# SoftNER: Mining Knowledge Graphs From Cloud Incidents

arXiv: https://arxiv.org/abs/2101.05961 · HTML: https://mlsanity.com/paper/2101.05961/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 64 references that are in the map.
- First posted on arXiv 15 Jan 2021.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [Relation Extraction : A Survey](https://mlsanity.com/paper/1712.05191/) (2017)
- [An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols](https://mlsanity.com/paper/1805.00195/) (2018)
- [A Multi-task Approach for Named Entity Recognition in Social Media Data](https://mlsanity.com/paper/1906.04135/) (2019)
- [DeCaf: Diagnosing and Triaging Performance Issues in Large-Scale Cloud Services](https://mlsanity.com/paper/1910.05339/) (2019)
- [Neural Knowledge Extraction From Cloud Service Incidents](https://mlsanity.com/paper/2007.05505/) (2020)
- [Task-Oriented Learning of Word Embeddings for Semantic Relation Classification](https://mlsanity.com/paper/1503.00095/) (2015)
- [Search4Code: Code Search Intent Classification Using Weak Supervision](https://mlsanity.com/paper/2011.11950/) (2020)

## Built on by (most cited first, 5 of 5)

- [AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges](https://mlsanity.com/paper/2304.04661/) (2023)
- [AutoTSG: Learning and Synthesis for Incident Troubleshooting](https://mlsanity.com/paper/2205.13457/) (2022)
- [A Comprehensive Survey on Root Cause Analysis in (Micro) Services: Methodologies, Challenges, and Trends](https://mlsanity.com/paper/2408.00803/) (2024)
- [DeepAnalyze: Learning to Localize Crashes at Scale](https://mlsanity.com/paper/2109.14326/) (2021)
- [FaultProfIT: Hierarchical Fault Profiling of Incident Tickets in Large-scale Cloud Systems](https://mlsanity.com/paper/2402.17583/) (2024)

## Abstract (arXiv)

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.