The Relational Vector-space Model and Industry Classification

This paper addresses the classification of linked entities. We introduce a relational vectnr-space (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 knowledgdtraining 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 identitication, we demonstrate that such classification proccdurcs can be effective. We then examine the method in more detail, showing that as expected the classification performance correlates with the relational autocorrelation of the data set. We then turn ~-~~~~~~ the tables and use the relational VS scores as a way to analy7.dvisualize the relational autocorrelation present in a complex linked structure. The main contribution ofthe 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 ~~~ ~ ~~ ~ autocomelation; it may be effective by itself for domains with high levels of relational autocorrelat~on, and it provides a useful abstraction for analyzing the properties of linked data. General Terms Algorilhms, Performance, Design, Experimentation