Keyword searching is the most common form of document search on the Web. Many Web publishers manually annotate the META tags and titles of their pages with frequently queried phrases in order to improve their placement and ranking. A "hidden phrase" is defined as a phrase that occurs in the META tag of a Web page but not in its body. In this paper we present an algorithm that mines the definitions of hidden phrases from the Web documents. Phrase definitions allow (i) publishers to find relevant phrases with high query frequency, and, (ii) search engines to test if the content of the body of a document matches the phrases. We use co-occurrence clustering and association rule mining algorithms to learn phrase definitions from high-dimensional data sets. We also provide experimental results.
|Original language||English (US)|
|Number of pages||10|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|State||Published - Dec 1 2003|
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)