论文标题
随着时间的流逝，有关异质归因的实体的监督学习
Supervised learning on heterogeneous, attributed entities interacting over time
论文作者
论文摘要
大多数身体或社会现象可以由组成实体以各种方式彼此及其环境相互作用的本体论代表。此外，这些实体可能是异质的，并且归因于在及时动态发展的特征，以响应其连续的相互作用。为了将机器学习应用于此类实体，例如出于分类目的，因此需要以系统的方式将交互作用集成到功能工程中。该建议显示，到此为止，当前的机器学习状态如何保持不足，需要通过时空的全面功能工程范式来增强。
Most physical or social phenomena can be represented by ontologies where the constituent entities are interacting in various ways with each other and with their environment. Furthermore, those entities are likely heterogeneous and attributed with features that evolve dynamically in time as a response to their successive interactions. In order to apply machine learning on such entities, e.g., for classification purposes, one therefore needs to integrate the interactions into the feature engineering in a systematic way. This proposal shows how, to this end, the current state of graph machine learning remains inadequate and needs to be be augmented with a comprehensive feature engineering paradigm in space and time.
