A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
Automatic document metadata extraction using support vector machines
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
Data integration: the teenage years
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
iDM: a unified and versatile data model for personal dataspace management
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Enhancing text clustering by leveraging Wikipedia semantics
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
The VLDB Journal — The International Journal on Very Large Data Bases
Exploiting Wikipedia as external knowledge for document clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Leveraging personal metadata for Desktop search: The Beagle++ system
Web Semantics: Science, Services and Agents on the World Wide Web
Predicate-based indexing for desktop search
The VLDB Journal — The International Journal on Very Large Data Bases
Web-based citation parsing, correction and augmentation
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
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Researchers maintain bibliographies and extensive sets of PDF files of scholarly publications on their desktop. The lack of proper metadata of downloaded PDFs makes this task a tedious one. With PDFMeat we present a solution to automatically determine publication metadata for scholarly papers within the user's desktop environment and link the metadata to the files. PDFMeat effectively matches local full texts to an online repository. In an evaluation for more than 2.000 diverse PDF files it worked highly reliable and showed excellent accuracy of up to 98 percent. We demonstrate PDFMeat for different sets of papers, highlighting the semantic integration and use of the retrieved metadata within the file browser of the desktop environment.