Knowledge capture from multiple online sources with the extensible web retrieval toolkit (eWRT)

A. Weichselbraun, A. Scharl, Heinz-Peter Lang
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引用次数: 1

Abstract

Knowledge capture approaches in the age of massive Web data require robust and scalable mechanisms to acquire, consolidate and pre-process large amounts of heterogeneous data, both unstructured and structured. This paper addresses this requirement by introducing the Extensible Web Retrieval Toolkit (eWRT), a modular Python API for retrieving social data from Web sources such as Delicious, Flickr, Yahoo! and Wikipedia. eWRT has been released as an open source library under GNU GPLv3. It includes classes for caching and data management, and provides low-level text processing capabilities including language detection, phonetic string similarity measures, and string normalization.
使用可扩展web检索工具包(eWRT)从多个在线源获取知识
在海量Web数据时代,知识捕获方法需要健壮且可伸缩的机制来获取、整合和预处理大量异构数据,包括非结构化和结构化数据。本文通过引入可扩展Web检索工具包(Extensible Web Retrieval Toolkit, eWRT)来解决这一需求,eWRT是一个模块化的Python API,用于从Delicious、Flickr、Yahoo!和维基百科。eWRT已经作为一个开源库在GNU GPLv3下发布。它包括用于缓存和数据管理的类,并提供低级文本处理功能,包括语言检测、语音字符串相似性度量和字符串规范化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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