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SoftNER: Mining knowledge graphs from cloud incidents
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DOI：10.1007/s10664-022-10159-w.png)
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摘要
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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 SoftNER's unsupervised pipeline learns the software entity types from unstructured incident data with high precision of 0.96 (at rank 50) and 0.77 (at rank 100). We also evaluate and show that SoftNER's unsupervised pipeline accurately labels data with a precision of 0.94. Further, our multi-task learning based deep learning model also outperforms the state-of-the-art NER models with an average F1 of 0.96. Lastly, using the knowledge extracted by SoftNER, we are able to build accurate models for tasks such as incident triaging and recommending entities based on their relevance to incident titles.
Keyword:
Cloud services
Service incidents
Knowledge extraction
Knowledge graphs
Deep learning
Machine learning
期刊
IF:
3.6
论文数:
2.0K
被引数:
5.3K
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