Content Characteristics of Knowledge Integration in the eHealth Field:An Analysis Based on Citation Contexts

Purpose:This study attempts to disclose the characteristics of knowledge integration in an interdisciplinary field by looking into the content aspect of knowledge.Design/methodology/approach:The eHealth field was chosen in the case study.Associated knowledge phrases(AKPs)that are shared between citing papers and their references were extracted from the citation contexts of the eHealth papers by applying a stem-matching method.A classification schema that considers the functions of knowledge in the domain was proposed to categorize the identified AKPs.The source disciplines of each knowledge type were analyzed.Quantitative indicators and a co-occurrence analysis were applied to disclose the integration patterns of different knowledge types.Findings:The annotated AKPs evidence the major disciplines supplying each type of knowledge.Different knowledge types have remarkably different integration patterns in terms of knowledge amount,the breadth of source disciplines,and the integration time lag.We also find several frequent co-occurrence patterns of different knowledge types.Research limitations:The collected articles of the field are limited to the two leading open access journals.The stem-matching method to extract AKPs could not identify those phrases with the same meaning but expressed in words with different stems.The type of Research Subject dominates the recognized AKPs,which calls on an improvement of the classification schema for better knowledge integration analysis on knowledge units.Practical implications:The methodology proposed in this paper sheds new light on knowledge integration characteristics of an interdisciplinary field from the content perspective.The findings have practical implications on the future development of research strategies in eHealth and the policies about interdisciplinary research.Originality/value:This study proposed a new methodology to explore the content characteristics of knowledge integration in an interdisciplinary field.

Corpus construction and mining for Citation Context Analysis

Citation Context Analysis(CCA)is a typical data-driven research field based on full-text information,which breaks the limitations of traditional citation analysis using only bibliographic data,and benefits further studies on various citation behaviors and other core issues behind them,such as citation motivation,citation function and citation sentiment.Corpus for CCA is the most important guarantee and support for these issues.This paper attempts to discuss the corpus construction and mining for CCA in order to comprehensively review the research significance,research status and existing deficiencies in this area.Two main sections in our paper are:1)corpus construction for CCA,its three building tasks,such as citation sentence extraction,citation-reference mapping and citation context extraction,are discussed;2)corpus mining and utilization for CCA,following related topics or situations are explored,including classification of citation motivation(or behavior)and citation sentiment,indexing and retrieval based on citation,citation recommendation and evaluation,citation-based abstracting and review generation automatically,and domains knowledge metrics.Finally,some suggestions and future research directions are briefly listed.

A New Citation Recommendation Strategy Based on Term Functions in Related Studies Section

Purpose:Researchers frequently encounter the following problems when writing scientific articles:(1)Selecting appropriate citations to support the research idea is challenging.(2)The literature review is not conducted extensively,which leads to working on a research problem that others have well addressed.The study focuses on citation recommendation in the related studies section by applying the term function of a citation context,potentially improving the efficiency of writing a literature review.Design/methodology/approach:We present nine term functions with three newly created and six identified from existing literature.Using these term functions as labels,we annotate 531 research papers in three topics to evaluate our proposed recommendation strategy.BM25 and Word2vec with VSM are implemented as the baseline models for the recommendation.Then the term function information is applied to enhance the performance.Findings:The experiments show that the term function-based methods outperform the baseline methods regarding the recall,precision,and F1-score measurement,demonstrating that term functions are useful in identifying valuable citations.Research limitations:The dataset is insufficient due to the complexity of annotating citation functions for paragraphs in the related studies section.More recent deep learning models should be performed to future validate the proposed approach.Practical implications:The citation recommendation strategy can be helpful for valuable citation discovery,semantic scientific retrieval,and automatic literature review generation.Originality/value:The proposed citation function-based citation recommendation can generate intuitive explanations of the results for users,improving the transparency,persuasiveness,and effectiveness of recommender systems.