论文标题
与知识图嵌入模型有关上下文依赖性异常检测
Context-Dependent Anomaly Detection with Knowledge Graph Embedding Models
论文作者
论文摘要
提高语义理解和对机器学习模型的上下文意识对于改善稳健性和降低数据转移的敏感性至关重要。在这项工作中，我们利用上下文意识来解决异常检测问题。尽管基于图形的异常检测进行了广泛的研究，但依赖上下文依赖性的异常检测是一个空旷的问题，并且没有当前的研究很多。我们开发了一个通用框架，以将依赖上下文的异常检测问题转换为链接预测问题，从而可以应用该领域的良好技术。我们基于我们的框架实现了一个系统，该系统利用知识图嵌入模型，并证明使用语义知识库提供的上下文检测异常值的能力。我们表明，我们的方法可以以高度准确性检测与上下文相关的异常，并表明当前对象检测器可以检测到足够的类，以在我们的示例域内为良好性能提供所需的上下文。
Increasing the semantic understanding and contextual awareness of machine learning models is important for improving robustness and reducing susceptibility to data shifts. In this work, we leverage contextual awareness for the anomaly detection problem. Although graphed-based anomaly detection has been widely studied, context-dependent anomaly detection is an open problem and without much current research. We develop a general framework for converting a context-dependent anomaly detection problem to a link prediction problem, allowing well-established techniques from this domain to be applied. We implement a system based on our framework that utilizes knowledge graph embedding models and demonstrates the ability to detect outliers using context provided by a semantic knowledge base. We show that our method can detect context-dependent anomalies with a high degree of accuracy and show that current object detectors can detect enough classes to provide the needed context for good performance within our example domain.
