Adaptive Focused Crawling of Linked Data View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Ran Yu , Ujwal Gadiraju , Besnik Fetahu , Stefan Dietze

ABSTRACT

Given the evolution of publicly available Linked Data, crawling and preservation have become increasingly important challenges. Due to the scale of available data on the Web, efficient focused crawling approaches which are able to capture the relevant semantic neighborhood of seed entities are required. Here, determining relevant entities for a given set of seed entities is a crucial problem. While the weight of seeds within a seed list vary significantly with respect to the crawl intent, we argue that an adaptive crawler is required, which considers such characteristics when configuring the crawling and relevance detection approach. To address this problem, we introduce a crawling configuration, which considers seed list-specific features as part of its crawling and ranking algorithm. We evaluate it through extensive experiments in comparison to a number of baseline methods and crawling parameters. We demonstrate that, configurations which consider seed list features outperform the baselines and present further insights gained from our experiments. More... »

PAGES

554-569

References to SciGraph publications

  • 2013. Combining a Co-occurrence-Based and a Semantic Measure for Entity Linking in THE SEMANTIC WEB: SEMANTICS AND BIG DATA
  • 2007. DBpedia: A Nucleus for a Web of Open Data in THE SEMANTIC WEB
  • 2015. Improving Entity Retrieval on Structured Data in THE SEMANTIC WEB - ISWC 2015
  • 1953-03. A new status index derived from sociometric analysis in PSYCHOMETRIKA
  • Book

    TITLE

    Web Information Systems Engineering – WISE 2015

    ISBN

    978-3-319-26189-8
    978-3-319-26190-4

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-26190-4_37

    DOI

    http://dx.doi.org/10.1007/978-3-319-26190-4_37

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1003210262


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