MACHINE LEARNING-BASED ISSUE CLASSIFICATION UTILIZING COMBINED REPRESENTATIONS OF SEMANTIC AND STATE TRANSITION GRAPHS
An apparatus comprises a processing device configured to obtain, for a given issue associated with one or more assets of an information technology infrastructure, a description of the given issue and system logs characterizing operation of the one or more assets. The processing device is also configured to generate one or more semantic graphs characterizing the description of the given issue and one or more state transition graphs characterizing a sequence of occurrence of states of the operation of the one or more assets. The processing device is further configured to provide a combined representation of the semantic and state transition graphs for the given issue to a machine learning model, to identify recommended classifications for the given issue based on an output of the machine learning model, and to initiate remedial action in the information technology infrastructure based on the recommended classifications for the given issue.
1 . An apparatus comprising:
at least one processing device comprising a processor coupled to a memory;
the at least one processing device being configured to perform steps of:
obtaining, for a given issue associated with one or more assets of an information technology infrastructure, a description of the given issue and one or more system logs characterizing operation of the one or more assets of the information technology infrastructure;
generating one or more semantic graphs characterizing the description of the given issue and one or more state transition graphs characterizing a sequence of occurrence of one or more states of the operation of the one or more assets of the information technology infrastructure;
providing a combined representation of the one or more semantic graphs and the one or more state transition graphs for the given issue to a machine learning model;
identifying one or more recommended classifications for the given issue based at least in part on an output of the machine learning model; and
initiating one or more remedial actions in the information technology infrastructure based at least in part on the one or more recommended classifications for the given issue.
2 . The apparatus of claim 1 wherein a given one of the one or more semantic graphs represents at least a subset of words of the description of the given issue as nodes with edges connecting the nodes representing placement of the words relative to one another in the description of the given issue.
3 . The apparatus of claim 2 wherein the given issue is associated with a given domain, and wherein one or more of the words in the subset of words of the description of the given issue comprise terms from a domain-specific glossary of terms in a corpus defined for the given domain.
4 . The apparatus of claim 3 wherein generating the given semantic graph comprises assigning a part of speech category to each of the words in the subset of words of the description of the given issue.
5 . The apparatus of claim 1 wherein generating the one or more semantic graphs and the one or more state transition graphs comprises performing preprocessing on the description of the given issue and the one or more system logs.
6 . The apparatus of claim 5 wherein performing preprocessing on the description of the given issue and the one or more system logs comprises at least one of:
removing digits, punctuation and symbols;
removing alphanumeric sequences; and
removing identifiers.
7 . The apparatus of claim 1 wherein a given one of the one or more state transition graphs represents states of operation of the one or more assets of the information technology infrastructure as nodes with edges connecting the nodes representing a sequence of occurrence of the states of operation of the one or more assets of the information technology infrastructure.
8 . The apparatus of claim 7 wherein the given issue is associated with a given domain, and wherein one or more of the states of operation of the one or more assets of the information technology infrastructure comprise terms from a domain-specific glossary of states in a corpus defined for the given domain.
9 . The apparatus of claim 1 wherein the machine learning model comprises a graph convolutional neural network.
10 . The apparatus of claim 9 wherein the graph convolutional neural network comprises two or more hidden layers, a first one of the two or more hidden layers having a structure determined based at least in part on a number of vertices in the combined representation of the one or more semantic graphs and the one or more state transition graphs for the given issue, a second one of the two or more hidden layers having a structure determined based at least in part on a number of possible classification labels for the given issue.
11 . The apparatus of claim 9 wherein the combined representation of the one or more semantic graphs and the one or more state transition graphs for the given issue comprises a feature matrix and an adjacency matrix, the feature matrix comprising an identity matrix with elements representing vertices of the one or more semantic graphs and the one or more state transition graphs, the adjacency matrix comprising elements representing whether pairs of vertices of the one or more semantic graphs and the one or more state transition graphs are adjacent to one another.
12 . The apparatus of claim 1 wherein the at least one processing device is further configured to train the machine learning model utilizing combined representations of one or more historical semantic graphs and one or more historical state transition graphs generated for one or more historical issues associated with the assets of the information technology infrastructure.
13 . The apparatus of claim 12 wherein the representations of the one or more historical issues associated with assets of the information technology infrastructure comprise:
a feature matrix comprising an identity matrix with elements representing vertices of the one or more historical semantic graphs and the one or more historical state transition graphs generated for the one or more historical issues;
an adjacency matrix comprising elements representing whether pairs of vertices of the one or more historical semantic graphs and the one or more historical state transition graphs are adjacent to one another; and
a label matrix comprising elements representing classification labels for the one or more historical issues.
14 . The apparatus of claim 1 wherein initiating the one or more remedial actions comprises modifying a configuration of the one or more assets of the information technology infrastructure.
15 . A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform steps of:
obtaining, for a given issue associated with one or more assets of an information technology infrastructure, a description of the given issue and one or more system logs characterizing operation of the one or more assets of the information technology infrastructure;
generating one or more semantic graphs characterizing the description of the given issue and one or more state transition graphs characterizing a sequence of occurrence of one or more states of the operation of the one or more assets of the information technology infrastructure;
providing a combined representation of the one or more semantic graphs and the one or more state transition graphs for the given issue to a machine learning model;
identifying one or more recommended classifications for the given issue based at least in part on an output of the machine learning model; and
initiating one or more remedial actions in the information technology infrastructure based at least in part on the one or more recommended classifications for the given issue.
16 . The computer program product of claim 15 wherein the machine learning model comprises a graph convolutional neural network, the graph convolutional neural network comprising two or more hidden layers, a first one of the two or more hidden layers having a structure determined based at least in part on a number of vertices in the combined representation of the one or more semantic graphs and the one or more state transition graphs for the given issue, a second one of the two or more hidden layers having a structure determined based at least in part on a number of possible classification labels for the given issue.
17 . The computer program product of claim 15 wherein the machine learning model comprises a graph convolutional neural network, the combined representation of the one or more semantic graphs and the one or more state transition graphs for the given issue comprises a feature matrix and an adjacency matrix, the feature matrix comprising an identity matrix with elements representing vertices of the one or more semantic graphs and the one or more state transition graphs, the adjacency matrix comprising elements representing whether pairs of vertices of the one or more semantic graphs and the one or more state transition graphs are adjacent to one another.
18 . A method comprising:
obtaining, for a given issue associated with one or more assets of an information technology infrastructure, a description of the given issue and one or more system logs characterizing operation of the one or more assets of the information technology infrastructure;
generating one or more semantic graphs characterizing the description of the given issue and one or more state transition graphs characterizing a sequence of occurrence of one or more states of the operation of the one or more assets of the information technology infrastructure;
providing a combined representation of the one or more semantic graphs and the one or more state transition graphs for the given issue to a machine learning model;
identifying one or more recommended classifications for the given issue based at least in part on an output of the machine learning model; and
initiating one or more remedial actions in the information technology infrastructure based at least in part on the one or more recommended classifications for the given issue;
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.
19 . The method of claim 18 wherein the machine learning model comprises a graph convolutional neural network, the graph convolutional neural network comprising two or more hidden layers, a first one of the two or more hidden layers having a structure determined based at least in part on a number of vertices in the combined representation of the one or more semantic graphs and the one or more state transition graphs for the given issue, a second one of the two or more hidden layers having a structure determined based at least in part on a number of possible classification labels for the given issue.
20 . The method of claim 18 wherein the machine learning model comprises a graph convolutional neural network, the combined representation of the one or more semantic graphs and the one or more state transition graphs for the given issue comprises a feature matrix and an adjacency matrix, the feature matrix comprising an identity matrix with elements representing vertices of the one or more semantic graphs and the one or more state transition graphs, the adjacency matrix comprising elements representing whether pairs of vertices of the one or more semantic graphs and the one or more state transition graphs are adjacent to one another.