First-order learning for Web mining

Abstract

Abstract. We present compelling evidence that the World Wide Web is a domain in which applications can benefit from using first-order learning methods, since the graph structure inherent in hypertext naturally lends itself to a relational representation. We demonstrate strong advantages for two applications -learning classifiers for Web pages, and learning rules to discover relations among pages. I n t r o d u c t i o nIn recent years, there has been a large body of research centered around the topic of learning first-order representations. Although these representations can succinctly represent a much larger class of concepts than propositional representations, to date there have been only a few problem domains in which first-order representations have demonstrated a decided advantage over propositional representations. The graph-like structure provided by pages on the World Wide Web is one domain that seems natural for first-order representation, yet has not been previously studied in this context. Cohen [1] has used first-order methods for text classification, but the focus was on finding relations between words rather than between documents. The lower half of Figure 1 illustrates the notion of the Web as a directed graph where pages correspond to the nodes in the graph and hyperlinks correspond to edges. Using this representation, we address two types of learning tasks: learning definitions of page classes, and learning definitions of relations between pages. In contrast to related efforts on similar Web tasks, our work focuses on learning concepts which represent relational generalizations of the inherent graph structure.Our work on these two learning tasks has been conducted as part of a larger effort aimed at developing methods for automatically constructing knowledge bases by extracting information from the Web [2]. Given an ontology defining classes and relations of interest, such as that shown in the top half of Figure 1, along with training examples consisting of labeled Web pages, the system learns a set of information extractors for the classes and relations in the ontology, and then populates a knowledge base by exploring the Web. The task of recognizing class instances can be framed as a page-classification task. For example, we can *

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