Knowledge graph embeddings for dealing with concept drift in machine learning

Chen Jiaoyan; Lecue Freddy; Pan Jeff Z.; Deng Shumin; Chen Huajun

摘要

Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. As data is evolving on a temporal basis, its underlying knowledge is subject to many challenges. Concept drift,1 as one of core challenge from the stream learning community, is described as changes of statistical properties of the data over time, causing most of machine learning models to be less accurate as changes over time are in unforeseen ways. This is particularly problematic as the evolution of data could derive to dramatic change in knowledge. We address this problem by studying the semantic representation of data streams in the Semantic Web, i.e., ontology streams. Such streams are ordered sequences of data annotated with ontological vocabulary. In particular we exploit three levels of knowledge encoded in ontology streams to deal with concept drifts: i) existence of novel knowledge gained from stream dynamics, ii) significance of knowledge change and evolution, and iii) (in)consistency of knowledge evolution. Such knowledge is encoded as knowledge graph embeddings through a combination of novel representations: entailment vectors, entailment weights, and a consistency vector. We illustrate our approach on classification tasks of supervised learning. Key contributions of the study include: (i) an effective knowledge graph embedding approach for stream ontologies, and (ii) a generic consistent prediction framework with integrated knowledge graph embeddings for dealing with concept drifts. The experiments have shown that our approach provides accurate predictions towards air quality in Beijing and bus delay in Dublin with real world ontology streams. (C) 2021 Elsevier B.V. All rights reserved.

机译：数据流学习已经很大程度上研究了从连续和快速数据记录中提取知识结构。随着数据在时间的发展中，其潜在的知识受到许多挑战。概念漂移，1作为流学习界的核心挑战之一，被描述为数据统计特性的变化随着时间的推移，导致大多数机器学习模型在不可预见的方式中随着时间的变化而减少准确。这尤其有问题，因为数据的演变可能导出知识的戏剧性变化。我们通过研究语义Web中的数据流的语义表示来解决这个问题，即本体流。这些流是用本体论词汇注释的数据排序的数据序列。特别是我们利用了在本体流中编码的三个知识水平，以处理概念漂移：i）从流动力学中获得的新颖知识，ii）知识变化和进化的意义，以及知识进化的一致性。这些知识通过新颖的表示：征集向量，征集权重和一致性矢量编码为知识图形嵌入。我们说明了我们对监督学习的分类任务的方法。该研究的主要贡献包括：（i）流体本体的有效知识图形嵌入方法，以及（ii）一个通用一致的预测框架，具有综合知识图形嵌入的嵌入，用于处理概念漂移。实验表明，我们的方法为北京和公共汽车延误提供了准确的预测，在都柏林与真实世界本体流。（c）2021 Elsevier B.v.保留所有权利。

Knowledge graph embeddings for dealing with concept drift in machine learning

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