The experimental results show that the FSM wrapper performs well even with small training data, while the CNN-based models require more training pages to achieve the same effectiveness but are more efficient with GPU support. We exploit unique mandatory templates to improve the FSM-based wrapper, and proposed two convolutional neural network (CNN)-based models for sequence-labeling. We consider two approaches for wrapper generation: schema-guided finite-state machine (FSM) approaches and data-driven machine learning (ML) approaches. In this paper, we argue that wrapper generation for unsupervised web data extraction is as important as supervised wrapper induction because the generated wrappers could work more efficiently without sophisticated analysis during testing. In fact, most unsupervised web data extraction ignores wrapper generation because they could work alone without any supervision. Not much attention has been paid to extraction efficiency. However, most research focuses on extraction effectiveness.
Many approaches have been proposed, with either labeled training examples (supervised) or annotation-free training pages (unsupervised). We discuss research challenges for extending our approach to a general method applicable to a yet larger number of cases.ĭiv>Web data extraction is a key component in many business intelligence tasks, such as data transformation, exchange, and analysis. This system works in the vast majority of test cases and produces very fast and extremely resource-efficient wrappers. We present the first algorithm and system performing such an automated translation on suitably restricted types of web sites. In this paper, we demonstrate the principal feasibility of automatically translating browser-based wrappers into "browserless" wrappers. However, creating and maintaining browserless wrappers of high precision requires specialists, and is prohibitively labor-intensive at scale. In contrast, it is magnitudes more resource-efficient to use a "browserless" wrapper which directly accesses a web server through HTTP requests, and takes the desired data directly from the raw replies. Such scrapers (or wrappers) are therefore expensive to execute, in terms of time and network traffic. Most modern web scrapers use an embedded browser to render web pages and to simulate user actions.
0 kommentar(er)
