End-to-end Extraction and Curation of Large RDF Repositories

大型 RDF 存储库的端到端提取和管理

• 批准号: 543961-2019
• 负责人: Ilyas, Ihab
• 金额: $ 11.82万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 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=698919
• 关键词: End; end; Extraction; Curation; Large

Enterprises are building massive storage repositories (often referred to as data lakes) that hold data in its native format (text, JSON, CSV files, relational databases, etc). The aim is to increase the amount of usable information by allowing fast acquisition of data from many sources as soon as they are available, instead of waiting for traditional ETL (extract-transform-load) stacks to curate the data and integrate it in a trustworthy warehouse. Unfortunately, with the massive number of external data sources, and the increasing heterogeneity in the formats of these sources (text, feeds, tweets, posts, blogs, events, etc.), handling unstructured and semi-structured data in a unified framework becomes key. In this project, we build an end-to-end system for handling (semi-) structured and unstructured in a unified way, in an open-source test-bed we call DSTLR (short for the data distiller). The DSTLR project combines advances in natural language processing (NLP) and information extraction (IE) particularly with deep learning, data cleaning, and managing RDF data to enable treating all types of data in a common format that allows for truth finding, question answering and structured data enrichment. The main objective of the proposal is to identify an investigate the technical challenges in building such a system including: (1) identifying the necessary provenance and lineage information to describe the context of the IE system; (2) efficient propagation of such information across the modules; (3) leveraging this rich extraction context in cleaning the extracted information; and (4) exploring novel ways to link the cleaning of the output to the information extraction subsystem in an iterative loop, with judicious involvement of humans in the whole life cycle. For this, the proposal identifies multiple concrete research tasks and expected outcome of these investigations.

企业正在构建大规模存储库（通常称为数据湖），以其原生格式（文本、JSON、CSV文件、关系数据库等）保存数据。其目的是通过允许从许多来源快速获取数据来增加可用信息的数量，而不是等待传统的ETL（提取-转换-加载）堆栈来管理数据并将其集成到值得信赖的仓库中。不幸的是，随着大量外部数据源的出现，以及这些数据源格式（文本、提要、推文、帖子、博客、事件等）的异质性日益增加，在统一框架中处理非结构化和半结构化数据成为关键。在这个项目中，我们构建了一个端到端的系统，以统一的方式处理（半）结构化和非结构化，在一个开源的测试平台，我们称之为DSTLR（数据蒸馏器的缩写）。DSTLR项目结合了自然语言处理（NLP）和信息提取（IE）的进步，特别是深度学习，数据清理和管理RDF数据，以实现以通用格式处理所有类型的数据，从而允许真相发现，问题回答和结构化数据丰富。该提案的主要目标是确定和调查构建这样一个系统的技术挑战，包括：（1）确定必要的起源和血统信息来描述IE系统的上下文;（2）跨模块有效传播这些信息;（3）利用这种丰富的提取上下文来清理提取的信息;以及（4）探索新的方法，将输出的清理与迭代循环中的信息提取子系统联系起来，并在整个生命周期中明智地参与人类。为此，该提案确定了多项具体的研究任务和这些调查的预期成果。