上下文感知的动态对象关系建模

Kentaroh Toyoda, Rachel Gan Kai Ying, Tan Puay Siew, Allan Neng Sheng Zhang
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引用次数: 0

摘要

寻找数据中的关系对于对象建模至关重要。然而,现有的方法通常关注使用语义和本体的预定义静态关系,当我们对数据源(如日志文件)中出现的对象之间的动态关系感兴趣时,这是不合适的。在本文中,我们提出了两种新的方法来动态提取出现在异构数据源中的上下文关系。我们的方法检测给定数据源中的上下文(例如时间和位置),并基于检测到的上下文量化对象之间的相似性。具体来说,我们的方法包括(i)一种具有精心设计的判别特征的快速准确的上下文检测方法和(ii)考虑上下文的相似性度量。我们用一个开放的数据集来评估我们的上下文检测方法,以显示其检测的准确性和速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Context-Aware Dynamic Object Relationship Modeling
Finding relationships in the data is essential for object modeling. However, existing methods generally focus on pre-defined static relationships using semantics and ontology, which is inappropriate when we are interested in dynamic relationships between objects that appear in data sources (e.g. log files). In this paper, we propose two novel methods to dynamically extract contextual relationships that appear in heterogeneous data sources. Our method detects contexts (e.g. time and location) in a given data source and quantifies the similarities between objects based on the detected contexts. Specifically, our methods consist of (i) a fast and accurate context detection method with carefully engineered discriminative features and (ii) a similarity measure that takes into account contexts. We evaluated our context detection method with an open dataset to show its detection accuracy and speed.
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