  The move from boxed products to services and the widespread adoption of cloud
computing has had a huge impact on the software development life cycle and
DevOps processes. Particularly, incident management has become critical for
developing and operating large-scale services. Prior work on incident
management has heavily focused on the challenges with incident triaging and
de-duplication. In this work, we address the fundamental problem of structured
knowledge extraction from service incidents. We have built SoftNER, a framework
for mining Knowledge Graphs from incident reports. First, we build a novel
multi-task learning based BiLSTM-CRF model which leverages not just the
semantic context but also the data-types for extracting factual information in
the form of named entities. Next, we present an approach to mine relations
between the named entities for automatically constructing knowledge graphs. We
have deployed SoftNER at Microsoft, a major cloud service provider and have
evaluated it on more than 2 months of cloud incidents. We show that the
unsupervised machine learning pipeline has a high precision of 0.96. Our
multi-task learning based deep learning model also outperforms the
state-of-the-art NER models. Lastly, using the knowledge extracted by SoftNER,
we are able to build accurate models for applications such as incident triaging
and recommending entities based on their relevance to incident titles.
