Re-Ranking Web Data per Knowledge Domain
Abstract
We propose a re-ranking algorithm to effectively assay and re-sequence the web data crawled by some credited web search engines, to meet the user needs in a domain space. The algorithm studies the structure and semantics of the domain ontology graph and constructs computational relations among nodes. After examining matching terms between ontology dictionary and the textual content (text, metadata) of the retrieved documents, we calculate three-dimensional information scores – distance, direction, and relationship of each document in the top k search result set. We further explore the directional relation with three information degrees: granularity, diversity and generality, and subsequently re-rank the retrieved documents.
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