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dc.contributor.authorBernstein, Abraham-
dc.contributor.authorClearwater, Scott-
dc.contributor.authorProvost, Foster-
dc.date.accessioned2005-11-10T14:51:23Z-
dc.date.available2005-11-10T14:51:23Z-
dc.date.issued2003-01-01-
dc.identifier.urihttp://hdl.handle.net/2451/14130-
dc.description.abstractThis paper addresses the classification of linked entities. We introduce a relational vector (VS) model (in analogy to the VS model used in information retrieval) that abstracts the linked structure, representing entities by vectors of weights. Given labeled data as background knowledge training data, classification procedures can be defined for this model, including a straightforward, "direct" model using weighted adjacency vectors. Using a large set of tasks from the domain of company affiliation identification, we demonstrate that such classification procedures can be effective. We then examine the method in more detail, showing that as expected the classification performance correlates with the- relational auto correlation of the data set. We then turn the tables and use the relational VS scores as a way to analyze/visualize the relational autocorrelation present in a complex linked structure. The main contribution of the paper 1s to introduce the relational VS model as a potentially useful addition to the toolkit for relational data mining. It could provide useful constructed features for domains with low to moderate relational autocorrelation; it may be effective by itself for domains with high levels of relational autocorrelation, and it provides a useful abstraction for analyzing the properties of linked data.en
dc.format.extent3392728 bytes-
dc.format.mimetypeapplication/pdf-
dc.languageEnglishEN
dc.language.isoen_US-
dc.publisherStern School of Business, New York Universityen
dc.relation.ispartofseriesIS-03-02-
dc.subjectRelational Data Miningen
dc.subjectVector-space modelsen
dc.subjectlndustry Classificationen
dc.titleThe Relational Vector-space Modelen
dc.typeWorking Paperen
dc.description.seriesInformation Systems Working Papers SeriesEN
Appears in Collections:IOMS: Information Systems Working Papers

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