文本数据集成

摘要

数据以多种形态存在。从浅层视角看，可将其划分为结构化（如关系型数据、键值对）与非结构化（如文本、图像）两种形式。迄今为止，机器在处理遵循精确模式的结构化数据方面已表现卓越。然而数据的异构性对多类别数据的有效存储与处理提出了重大挑战。作为数据工程流程的关键环节，数据集成技术通过整合异构数据源并为终端用户提供统一数据访问接口来解决这一难题。目前大多数数据集成系统主要侧重于结构化数据源的融合。但非结构化数据（即自由文本）同样蕴含着大量待挖掘的知识宝藏。因此，本章将首先论证文本数据整合的必要性，继而系统阐述其面临的挑战、当前技术前沿及待解难题。

English

Data comes in many forms. From a shallow perspective, they can be viewed as being either in structured (e.g., as a relation, as key-value pairs) or unstructured (e.g., text, image) formats. So far, machines have been fairly good at processing and reasoning over structured data that follows a precise schema. However, the heterogeneity of data poses a significant challenge on how well diverse categories of data can be meaningfully stored and processed. Data Integration, a crucial part of the data engineering pipeline, addresses this by combining disparate data sources and providing unified data access to end-users. Until now, most data integration systems have leaned on only combining structured data sources. Nevertheless, unstructured data (a.k.a. free text) also contains a plethora of knowledge waiting to be utilized. Thus, in this chapter, we firstly make the case for the integration of textual data, to later present its challenges, state of the art and open problems.

文本数据集成

Text Data Integration

摘要

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