Page 142 - Journal of Library Science in China, Vol.45, 2019

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Extended English abstracts of articles published in the Chinese edition of Journal of Library Science in China, Vol.45, 2019 141

differentiated according to relevant socio-cultural characteristics of SNS users. As with this
study, platforms developers should explore the reasons for the differences in motivations, from
the characteristics of the SNS platforms and the cultural characteristics of the users themselves.
Finally, recommendations are made for future research, namely, a quantitative examination of the
role of specific individual cultural characteristics in moderating knowledge sharing motivation on
SNS.

Topic identification based on multi-semantic relation fusion

XU Haiyun , WU Huawei, LUO Rui, DONG Kun & LI Jing
〇a ∗
One of the typical characteristics of big data analysis is multivariate data relation processing. The
multi-relationship analysis of topics refers to the analysis of the relationships established between
topics and other measurable entities (MEs). There are many MEs in scientific or technological
documents, and they relate directly or indirectly with knowledge units. However, the current topic
acquisition methods for this document rely mostly on single association analysis, so it is difficult
to obtain the topics of scientific or technological developments accurately. Therefore, finding the
multi-relationships between the entities of a document is one of the key technologies for accurate
topic identification in massive scientific or technological literatures.
This paper firstly reviewed the research status of multi-relations fusion in topic identification,
and summarized the various measurable relationships of topic terms in the scientific or technical
literatures. The research found that there are semantic relations between topic terms, authors and
citations in the scientific or technical literatures based on the topic content, with their co-occurrence
relations can reveal respectively the topic association from different perspectives. Based on the
distance between the semantic distances of topic terms, we divided the topic terms associations
in topic identification into basic relations, strengthened relations and additional relations. For the
strengthened relations and additional relations, any type of MEs can be the intermediate node of
the topic terms association. Choosing the appropriate intermediate MEs is especially important for
fully establishing the semantic association between the topic terms. This paper chooses authors,
references and citation literatures as the intermediate MEs of topic term strengthened relations and
additional relations. Seven types of topic associations are formed by the topic terms and the MEs.
The fusion relationship can make up for the lack of information of a single association relationship
through obtaining more accurate topic association.
The acquisition of multiple topic associations is the basis of multi-relations fusion. Whether
the multi-relations fusion algorithm can enhance the meaningful topic semantic association
and weaken the noise correlation is also an important step to achieve multi-relationship topic

* Correspondence should be addressed to XU Haiyun, Email: xuhy@clas.ac.cn, ORCID: 0000-0002-7453-3331