knowledge base; determine sub-classes for objects of the knowledge graph; and re-initialize the knowledge graph based on the sub-classes to generate a refined knowledge graph, wherein the size of the object cluster is reduced in the refined knowledge graph. Advantageously, the computer program product enables automatic identification of under-specification without the need for an analyst with specialized knowledge of the knowledge graph domain.

In embodiments, the determining the sub-classes for the knowledge graph includes determining parent-child relationships within a latent hierarchical structure of the knowledge base. Such computer program products advantageously utilize the identification of latent hierarchical structure in the knowledge base to automatically address under-specification of the knowledge graph domain.

In other embodiments, the determining the sub-classes for the knowledge graph includes determining the sub-classes for the knowledge graph based on peer nodes. Such computer program products advantageously utilize the identification of latent peer-to-peer relationships in the knowledge base to automatically address under-specification of the knowledge graph domain.

In another aspect of the invention, there is a system including a processor, a computer readable memory and a computer readable storage medium associated with a computing device. The system also includes: program instructions to generate a knowledge graph from a knowledge base; program instructions to determine whether a size of an object cluster of the knowledge graph meets a threshold value indicating under-specification of the knowledge base; program instructions to determine sub-classes for objects of the knowledge graph based on a determination that the size of the object cluster of the knowledge graph meets the threshold value; program instructions to re-initialize the knowledge graph based on the sub-classes to generate a refined knowledge graph, wherein the size of the object cluster is reduced in the refined knowledge graph; program instructions to determine whether over-specification of the knowledge base exists by performing a clustering analysist utilizing a Voronoi cells cluster initialization formula; and program instructions to re-initialize the knowledge graph based on the determining the over-specification to generate an updated knowledge graph wherein entities of over-specified object clusters of the refined knowledge graph are subsumed into a single cluster of the updated knowledge graph. The program instructions are stored on the computer readable storage medium for execution by the processor via the computer readable memory. Advantageously, the system enables automatic identification of over-specification and under-specification without the need for an analyst with specialized knowledge of the knowledge graph domain.

In embodiments, the determining the sub-classes for the knowledge graph includes determining parent-child relationships within a latent hierarchical structure of the knowledge base. Such systems advantageously utilize the identification of latent hierarchical structure in the knowledge base to automatically address under-specification of the knowledge graph domain.

In embodiments, the determining the sub-classes for the knowledge graph comprises determining the sub-classes for the knowledge graph based on peer nodes. Such systems advantageously utilize the identification of latent peer-to-peer relationships in the knowledge base to automatically address under-specification of the knowledge graph domain.

In an aspect of the invention, a computer-implemented method includes: accessing, by a computing device, a knowledge graph generated for a knowledge base; determining, by the computing device, over-specification of the knowledge base based on one or more object clusters of the knowledge graph having less than a threshold amount of data points; and re-initializing, by the computing device, the knowledge graph based on the determining the over-specification to generate an updated knowledge graph wherein entities of over-specified object clusters of the knowledge graph are subsumed into a single object cluster of the updated knowledge graph. Such methods enable the determination of over-specification of a knowledge graph without requiring an analyst to have any specialized knowledge of the subject matter of the knowledge base.

In implementations, the determining the over-specification includes performing a clustering analysis utilizing a Voronoi cells cluster initialization formula.

In another aspect of the invention, there is a computer program product including a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a computing device to cause the computing device to: access a knowledge graph generated for a knowledge base; determine over-specification of the knowledge base based on one or more object clusters of the knowledge graph having less than a threshold amount of data points; and re-initialize the knowledge graph based on the determining the over-specification to generate an updated knowledge graph wherein entities of over-specified object clusters of the knowledge graph are subsumed into a single object cluster of the updated knowledge graph.

In implementations, the determining the over-specification includes performing a clustering analysis utilizing a Voronoi cells cluster initialization formula.

