%0 Journal Article
	%A Mehrnoush Shamsfard and  Azadeh Nematzadeh and  Sarah Motiee
	%D 2008
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 15, 2008
	%T ORank: An Ontology Based System for Ranking Documents
	%U https://publications.waset.org/pdf/10525
	%V 15
	%X Increasing growth of information volume in the
internet causes an increasing need to develop new (semi)automatic
methods for retrieval of documents and ranking them according to
their relevance to the user query. In this paper, after a brief review
on ranking models, a new ontology based approach for ranking
HTML documents is proposed and evaluated in various
circumstances. Our approach is a combination of conceptual,
statistical and linguistic methods. This combination reserves the
precision of ranking without loosing the speed. Our approach
exploits natural language processing techniques for extracting
phrases and stemming words. Then an ontology based conceptual
method will be used to annotate documents and expand the query.
To expand a query the spread activation algorithm is improved so
that the expansion can be done in various aspects. The annotated
documents and the expanded query will be processed to compute
the relevance degree exploiting statistical methods. The outstanding
features of our approach are (1) combining conceptual, statistical
and linguistic features of documents, (2) expanding the query with
its related concepts before comparing to documents, (3) extracting
and using both words and phrases to compute relevance degree, (4)
improving the spread activation algorithm to do the expansion based
on weighted combination of different conceptual relationships and
(5) allowing variable document vector dimensions. A ranking
system called ORank is developed to implement and test the
proposed model. The test results will be included at the end of the
	%P 892 - 898