Items from XML and Web Data track
Number of items: 6.
and Ma, Z. M.
and Yan, Li Answering Approximate Queries over Autonomous Web Databases.
To deal with the problem of empty or too little answers returned from a Web database in response to a user query, this paper proposes a novel approach to provide relevant and ranked query results. Based on the user original query, we speculate how much the user cares about each specified attribute and assign a corresponding weight to it. This original query is then rewritten as an approximate query by relaxing the query criteria range. The relaxation order of all specified attributes and the relaxed degree on each specified attribute are varied with the attribute weights. For the approximate query results, we generate users’ contextual preferences from database workload and use them to create a priori orders of tuples in an off-line preprocessing step. Only a few representative orders are saved, each corresponding to a set of contexts. Then, these orders and associated contexts are used at query time to expeditiously provide ranked answers. Results of a preliminary user study demonstrate that our query relaxation and results ranking methods can capture the user’s preferences effectively. The efficiency and effectiveness of our approach is also demonstrated by experimental result.
and Roth, Dan Extracting Article Text from the Web with Maximum Subsequence Segmentation.
Much of the information on the Web is found in articles from online news outlets, magazines, encyclopedias, review collections, and other sources. However, extracting this content from the original HTML document is complicated by the large amount of less informative and typically unrelated material such as navigation menus, forms, user comments, and ads. Existing approaches tend to be either brittle and demand significant expert knowledge and time (manual or tool-assisted generation of rules or code), necessitate labeled examples for every different page structure to be processed (wrapper induction), require relatively uniform layout (template detection), or, as with Visual Page Segmentation (VIPS), are computationally expensive. We introduce maximum subsequence segmentation, a method of global optimization over token-level local classifiers, and apply it to the domain of news websites. Training examples are easy to obtain, both learning and prediction are linear time, and results are excellent (our semi-supervised algorithm yields an overall F1score of 97.947%), surpassing even those produced by VIPS with a hypothetical perfect block-selection heuristic. We also evaluate against the recent CleanEval shared task with surprisingly good cross-task performance cleaning general web pages, exceeding the top “text-only” score (based on Levenshtein distance), 87.8% versus 84.1%.
and Tatemura, Junichi
and Hsiung, Wang-Pin
and Sawires, Arsany
and Moser, Louise E. Extracting Data Records from the Web Using Tag Path Clustering.
Fully automatic methods that extract lists of objects from the Web have been studied extensively. Record extraction, the ﬁrst step of this object extraction process, identiﬁes a set of Web page segments, each of which represents an individual object (e.g., a product). State-of-the-art methods suffice for simple search, but they often fail to handle more complicated or noisy Web page structures due to a key limitation – their greedy manner of identifying a list of records through pairwise comparison (i.e., similarity match) of consecutive segments. This paper introduces a new method for record extraction that captures a list of objects in a more robust way based on a holistic analysis of a Web page. The method focuses on how a distinct tag path appears repeatedly in the DOM tree of the Web document. Instead of comparing a pair of individual segments, it compares a pair of tag path occurrence patterns (called visual signals ) to estimate how likely these two tag paths represent the same list of objects. The paper introduces a similarity measure that captures how closely the visual signals appear and interleave. Clustering of tag paths is then performed based on this similarity measure, and sets of tag paths that form the structure of data records are extracted. Experiments show that this method achieves higher accuracy than previous methods.
and Wang Ling, Tok
and Xu, Liang
and Bao, Zhifeng Performing Grouping and Aggregate Functions in XML Queries.
Since more and more business data are represented in XML format, there is a compelling need of supporting analytical operations in XML queries. Particularly, the latest version of XQuery proposed by W3C, XQuery 1.1, introduces a new construct to explicitly express grouping operation in FLWOR expression. Existing works in XML query processing mainly focus on physically matching query structure over XML document. Given the explicit grouping operation in a query, how to efficiently compute grouping and aggregate functions over XML document is not well studied yet. In this paper, we extend our previous XML query processing algorithm, VERT, to efficiently perform grouping and aggregate function in queries. The main technique of our approach is introducing relational tables to index values. Query pattern matching and aggregation computing are both conducted with table indices. We also propose two semantic optimizations to further improve the query performance. Finally we present experimental results to validate the efficiency of our approach, over other existing approaches.
and Shivakumar, Narayanan Sitemaps: Above and Beyond the Crawl of Duty.
Comprehensive coverage of the public web is crucial to web search engines. Search engines use crawlers to retrieve pages and then discover new ones by extracting the pages’ outgoing links. However, the set of pages reachable from the publicly linked web is estimated to be signiﬁcantly smaller than the invisible web , the set of documents that have no incoming links and can only be retrieved through web applications and web forms. The Sitemaps protocol is a fast-growing web protocol supported jointly by major search engines to help content creators and search engines unlock this hidden data by making it available to search engines. In this paper, we perform a detailed study of how “classic” discovery crawling compares with Sitemaps, in key measures such as coverage and freshness over key representative websites as well as over billions of URLs seen at Google. We observe that Sitemaps and discovery crawling complement each other very well, and offer different tradeoffs. Categories and Subject Descriptors: H.3.3: Information Search and Retrieval. General Terms: Experimentation, Algorithms. Keywords: search engines, crawling, sitemaps, metrics, quality.
and Pilman, Markus
and Florescu, Daniela
and Kossmann, Donald
and Kraska, Tim
and McBeath, Darin XQuery in the Browser.
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