Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search

Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search

The original PageRank algorithm for improving the ranking of search-query results computes a single vector, using the link structure of the Web, to capture the relative “importance” of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a set of PageRank vectors, biased using a set of representative topics, to capture more accuratel...

Topic Sensitive SourceRank: Extending SourceRank for Performing Context-Sensitive Search over Deep Web by

Source selection is one of the foremost challenges for searching deep-web. For a user query, source selection involves selecting a subset of deep-web sources expected to provide relevant answers to the user query. Existing source selection models employ query-similarity based local measures for assessing source quality. These local measures are necessary but not sufficient as they are agnostic ...

Dynamic Context-Sensitive PageRank for Expertise Mining

Online tools for collaboration and social platforms have become omnipresent in Web-based environments. Interests and skills of people evolve over time depending in performed activities and joint collaborations. We believe that ranking models for recommending experts or collaboration partners should not only rely on profiles or skill information that need to be manually maintained and updated by...

Context-Sensitive Keyword Search and Ranking for XML

Topic-Sensitive Hidden-Web Crawling

A constantly growing amount of high-quality information is stored in pages coming from the Hidden Web. Such pages are accessible only through a query interface that a Hidden-Web site provides and may span a variety of topics. In order to provide centralized access to the Hidden Web, previous works have focused on query generation techniques that aim at downloading all content of a given Hidden ...