Page 174 - Journal of Library Science in China 2020 Vol.46

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Extended English abstracts of articles published in the Chinese edition of Journal of Library Science in China, Vol.46, 2020 173

by interactions of data intelligence and knowledge discovery, including: 1) Data intelligence
facing knowledge discovery, 2) Knowledge discovery penetrating data intelligence, and
3) Synthetic applications in academic assessment. Two typical projects, “Engineering science
and technology oriented multi-source data mining of social demands in Shanghai” and “Frontiers
of Internationalization of Chinese Philosophy and Social Sciences”, are applied for identifying
theoretical effectiveness.
In view of the division of “soft science，hard technology”, the authors suggested that LIS exert
the comparative advantages and take data intelligence and knowledge discovery as the special
direction to make an integration of data-academy-creativity or digital academic culture (DAC).
LIS might take the development strategy through the “hard expansion” of information technology
and the “soft expansion” of management and economics for promoting the interdisciplinary
extension.

Towards knowledge fusion: The development trend of information science in
big data environment

〇a*
LI Guangjian〇 & LUO Liqun
In recent years, an important development trend of the intelligence community is to emphasize
the integration and consilience of different sources and types of intelligence. In this process,
knowledge fusion plays a key role, which has attracted the attention of researchers and practitioners
of information science and related disciplines.
This article first summarizes the current situation and trends of knowledge fusion in the research
and practice of information science under the current big data environment: 1) The concept of
intelligence has shifted from assisted decision support to direct prediction and early warning. In the
process of warning and early warning, knowledge fusion is indispensable. 2) Intelligence gathering
has shifted from traditional task-oriented passive gathering to active perception of content-based
understanding. In the big data environment, with the development of data processing technology
and intelligent algorithm, the method of information collection has transformed from traditional
task-oriented collection to more intelligence perception. 3) Intelligence analysis has further
expanded from focusing on association relationships to the exploration of causality. In the big data
environment, the demand for causal analysis in the intelligence field is increasingly strong, and it
is gradually becoming the “normal” of intelligence analysis. 4) Intelligence services have changed
from knowledge services to wisdom services. On the one hand, the application of intelligent
information technology is the core of the smart service, which is different from the application of
information technology in the past. On the other hand, smart intelligence services not only need

* Correspondence should be addressed to LI Guangjian, Email: ligj@pku.edu.cn, ORCID: 0000-0002-2897-6246.