|
学习URI选择标准以改进链接开放数据的爬行
Learning URI Selection Criteria to Improve the Crawling of Linked Open Data |
|
| |
| 课程网址: |
http://videolectures.net/eswc2019_huang_learning_uri/
|
| 主讲教师: |
Hai Huang
|
| 开课单位: |
蓝色海岸大学
|
| 开课时间: |
2019-09-19
|
| 课程语种: |
英语
|
| 中文简介: |
随着链接开放数据网络的发展，对云的爬行问题变得越来越重要。与普通Web爬虫不同，链接数据爬虫执行选择以集中收集Web上链接的RDF（包括RDFa）数据。从吞吐量和覆盖率的角度来看，给定一个新发现的目标URI，链接数据爬虫的关键问题是决定此URI是否可能取消对RDF数据源的引用，因此值得下载它所指向的表示。当前的解决方案采用启发式规则来过滤不相关的URI。不幸的是，当启发式过于严格时，这会阻碍爬行的覆盖范围。本文通过预测新发现的URI是否会导致RDF数据源，提出并比较了在Web上爬行链接数据的学习策略的方法。我们详细介绍了用于预测相关性的特征和我们评估的方法，包括FTRL近端在线学习算法的一种有前景的适应性。我们通过广泛的实验比较了几个选项，包括现有的爬行器作为基线方法，以评估其效果。
|
| 课程简介: |
As the Web of Linked Open Data is growing the problem of crawling that cloud becomes increasingly important. Unlike normal Web crawlers, a Linked Data crawler performs a selection to focus on collecting linked RDF (including RDFa) data on the Web. From the perspectives of throughput and coverage, given a newly discovered and targeted URI, the key issue of Linked Data crawlers is to decide whether this URI is likely to dereference into an RDF data source and therefore it is worth downloading the representation it points to. Current solutions adopt heuristic rules to filter irrelevant URIs. Unfortunately, when the heuristics are too restrictive this hampers the coverage of crawling. In this paper, we propose and compare approaches to learn strategies for crawling Linked Data on the Web by predicting whether a newly discovered URI will lead to an RDF data source or not. We detail the features used in predicting the relevance and the methods we evaluated including a promising adaptation of FTRL-proximal online learning algorithm. We compare several options through extensive experiments including existing crawlers as baseline methods to evaluate their efficacy.
|
| 关 键 词: |
大数据挖掘; 学习URI选择标准; Web爬虫; 改进链接开放数据的爬行; RDF数据源; 语义网
|
| 课程来源: |
视频讲座网
|
| 数据采集: |
2022-09-20：cyh
|
| 最后编审: |
2022-09-21：cyh
|
| 阅读次数: |
8
|
|
图 书 馆