A Machine Learning Approach to Linking FOAF Instances

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dc.contributor.authorSleeman, Jennifer
dc.contributor.authorFinin, Tim
dc.date.accessioned2018-11-12T16:42:04Z
dc.date.available2018-11-12T16:42:04Z
dc.date.issued2010-01-23
dc.descriptionProceedings of the AAAI Spring Symposium on Linked Data Meets Artificial Intelligenceen_US
dc.description.abstractThe friend of a friend (FOAF) vocabulary is widely used on the Web to describe individual people and their properties. Since FOAF does not require a unique ID for a person, it is not clear when two FOAF agents should be linked as coreferent, i.e., denote the same person in the world. One approach is to use the presence of inverse functional properties (e.g., foaf:mbox) as evidence that two individuals are the same. Another applies heuristics based on the string similarity of values of FOAF properties such as name and school as evidence for or against co-reference. Performance is limited, however, by many factors: non-semantic string matching, noise, changes in the world, and the lack of more sophisticated graph analytics. We describe a supervised machine learning approach that uses features defined over pairs of FOAF individuals to produce a classifier for identifying co-referent FOAF instances. We present initial results using data collected from Swoogle and other sources and describe plans for additional analysis.en_US
dc.description.urihttps://www.google.com/url?q=https://www.aaai.org/ocs/index.php/SSS/SSS10/paper/download/1156/1464&sa=U&ved=0ahUKEwjeq8ytucDeAhVNzVMKHai-B2IQFggEMAA&client=internal-uds-cse&cx=016314354884912110518:gwmynp16xuu&usg=AOvVaw0zdE8NUHKEbKIkHQsOlweSen_US
dc.format.extent7 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.identifierdoi:10.13016/M2C24QR95
dc.identifier.citationJennifer Sleeman and Tim Finin, A Machine Learning Approach to Linking FOAF Instances, Proceedings of the AAAI Spring Symposium on Linked Data Meets Artificial Intelligence, 2010, https://www.google.com/url?q=https://www.aaai.org/ocs/index.php/SSS/SSS10/paper/download/1156/1464&sa=U&ved=0ahUKEwjeq8ytucDeAhVNzVMKHai-B2IQFggEMAA&client=internal-uds-cse&cx=016314354884912110518:gwmynp16xuu&usg=AOvVaw0zdE8NUHKEbKIkHQsOlweSen_US
dc.identifier.urihttp://hdl.handle.net/11603/11956
dc.language.isoen_USen_US
dc.publisherAAAIen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectsemantic weben_US
dc.subjectlearningen_US
dc.subjectUMBC Ebiquity Research Groupen_US
dc.subjectThe friend of a friend (FOAF)en_US
dc.titleA Machine Learning Approach to Linking FOAF Instancesen_US
dc.typeTexten_US

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