There have been major advances on automatically constructing large knowledge bases by extracting relational facts from Web and text sources. However, the world is dynamic: periodic events like sports competitions need to be interpreted with their respective timepoints, and facts such as coaching a sports team, holding political or business positions, and even marriages do not hold forever and should be augmented by their respective timespans. This paper addresses the problem of automatically harvesting temporal facts with such extended time-awareness. We employ pattern-based gathering techniques for fact candidates and construct a weighted pattern-candidate graph. Our key contribution is a system called PRAVDA based on a new kind of label propagation algorithm with a judiciously designed loss function, which iteratively processes the graph to label good temporal facts for a given set of target relations. Our experiments with online news and Wikipedia articles demonstrate the accuracy of this method.
Web archives preserve the history of borndigital content and offer great potential for sociologists, business analysts, and legal experts on intellectual property and compliance issues. Data quality is crucial for these purposes. Ideally, crawlers should gather coherent captures of entire Web sites, but the politeness etiquette and completeness requirement mandate very slow, long-duration crawling while Web sites undergo changes. This paper presents the SHARC framework for assessing the data quality in Web archives and for tuning capturing strategies toward better quality with given resources. We define data quality measures, characterize their properties, and develop a suite of quality-conscious scheduling strategies for archive crawling. Our framework includes single-visit and visit-revisit crawls. Single-visit crawls download every page of a site exactly once in an order that aims to minimize the "blur" in capturing the site. Visit-revisit strategies revisit pages after their initial downloads to check for intermediate changes.The revisiting order aims to maximize the "coherence" of the site capture(number pages that did not change during the capture). The quality notions of blur and coherence are formalized in the paper. Blur is a stochastic notion that reflects the expected number of page changes that a time-travel access to a site capture would accidentally see, instead of the ideal view of a instantaneously captured, "sharp" site. Coherence is a deterministic quality measure that counts the number of unchanged and thus coherently captured pages in a site snapshot. Strategies that aim to either minimize blur or maximize coherence are based on prior knowledge of or predictions for the change rates of individual pages. Our framework includes fairly accurate classifiers for change predictions. All strategies are fully implemented in a testbed and shown to be effective by experiments with both synthetically generated sites and a periodic crawl series for different Web sites.
Web archives preserve the history of Web sites and have high long-term value for media and business analysts. Such archives are maintained by periodically re-crawling entire Web sites of interest. From an archivist's point of view, the ideal case to ensure highest possible data quality of the archive would be to "freeze" the complete contents of an entire Web site during the time span of crawling and capturing the site. Of course, this is practically infeasible. To comply with the politeness specification of a Web site, the crawler needs to pause between subsequent http requests in order to avoid unduly high load on the site's http server. As a consequence, capturing a large Web site may span hours or even days, which increases the risk that contents collected so far are incoherent with the parts that are still to be crawled. This paper introduces a model for identifying coherent sections of an archive and, thus, measuring the data quality in Web archiving. Additionally, we present a crawling strategy that aims to ensure archive coherence by minimizing the diffusion of Web site captures. Preliminary experiments demonstrate the usefulness of the model and the effectiveness of the strategy.
Large collections of digital knowledge have become valuable assets for search and recommendation applications. The taxonomic type systems of such knowledge bases are often highly heterogeneous, as they reflect different cultures, languages, and intentions of usage. We present a novel method to the problem of multi-cultural knowledge alignment, which maps each node of a source taxonomy onto a ranked list of most suitable nodes in the target taxonomy. We model this task as combinatorial optimization problems, using integer linear programming and quadratic programming. The quality of the computed alignments is evaluated, using large heterogeneous taxonomies about book categories.
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