elektronická verzia publikácie - FIIT STU - Slovenská technická ...
elektronická verzia publikácie - FIIT STU - Slovenská technická ...
elektronická verzia publikácie - FIIT STU - Slovenská technická ...
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242 Selected Studies on Software and Information Systems<br />
Paper [64] similarly deals with ambiguity problems in person information mining on the<br />
Web. Authors propose five distinct features and a cascaded multiple-clusterer approach for<br />
name disambiguation using personal titles, community chains, contextual terms, temporal<br />
expressions and hostnames.<br />
Web Object Extraction. Web object extraction/retrieval is a new approach to information<br />
retrieval on the Web being invented at Microsoft Research. Current search engines<br />
are working at a document-level, ranking documents by their relevance to a set of keywords<br />
(query). However, these documents embed various kinds of objects along with their<br />
attributes such as people, products, papers 7 , organizations, etc. Web object extraction is<br />
aiming at extracting such objects to create an object-level vertical search engines (specialized<br />
on a particular domain). Such a search engine gives a list of object with explicit properties<br />
instead of list of URLS, which costs user’s significant efforts to decipher for needed information<br />
[49]. Moreover, authors of web object extraction method deal also with integration of<br />
the same object retrieved from multiple sources into one “real-world” object.<br />
Figure 8-9 depicts architecture of scientific papers extraction system. It is able to extract<br />
four types of objects (papers, authors, conferences and locations) and relationships between<br />
them. The architecture follows the method, where web crawler and classifier automatically<br />
collect all relevant webpages/documents that contain object information for a specific vertical<br />
domain. The crawled webpages/documents will be passed to the corresponding object<br />
extractor for extracting the structured object information and building the object warehouse.<br />
The task of aggregators is obvious: they aggregate information about the same object from<br />
multiple different data sources.<br />
The key point is object extraction itself. The problem is that webpages are generated by<br />
tens of thousands of different templates. One possible solution is to distinguish webpages<br />
generated by different templates, and then build an extractor for each template (called<br />
template-dependent solution). This solution is of no use in real world applications: firstly,<br />
it is practically impossible to collect all possible templates (even webpages from the same<br />
website may be generated by several different templates0. Secondly, it would be impossible<br />
to train and maintain of all required extractors for each template.<br />
Authors in [50] conducted an analysis of webpages across web sites and extracted some<br />
template-independent features from it:<br />
– Information about an object in a web page is generally grouped together as an object<br />
block, which can be further segmented into object elements, providing information<br />
about individual attributes.<br />
– Strong sequence characteristics exist for web objects of the same type across different<br />
web sites. For example, a person’s name is always ahead of contact information<br />
(telephone, postal address) in all the pages.<br />
Based on template-independent features authors propose template-independent method of<br />
Web Object Extraction based on linear-chain Conditional Random Fields (CRFs) and achieved<br />
7 A working object-level search engine for scientific papers can be found at http://libra.msra.cn/.