The web is the greatest information source in human history, yet finding all offers for flats with gardens in London, Paris, and Berlin or all restaurants open after a screening of the latest blockbuster remain hard tasks – as that data is not easily amenable to processing. Extracting web data into databases for easier processing has been a resource-intensive process, requiring human supervision for every source from which to extract. This has been changing with approaches that replace human annotators with automated annotations. Such approaches could be successfully applied to restricted settings such as single attribute extraction or for domains with significant redundancy among sources. Multi-attribute objects are often presented on (i) Result pages, where multiple objects are presented on a single page as lists, tables or grids, with most important attributes and a summary description, (ii) Detail pages, where each page provides a detailed list of attributes and long description for a single entity, often in rich format. Both result and detail pages are having their own advantages. Extracting objects from result pages is orders of magnitude faster than from detail pages, and the links to detail pages are often only accessible through result pages. Detail pages have a complete list of attributes and full description of the entity. Early web data extraction approaches requires manual annotations for each web site to reach high accuracy, while a number of domain independent approaches only focus on unsupervised repeated structure segmentation. The former is limited in scaling and automation, while the latter is lacked in accuracy. Recent automated data extraction systems are often informed with an ontology and a set of object and attribute recognizers, however they have focused on extracting simple objects with few attributes from single-entity pages and avoided result pages. We present an automatic ontology-based multi-attribute object extraction system AMBER, which deals with both result and detail pages, achieves very high accuracy (>96%) with zero site-specific supervision, and is able to solve practical issues that arise in real-life data extraction tasks. AMBER is also applied as an important component of DIADEM, the first automatic full-site extraction system that is able to extract structured data from different domains without site-specific supervision, and has been tested through a large-scale evaluation (>10, 000) sites. On the result page side, AMBER achieves high accuracy through a novel domain- aware, path-based template discovery algorithm, and integrates annotations for all parts of the extraction, from identifying the primary list of objects, over segment- ing the individual objects, to aligning the attributes. Yet, AMBER is able to tolerate significant noise in the annotations, by combining these annotations with a novel algorithm for finding regular structures based on XPATH expressions that capture regular tree structures. On the detail page side, AMBER integrates boilerplate removal, dynamic lists identification and page dissimilarity calculation seamlessly to identify data region, then employs a set of fairly simple and cheaply computable features for attribute extraction. Besides, AMBER is the first approach that combines result page extraction and detail page extraction by integrating attributes extracted from result pages and the attributes found on corresponding detail pages. AMBER is able to identify attributes of objects with near perfect accuracy and to extract dozens of attributes with > 96% across several domains, even in presence of significant noise. It outperforms uninformed, automated approaches by a wide margin if given an ontology. Even in absence of an ontology, AMBER outperforms most previous systems on record segmentation.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:667031 |
| Date | January 2015 |
| Creators | Cheng, Wang |
| Contributors | Gottlob, Georg |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:ff49d786-bfd8-4cd4-a69c-19e81cb95920 |
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