Improving the Relevance of Search Results
Search-term Disambiguation and Ontological Filtering
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Synonymy & polysemy of natural languages together with information overload are two main factors that affect the relevance of Web hits. When users submit a query, search engines usually return a long list of hits with syntactic similarity. Users are confronted with choosing a needle from a haystack relevant items from long lists of hits. This book proposes an improved strategy for increasing the relevance of Web search results via search term disambiguation and ontological filtering. Results are classified into an ontology, such as Open Directory Project. Semantic characteristics of ontology c...
Synonymy & polysemy of natural languages together
with information overload are two main factors that
affect the relevance of Web hits. When
users submit a query, search engines usually return a
long list of hits with syntactic similarity. Users
are confronted with choosing a needle from a haystack relevant items from long lists of hits. This book
proposes an improved strategy for increasing the
relevance of Web search results via search term
disambiguation and ontological filtering. Results are
classified into an ontology, such as Open Directory
Project. Semantic characteristics of ontology
categories are represented by a category-document and
similarities of this and search results are evaluated
using a Vector Space Model. Users choose a category
to obtain only the search results classified under
the selected category. Experimental data show the
approach boosts the Web hits precision by more than
20%. The book should help shed some light on Web
searching and word sense disambiguation, and should
be useful to students and researchers in the fields
of information retrieval, text classification, and
data mining; or anyone else interested in Web
searching.
with information overload are two main factors that
affect the relevance of Web hits. When
users submit a query, search engines usually return a
long list of hits with syntactic similarity. Users
are confronted with choosing a needle from a haystack relevant items from long lists of hits. This book
proposes an improved strategy for increasing the
relevance of Web search results via search term
disambiguation and ontological filtering. Results are
classified into an ontology, such as Open Directory
Project. Semantic characteristics of ontology
categories are represented by a category-document and
similarities of this and search results are evaluated
using a Vector Space Model. Users choose a category
to obtain only the search results classified under
the selected category. Experimental data show the
approach boosts the Web hits precision by more than
20%. The book should help shed some light on Web
searching and word sense disambiguation, and should
be useful to students and researchers in the fields
of information retrieval, text classification, and
data mining; or anyone else interested in Web
searching.
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