SoftNER: Mining Knowledge Graphs From Cloud Incidents

TL;DR

SoftNER is a framework that extracts structured knowledge graphs from cloud incident reports using a novel multi-task learning model, significantly aiding incident management and triaging in cloud services.

Contribution

The paper introduces SoftNER, a new approach combining multi-task learning and relation mining to automatically construct knowledge graphs from incident reports.

Findings

High precision of 0.96 in entity extraction

Outperforms existing NER models

Enables accurate incident triaging and entity relevance modeling

Abstract

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.