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