Approximate string-matching with q-grams and maximal matches
Theoretical Computer Science - Selected papers of the Combinatorial Pattern Matching School
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
Detecting similarities in ontologies with the SOQA-SimPack toolkit
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
MultiCrawler: a pipelined architecture for crawling and indexing semantic web data
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Proceedings of the 1st International Workshop on Linked Web Data Management
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Nowadays the popularity of data quality is increasing notably in linked data. Linked data consuming applications need to be aware that changes in a dataset. Changes such as update, remove or creation links may occur for a time so 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 can be done simply using the Levenshtein distance measure. However, a specific event subclassification of updated event and removed event, which is created by detecting changes systems developed, does not exist based on content analysis. A semantic signature and Maximum Similarity Measure (MaSiMe) combination approach is developed to create a more specific subclassification of the initial updated and removed event when its meaning has been changed. It is used to enrich the resources, annotating the new subclassification of the initial updated event and removed event, and will be annotated the author who created this annotation, adding provenance information. Annotations on the modification time are made in linked data resource, and making an average time study about when these specific events changes, could be improved the crawling techniques for a domain.