Ranking web pages using collective knowledge
|Ranking web pages using collective knowledge|
|Author(s)||Al-Akashi F.H., Inkpen D.|
|Published in||NIST Special Publication|
|Keyword(s)||Collective knowledge, Grid indexing structure, Web indexing & ranking, Wikipedia, Wikipedia article-based indexing (Extra: Collective knowledge, Hybrid techniques, Indexing scheme, Indexing structures, Metacontent, Preliminary algorithms, Web document, Web indexing, Wikipedia, Experiments, Information retrieval, Websites, Indexing (of information))|
|Article||BASE, CiteSeerX, Google Scholar|
|Web||Ask, Bing, Google (PDF), Yahoo!|
|Download and mirrors|
|Local copy||Not available|
|Remote mirror(s)||Not available|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of conference papers|
Indexing is a crucial technique for dealing with the massive amount of data present on the web. Indexing can be performed based on words or on phrases. Our approach aims to efficiently index web documents by employing a hybrid technique in which web documents are indexed in such a way that knowledge available in the Wikipedia and in meta-content is efficiently used. Our preliminary experiments on the TREC dataset have shown that our indexing scheme is a robust and efficient method for both indexing and for retrieving relevant web pages. We ranked term queries in different ways, depending if they were found in Wikipedia pages or not. This paper presents our preliminary algorithm and experiments for the ad-hoc and diversity tasks of the TREC 2011 Web track. We ran our system on the subset B (50 million web documents) from the ClueWeb09 dataset.
- This section requires expansion. Please, help!
Probably, this publication is cited by others, but there are no articles available for them in WikiPapers.