We address the problem of synthesizing a robust data-extractor from a family of websites that contain the same kind of information. This problem is common when trying to aggregate information from many web sites, for example, when extracting information for a price-comparison site.

A web-crawler is a program that automatically and systematically tracks the links of a website and extracts information from its pages. Due to the different formats of websites, the crawling scheme for different sites can differ dramatically. Manually customizing a crawler for each specific site is time consuming and error-prone. Furthermore, because sites periodically change their format and presentation, crawling schemes have to be manually updated and adjusted. In this paper, we present a technique for automatic synthesis of web-crawlers from examples. The main idea is to use hand-crafted (possibly partial) crawlers for some websites as the basis for crawling other sites that contain the same kind of information. Technically, we use the data on one site to identify data on another site. We then use the identified data to learn the website structure and synthesize an appropriate extraction scheme. We iterate this process, as synthesized extraction schemes result in additional data to be used for re-learning the website structure. We implemented our approach and automatically synthesized 30 crawlers for websites from nine different categories: books, TVs, conferences, universities, cameras, phones, movies, songs, and hotels.

A modern web page is often served by running layout code on data, producing an HTML document that enhances the data with front/back matters and layout/style operations. In this paper, we consider the opposite task: separating a given web page into a data component and a layout program. This separation has various important applications: page encoding may be significantly more compact (reducing web traffic), data representation is normalized across web designs (facilitating wrapping, retrieval and extraction), and repetitions are diminished (expediting site updates and redesign).

Questions and their corresponding answers within a community based question answering (CQA) site are frequently presented as top search results forWeb search queries and viewed by millions of searchers daily. The number of answers for CQA questions ranges from a handful to dozens, and a searcher would be typically interested in the different suggestions presented in various answers for a question. Yet, especially when many answers are provided, the viewer may not want to sift through all answers but to read only the top ones. Prior work on answer ranking in CQA considered the qualitative notion of each answer separately, mainly whether it should be marked as best answer. We propose to promote CQA answers not only by their relevance to the question but also by the diversification and novelty qualities they hold compared to other answers. Specifically, we aim at ranking answers by the amount of new aspects they introduce with respect to higher ranked answers (novelty), on top of their relevance estimation. This approach is common in Web search and information retrieval, yet it was not addressed within the CQA settings before, which is quite different from classic document retrieval. We propose a novel answer ranking algorithm that borrows ideas from aspect ranking and multi-document summarization, but adapts them to our scenario. Answers are ranked in a greedy manner, taking into account their relevance to the question as well as their novelty compared to higher ranked answers and their coverage of important aspects. An experiment over a collection of Health questions, using a manually annotated gold-standard dataset, shows that considering novelty for answer ranking improves the quality of the ranked answer list.