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Extended English abstracts of articles published in the Chinese edition of Journal of Library Science in China 2018 Vol.44 163

and data resources from LAMs. The paper supplies a number of cases that reveal new ideas for
information services, especially the structuralization and semantic enrichment of raw data. In
addition to showing how the structured data provided by LAMs can infinitely enrich knowledge
graphs and associated datasets and be used for the development of knowledge bases, the article
also addresses the data exchange and in-depth semantic annotation of images, introducing the
International Image Interoperability Framework (IIIF) APIs and a study of semantic-based deep
image annotation. Lastly, this paper focuses on intangible cultural heritages and the potential for
taking digital humanities approaches, technologies, and other channels to promote the construction
of digital resources for them, in compliance with the requirements of Smart Data. Overall, the
literature review, project analyses, and various case examples used in this paper provide evidence
that, by taking the concept and methodology of Big Data and using the approaches of Smart
Data, we can turn unstructured data into structured data in the data organization and integration
process, producing the kind of data that will be machine-processable, reusable for multi-purposes,
and highly efficient in processing. Thus, LAMs will be able to bring these rich resources into the
mainstream of the digital age. In conclusion, in the Semantic Web and Big Data era, LAMs are
not only the providers but also the direct beneficiaries of Smart Data. The development of Smart
Data can effectively enable the advancement of digital humanities, while also becoming the most
important emerging work of LAMs.

Cognitive computing: A new perspective for evaluating the individual
academic paper
〇a*
SUO Chuanjun〇, GAI Shuangshuang & ZHOU Zhichao
Academic paper evaluation is a classic problem in the field of library science. Its main purpose is
to help users find the excellent papers they need. The qualitative evaluation based on peer review
and the quantitative evaluation based on bibliometrics are the most accepted evaluation methods of
academic papers. Although qualitative evaluation is based on contents in each paper, it is inefficient
and susceptible to expert subjectivity or other non-scientific factors, not suitable for the highly
efficient evaluation of massive papers. Although quantitative evaluation is objective, efficient
and operable, it is not directly related to the content of the paper and it is easily manipulated. All
the time, although scholars at home and abroad have never stopped investigating the evaluative
methods of papers, they have never achieved satisfactory results. There is contradiction that
different evaluation methods could not be complemented by each other. Therefore, new theory and
method for evaluation are in urgent need.
Cognitive computing is a system that interacts with human beings by learning with a certain

* Correspondence should be addressed to SUO Chuanjun, Email: suocj@ruc.edu.cn, ORCID: 0000-0002-7416-1531