document: Extracting data records from the web using tag path clustering Fully automatic methods that extract lists of objects from the Web have been studied extensively . Record extraction , the first step of this object extraction process , identifies a set of Web page segments , each of which represents an individual object ( e.g. , a product ) . State-of-the-art methods suffice for simple search , but they often fail to handle more complicated or noisy Web page structures due to a key limitation -- their greedy manner of identifying a list of records through pairwise comparison ( i.e. , similarity match ) of consecutive segments . This paper introduces a new method for record extraction that captures a list of objects in a more robust way based on a holistic analysis of a Web page . The method focuses on how a distinct tag path appears repeatedly in the DOM tree of the Web document . Instead of comparing a pair of individual segments , it compares a pair of tag path occurrence patterns ( called visual signals ) to estimate how likely these two tag paths represent the same list of objects . The paper introduces a similarity measure that captures how closely the visual signals appear and interleave . Clustering of tag paths is then performed based on this similarity measure , and sets of tag paths that form the structure of data records are extracted . Experiments show that this method achieves higher accuracy than previous methods .
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