Collective Machine Learning for Semantic Data Interpretation

用于语义数据解释的集体机器学习

• 批准号: RGPIN-2017-06320
• 负责人: Guo, Yuhong
• 金额: $ 3.06万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711813
• 关键词: Collective; Machine; Learning; Semantic; Data

This research will investigate collective machine learning algorithms that combine information from heterogeneous sources to automatically induce semantic interpreters of complex data. Two emerging trends motivate this research. First, in the era of big data there are an increasing number of freely available data sources that are relevant to any particular interpretation problem. Although such data sources vary in size and annotation coverage, their union can be leveraged to reduce the annotation cost required to achieve competence in a target interpretation task. Second, the growing success of machine learning has increased the ambition to move beyond learning simple classification models to adapting related semantic predictors across complex output categories. The primary challenge of performing collective learning across complex output spaces lies in the heterogeneity of data sources, in terms of the different input features recorded, different output annotations captured, and different prediction tasks considered. To address this fundamental challenge, this research will develop novel representation learning algorithms that can uncover the shared structure underlying different data sets and different output targets. If successful, this research program will overcome the boundaries of traditional machine learning and data analysis systems, and provide new tools for heterogeneous data analysis that address a significant need in the modern context of big data. Moreover, this research will also dramatically increase the autonomy and robustness of semantic data analysis systems while reducing, and in some cases eliminating, their dependence on human guidance. The proposed research program has both fundamental and applied aspects and is expected to contribute progress in both respects. In particular, this research will not only contribute new mathematical and algorithmic developments in machine learning and data analysis research, it will also broaden the applicability of automated data analysis systems to a wider range of natural language processing, computer vision, bioinformatics, social and commercial data analysis problems. The resulting methods will be applicable to broad classes of heterogeneous data collected by governments, industry, organizations or individuals, and will significantly reduce the dependence on domain expertise in developing useful data interpretation systems.

这项研究将研究集体机器学习算法，这些算法结合了来自异质源的信息，以自动诱导复杂数据的语义解释者。 两种新兴趋势激发了这项研究。 首先，在大数据时代，与任何特定解释问题有关的可自由可用数据源越来越多。 尽管此类数据源的大小和注释覆盖率各不相同，但可以利用它们的工会来减少在目标解释任务中获得能力所需的注释成本。 其次，机器学习的越来越多的成功增加了超越学习简单分类模型以适应复杂输出类别相关的语义预测变量的雄心。 跨复杂输出空间进行集体学习的主要挑战在于数据源的异质性，根据记录的不同输入功能，捕获的不同输出注释以及所考虑的不同预测任务。 为了应对这一基本挑战，这项研究将开发新的表示学习算法，这些学习算法可以揭示不同数据集和不同输出目标的共享结构。 如果成功，该研究计划将克服传统的机器学习和数据分析系统的界限，并为异质数据分析提供新的工具，以满足现代大数据背景下的重要需求。 此外，这项研究还将大大提高语义数据分析系统的自主性和鲁棒性，同时减少，在某些情况下消除其对人类指导的依赖。 拟议的研究计划既有基本和应用方面，又有望在这两个方面做出进展。 特别是，这项研究不仅将在机器学习和数据分析研究中贡献新的数学和算法发展，还将扩大自动数据分析系统对更广泛的自然语言处理，计算机视觉，生物信息学，社交和商业的适用性数据分析问题。 所得的方法将适用于政府，行业，组织或个人收集的广泛类别的异质数据，并将大大降低对开发有用数据解释系统的领域专业知识的依赖。