Author:
Zachary Mason
Affiliation:
Brandeis University, United States
Keyword(s):
Corpus linguistics, semantic search, query refinement, semantic modeling.
Related
Ontology
Subjects/Areas/Topics:
Data Engineering
;
Ontologies and the Semantic Web
;
Personalized Web Sites and Services
;
Searching and Browsing
;
Web Information Systems and Technologies
;
Web Interfaces and Applications
;
Web Personalization
Abstract:
This paper describes a system for doing contextually-steered web search. The system is based on a method for estimating the semantic relevance of a web page to a query. Consider doing a web search for conferences about web search. The query “search conferences” is not effective, as it produces results relevant for the most part to searching over conferences, rather than conferences on the topic of search. The system described in this paper enables queries of the form “search conference context:pagerank”. The context field in this example specifies a preference for results semantically relevant to the term “pagerank”, although there is no requirement that said results contain the word “pagerank” itself. This a more semantic, less lexical way of refining the query than adding literal conjuncts. Contextual search, as implemented in this paper, is based on the Google (Google) search engine. For each query, the top one hundred search results are fetched from Google and sorted according to
their relevance to the context query. Relevance is computed as a distance function between the vocabulary vectors associated with a web-page and a query. For queries, the vocabulary vector is formed by aggregating the web-pages in the search results for that query. For web-pages, the vocabulary vector is aggregated from that web-page and other web-pages nearby in link-space.
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