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.
翻译:从箱装产品转向服务和广泛采用云计算,对软件开发生命周期和DevOps流程产生了巨大影响。特别是,事故管理对于开发和运行大型服务至关重要。事故管理以前的工作主要侧重于事件变异和减少变化的挑战。我们在工作中解决了从服务事故中结构化知识提取的根本问题。我们建立了SoftNER,一个从事件报告中挖掘知识图的框架。首先,我们建立了一个基于BILSTM-CRF的新颖的多任务学习模式,它不仅利用语义环境,而且还利用数据类型来提取以指定实体形式提供的事实信息。接下来,我们介绍了在指定实体间进行地雷处理的方法,以便自动建立知识图。我们在微软公司(一个主要的云服务供应商)部署了SoftNER,并在超过两个月的云中事件事件中对它进行了评估。我们发现,未经监督的机器学习管道的精确度高达0.96,我们基于多任务学习的深层次学习模式也超越了以指定实体的形式获取的事实信息。我们用这种知识模型来自动建立准确的模型。最后,我们用这种知识模型来建立事件内核模型,我们用这种模型来进行测试。我们能够的三核反应模型。