Triplify: light-weight linked data publication from relational databases
Proceedings of the 18th international conference on World wide web
Bringing your dead links back to life: a comprehensive approach and lessons learned
Proceedings of the 20th ACM conference on Hypertext and hypermedia
MaSiMe: A Customized Similarity Measure and Its Application for Tag Cloud Refactoring
OTM '09 Proceedings of the Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: ADI, CAMS, EI2N, ISDE, IWSSA, MONET, OnToContent, ODIS, ORM, OTM Academy, SWWS, SEMELS, Beyond SAWSDL, and COMBEK 2009
DSNotify: handling broken links in the web of data
Proceedings of the 19th international conference on World wide web
A more specific events classification to improve crawling techniques
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems
MultiCrawler: a pipelined architecture for crawling and indexing semantic web data
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
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Today the popularity of data quality is increasing in linked data, and its changes are being annotated. Linked data consuming applications need to be aware of changes in a dataset. Changes such as update, remove or creation links may occur for a time so it is necessary to detect them to update local data dependencies where this annotation is made by detecting changes systems. Updated or removed links can be detected using a syntactic change similarity measure, and it is simply done using the Levenshtein distance measure. However, a specific event classification of updated event and removed event, which is created by developing detecting changes systems, does not exist based on content analysis. A Hash number comparison approach over each linked data resource is developed to create a more specific classification of the initial updated and removed event. It is computed over RDF triples and is used to enrich the resources, annotating the new classification of the initial updated event and removed event. Annotations on the modification time are made in linked data resource, and making an average time study about when these specific events change, and using a Crawling Window to analyze a portion of Linked Data graph could be improved the crawling techniques for a domain. In this paper a study based in depth of child nodes analyzed in Linked Data graph is made to estimate what is the Crawling Window design more efficient.