Keyword Ranking Analysis in PHP

Abstract:

Many real-world networks such as Face book, LinkedIn and Wikipedia exhibit rich connectivity patterns along with worthwhile content nodes often labeled with meaningful attributes. Keyword search is an effective method to retrieve information from such useful networks. The aim of keyword search is to find a set of answers (sub graphs) covering all or part of the queried keywords. A challenge in keyword search systems is to rank answers according to their relevance to the query. This relevance lies in the textual content and structural compactness of the answers. In this paper, an attribute-specific ranking method is proposed based on language models to rank candidate answers according to their semantic information up to the attribute level. This method scores answers using a model enriched with attribute-specific preferences and integrating both the structure and content of answers. The proposed model is directly estimated on the sub-graphs (answers) and is defined such that it can preserve the local importance of keywords in nodes. Extensive experiments conducted on a standard evaluation framework with three real-world datasets illustrate the superior effectiveness of the proposed ranking method to that of the state-of-the-art methods.

Download