Page 231 - JOURNAL OF LIBRARY SCIENCE IN CHINA 2018 Vol. 42

230 Journal of Library Science in China, Vol. 8, 2016

In this thesis,a statistical learning method is proposed to recognize entities and build links
across different linked datasets. Before the entities comparing computation,first,the method finds
class correspondences to classify related entity attributes correspondences across datasets. It gives
a matching relationship description for the high correlation attributes and reduces the calculation
times to match entity attributes. Second,our method compares the similarity of entities based on
calculating the similarity of the matched attributes,and builds entities’ linking to complete the goal
of linking discovery across different datasets. When to cluster the attributes correspondences,we
use K-medoids clustering algorithm to discover the potential attributes correspondences. K-medoids
clustering algorithm is mainly aimed at classifying property concepts and corresponding attributes
that represent the same expression meanings between datasets. At last,the attributes can be
compared and matched in groups. Then EDOAL language is used to define the clustered attributes
and describe the correspondences relation between those attributes. According to the matching
relation,we compare and calculate the similarity between entity attributes. Finally our method
works out the linking under the SILK framework:mapping the property relationship to SILK
scripts,building entities linking between datasets according to a preset confidence value,endowing
entities with RDFs properties,and realizing entity links discovery between datasets. The thesis
testifies different open linked datasets on the basis of linked data entity linking discovery method.
The datasets mainly include IM@OAIE2014(dataset Abox3)、CKAN(dataset EUROSTAT)
and GADM-RDF(dataset GADM),and data are used to cluster matched attributes and interlink
entities. Through twice entity linking discovery process of experimental verification,experimental
results show that K-medoids clustering algorithm calculates the similarity of entities matching
between dissimilar properties can increase the number of entities links. The method already
reaches the high accuracy rate and F values. So the proposed method can reduce the calculation
times of matching entities across different datasets and improve the accuracy of physical links. It
has high feasibility and practicability to solve this problem.

Application of SNS in libraries: Status and prospects

LIU Xuan ①a *

With the development of network technology,social networking becomes a cultural phenomenon.
Its applications in the library attract more and more attentions and have become an important
feature of the modern library. In 2007, social networking became a new research theme in Library
and Information Science, and its application expanded from the original BBS, entertainment SNS
to the current micro-information age. This paper is a systematic review of the application of SNS
in the library.

* Correspondence should be addressed to LIU Xuan,Email:liuxuan0324@qq.com,ORCID:0000-0001-8757-539X