TITLE:
DBpedia-Based Fuzzy Query Recommenda-tion Algorithm and Its Applications in the Resource-Sharing Platform of Polar Samples
AUTHORS:
Wenfang Cheng, Qing’e Wu, Xiao Cheng, Jie Zhang, Zhuanling Song
KEYWORDS:
Search Engine, Fuzzy Query, Semantic Similarity, Wiki
JOURNAL NAME:
International Journal of Intelligence Science,
Vol.5 No.5,
October
12,
2015
ABSTRACT: In order to continuously promote the polar
sample resource services in China and effectively guide the users to access
such information as needed, a fuzzy algorithm based on DBpedia has been
proposed through the analysis of the characteristics of the query
recommendations in search engines, namely, to search similar entry queues by
constructing a DBpedia category tree, then use the fuzzy matching algorithm to
work out the entry similarity, and then present the example query applications
of this algorithm on the resource-sharing platform of polar samples. Comparing
the traditional literal character matching method and DBpedia semantic
similarity algorithm, the experimental results show that the fuzzy query
algorithm based on DBpedia features has a higher search accuracy rate, stronger
anti-interference capability, and more flexible algorithm use by virtue of its
fuzzy weight adjustment.