Dpaper:An Authoring Tool for Extractable Digital Papers

Purpose： To develop a structured, rich media digital paper authoring tool with an object- based model that enables interactive, playable, and convertible functions. Design/methodology/approach： We propose Dpaper to organize the content （text, data, rich media, etc.） of dissertation papers as XML and HTML5 files by means of digital objects and digital templates. Findings： Dpaper provides a structured-paper editorial platform for the authors of PhDs to organize research materials and to generate various digital paper objects that are playable and reusable. The PhD papers are represented as Web pages and structured XML files, which are marked with semantic tags. Research limitations： The proposed tool only provides access to a limited number of digital objects. For instance, the tool cannot create equations and graphs, and typesetting is not yet flexible compared to MS Word. Practical implications： The Dpaper tool is designed to break through the patterns of unstructured content organization of traditional papers, and makes the paper accessible for not only reading but for exploitation as data, where the document can be extractable and reusable. As a result, Dpaper can make the digital publishing of dissertation texts more flexible and efficient, and their data more assessable. Originality/value： The Dpaper tool solves the challenge of making a paper structured and object-based in the stage of authoring, and has practical values for semantic publishing.

Construction of a domain knowledge service system based on the STKOS

Purpose: The study was carried out to construct a domain knowledge service system based on the Scientific & Technological Knowledge Organization Systems(STKOS). Design/methodology/approach: The framework of a domain knowledge service system is designed on the basis of the STKOS, and the STKOS science and technology vocabularies, category systems, and ontology networks are applied to realize the knowledge organization and semantic linking of the scientific and technological information resources. Meanwhile, related knowledge-mining analysis algorithms and models are improved, and some tools such as Solr and D3 are used for developing the system. This system integrates various knowledge service modules, including unified search of domain information resources and knowledge-linked navigation, domain hotspot and burst topics monitoring analysis, knowledge structure and evolution analysis, literature citation network, and research agents’ cooperative relationship network analysis. Findings: The system can help to refine descriptions, knowledge organization, and the semantic linking of various kinds of information resources closely related to science and technology. Such resources include domain literature, institutions, scientists, projects, and more. Research limitations: Trial assessment and performance improvement should be carried out for the knowledge service application on the basis of more types of and larger quantities of domain information resources.Practical implications: The domain knowledge service system provides an integrated knowledge discovery tool, as well as several kinds of knowledge mining analysis services for researchers.Originality/value: Our practice can be used as a valuable guide for libraries and information institutions that plan to provide deep domain knowledge services.

CiteOpinion: Evidence-based Evaluation Tool for Academic Contributions of Research Papers Based on Citing Sentences

Purpose:To uncover the evaluation information on the academic contribution of research papers cited by peers based on the content cited by citing papers,and to provide an evidencebased tool for evaluating the academic value of cited papers.Design/methodology/approach:CiteOpinion uses a deep learning model to automatically extract citing sentences from representative citing papers;it starts with an analysis on the citing sentences,then it identifies major academic contribution points of the cited paper,positive/negative evaluations from citing authors and the changes in the subjects of subsequent citing authors by means of Recognizing Categories of Moves(problems,methods,conclusions,etc.),and sentiment analysis and topic clustering.Findings:Citing sentences in a citing paper contain substantial evidences useful for academic evaluation.They can also be used to objectively and authentically reveal the nature and degree of contribution of the cited paper reflected by citation,beyond simple citation statistics.Practical implications:The evidence-based evaluation tool CiteOpinion can provide an objective and in-depth academic value evaluation basis for the representative papers of scientific researchers,research teams,and institutions.Originality/value:No other similar practical tool is found in papers retrieved.Research limitations:There are difficulties in acquiring full text of citing papers.There is a need to refine the calculation based on the sentiment scores of citing sentences.Currently,the tool is only used for academic contribution evaluation,while its value in policy studies,technical application,and promotion of science is not yet tested.