Biperpedia: An Ontology for Search Applications
Venue
Proc. 40th Int'l Conf. on Very Large Data Bases (PVLDB) (2014)
Publication Year
2014
Authors
Alon Halevy, Xuezhi Wang, Steven Whang, Fei Wu
BibTeX
Abstract
Search engines make significant efforts to recognize queries that can be answered
by structured data and invest heavily in creating and maintaining high-precision
databases. While these databases have a relatively wide coverage of entities, the
number of attributes they model (e.g., gdp, capital, anthem) is relatively small.
Extending the number of attributes known to the search engine can enable it to more
precisely answer queries from the long and heavy tail, extract a broader range of
facts from the Web, and recover the semantics of tables on the Web. We describe
Biperpedia, an ontology with 1.6M (class, attribute) pairs and 67K distinct
attribute names. Biperpedia extracts attributes from the query stream, and then
uses the best extractions to seed attribute extraction from text. For every
attribute Biperpedia saves a set of synonyms and text patterns in which it appears,
thereby enabling it to recognize the attribute in more contexts. In addition to a
detailed analysis of the quality of Biperpedia, we show that it can increase the
number of Web tables whose semantics we can recover by more than a factor of 4
compared with Freebase.
