III: Small: A New Machine Learning Approach for Improved Entity Identification

现代分析依赖于数据集成，将异构数据组合到统一的存储库中，从而获得见解、服务和科学知识。数据集成的典型目标是将关于相同现实世界实体的异构数据组合成该实体的规范化表示。传统上，实体规范化方法关注结构化数据，并利用伴随数据的模式语义来生成规范化实体表示。这种对数据语义的依赖使得现有的实体规范化方法不适用于暗数据，即对应于非结构化、嘈杂和不完整数据的操作数据。该项目将开发实体规范化方法，重点关注非结构化和半结构化数据，并适用于大规模集成应用。这项工作将有助于简化目前具有挑战性的过程，即启发式地将关于同一实体的匹配信息整合到统一表示中，从而使暗数据能够更有效地用于下游分析应用程序。这项工作的重点是利用表征学习（又名特征学习）和深度学习的实体规范化技术。分布式表示与深度架构的结合已经成为分析和处理非结构化数据的事实标准。该项目将开发新的深度学习架构，用于：(1)记录链接，即聚类提供同一实体信息的非结构化数据记录；(2)数据融合，即将匹配的非结构化记录组合成底层实体的规范表示。对于记录链接，这项工作将引入新的深度学习技术，捕获多上下文特定领域的知识，以学习记录之间的语义相似性。对于数据融合，该项目将设计新的多序列到单序列编码器-解码器递归神经网络用于数据融合，特别关注不完整数据。该项目的成果有可能推动大规模数据集成方法的最新发展，以及高维、稀疏和噪声数据的机器学习方法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Modern analytics rely on data integration to combine heterogeneous data into a unified repository they can tap into for insights, services, and scientific knowledge. The typical goal of data integration is to combine heterogeneous data about the same real-world entity into a canonical representation of that entity. Traditionally, entity canonicalization methods focus on structured data and leverage the semantics of the schema accompanying the data to come up with canonical entity representations. This dependency on data semantics makes existing entity canonicalization methods inapplicable to dark data, i.e., operational data that corresponds to unstructured, noisy, and incomplete data. This project will develop entity canonicalization methods that focus on unstructured and semi-structured data and are suitable for large-scale integration applications. This work will help ease the currently challenging procedure of heuristically consolidating matching information about the same entity into unified representations and thus enable dark data to be more effectively used in downstream analytics applications.The emphasis of this work is on entity canonicalization techniques that leverage representation learning (a.k.a. feature learning) and deep learning. The combination of distributed representations with deep architectures has emerged as the de facto standard for analyzing and processing unstructured data. This project will develop new deep learning architectures for: (1) record linkage, i.e., clustering unstructured data records that provide information about the same entity; and (2) data fusion, i.e., combining matching unstructured records into a canonical representation of the underlying entity. For record linkage, this work will introduce new deep learning techniques that capture multi-context domain-specific knowledge to learn the semantic similarity between records. For data fusion, this project will design new multi-sequence to one-sequence encoder-decoder recurrent neural networks for data fusion with a particular focus on incomplete data. The outcomes of this project have the potential to advance the state-of-the-art in large scale data integration methods as well as machine learning methods for high-dimensional, sparse, and noisy data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.