DATA EXTRACTION AND ALIGNMENT USING TAGS AND VALUE SIMILARITY

Abstract

Web databases generate query result pages based on a user’s query. Automatically extracting these data from query result pages is very important for many applications, such as data integrations, which needs to cooperate with multiple web databases. This system presents a novel data extraction and alignment method called DATVS that combines both tag and value similarity. DATVS automatically extracts data from query result pages by first identifying and segmenting the query result records (QRRs) in the query result pages and then aligning the data segmentation QRRs into a table, in which the data values from the same each attributes the put into the same column. Specifically, This propose new techniques to handle the case when the QRRs is not contiguous, which may be due to presence of an auxiliary information, such a comment, recommendation or advertisement and for handling they any nested structure that may exist in the QRRs. The new system is a design and the new record alignment algorithm that aligns the attributes in a record and first pair wise and they holistically, by combines the tag and data value similar information. Experimental results show that DATVS achieves high precision and outperforms existing state-of-the-art data extraction methods.