Electronic document addressing: dealing with change
ACM Computing Surveys (CSUR)
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IEEE Transactions on Knowledge and Data Engineering
Introduction to Information Retrieval
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Pattern Recognition, Fourth Edition
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Triplify: light-weight linked data publication from relational databases
Proceedings of the 18th international conference on World wide web
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
DBpedia - A crystallization point for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
On Detecting High-Level Changes in RDF/S KBs
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Discovering and Maintaining Links on the Web of Data
ISWC '09 Proceedings of the 8th International Semantic Web Conference
A versioning and evolution framework for RDF knowledge bases
PSI'06 Proceedings of the 6th international Andrei Ershov memorial conference on Perspectives of systems informatics
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
DSNotify - A solution for event detection and link maintenance in dynamic datasets
Web Semantics: Science, Services and Agents on the World Wide Web
Using Metadata to Maintain Link Integrity for Linked Data
ITHINGSCPSCOM '11 Proceedings of the 2011 International Conference on Internet of Things and 4th International Conference on Cyber, Physical and Social Computing
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In the web of data, linked datasets are changed over time. These changes include updating on features and address of entities. The address change in RDF entities causes their corresponding links to be broken. Broken link is one of the major obstacles that the web of data is facing. Most approaches to solve this problem attempt to fix broken links at the destination point. These approaches have two major problems: a single point of failure; and reliance on the destination data source. In this paper, we introduce a method for fixing broken links which is based on the source point of links, and discover the new address of the detached entity. To this end, we introduce two datasets, which we call 'superior' and 'inferior'. Through these datasets, our method creates an exclusive graph structure for each entity that needs to be observed over time. This graph is used to identify and discover the new address of the detached entity. Afterward, the most similar entity, which is candidate for the detached entity, is deduced and suggested by the algorithm. The proposed model is evaluated with DBpedia dataset within the domain of 'person' entities. The result shows that most of the broken links, which had referred to a 'person' entity in DBpedia, had been fixed correctly.