{'fraction_non_alphanumeric': 0.01524390243902439, 'fraction_numerical': 0.0, 'mean_word_length': 5.354838709677419, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Mining templates from search result records of search engines

Metasearch engine, Comparison-shopping and Deep Web crawling applications need to extract search result records enwrapped in result pages returned from search engines in response to user queries. The search result records from a given search engine are usually formatted based on a template. Precisely identifying this template can greatly help extract and annotate the data units within each record correctly. In this paper, we propose a graph model to represent record template and develop a domain independent statistical method to automatically mine the record template for any search engine using sample search result records. Our approach can identify both template tags (HTML tags) and template texts (non-tag texts), and it also explicitly addresses the mismatches between the tag structures and the data structures of search result records. Our experimental results indicate that this approach is very effective.