Mining Knowledge Graphs From Incident Reports. (arXiv:2101.05961v1 [cs.SE])

Incident management is a critical part of the DevOps processes for developing
and operating large-scale services in the cloud. Incident reports filed by
customers are largely unstructured making any automated diagnosis or mitigation
non-trivial. It requires on-call engineers to parse verbose reports to
understand the issue and locate key information. Prior work has looked into
extraction of key attributes or entities like error codes, tenant Ids, stack
traces, etc. from incident and bug reports. Although a flat list of entities is
informative, to unlock the full potential of knowledge extraction, it is
necessary to provide context to these entities. For instance, the relations
between the real-world concepts or objects that these entities represent in
otherwise unstructured data is useful for downstream tasks like incident
linking, triaging and mitigation. With this additional context, entities are
transformed from “Strings” to “Things”. In this work, we present an approach to
mine and score binary entity relations from co-occurring entity pairs. We
evaluate binary relations extracted and show that our approach has a high
precision of 0.9. Further, we construct knowledge graphs automatically and show
that the implicit knowledge in the graph can be used to mine and rank relevant
entities for distinct incidents, by mapping entities to clusters of incident
titles.

Source: https://arxiv.org/abs/2101.05961

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