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.

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.

Entity Burst Discriminative Model for Cumulative Citation Recommendation

Knowledge base acceleration-cumulative citation recommendation(KBA-CCR)aims to detect citation-worthiness documents from a chronological stream corpus for a set of target entities in a knowledge base.Most previous works only consider a number of semantic features between documents and target entities in the knowledge base,and then use powerful machine learning approaches such as logistic regression to classify relevant documents and non-relevant documents.However,the burst activities of an entity have been proved to be a significant signal to predict potential citations.In this paper,an entity burst discriminative model(EBDM)is presented to substantially exploit such burst features.The EBDM presents a new temporal representation based on the burst features,which can capture both temporal and semantic correlations between entities and documents.Meanwhile,in contrast to the bag-of-words model,the EBDM can significantly decrease the number of non-zero entries of feature vectors.An extensive set of experiments were conducted on the TREC-KBA-2012 dataset.The results show that the EBDM outperforms the performance of the state-of-the-art models.

Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System

In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniques and tries to combine nodes’textual content for modelling.They still do not,however,directly simulate many interactions in network learning.In order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations.Specifically,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation network.Then,the Commuting Matrix for massive node pair paths is used to improve computational performance.Finally,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson distribution.In addition,we also consider solving the model’s parameters by applying variational inference.Experimental results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational data.Based on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.

A Latent Entity-Document Class Mixture of Experts Model for Cumulative Citation Recommendation

Knowledge Bases （KBs） are valuable resources of human knowledge which contribute to many applications. However, since they are manually maintained, there is a big lag between their contents and the upto-date information of entities. Considering a target entity in KBs, this paper investigates how Cumulative Citation Recommendation （CCR） can be used to effectively detect its worthy-citation documents in large volumes of stream data. Most global relevant models only consider semantic and temporat features of entity-document instances, which does not sufficiently exploit prior knowledge underlying entity-document instances. To tackle this problem, we present a Mixture of Experts （ME） model by introducing a latent layer to capture relationships between the entity-document instances and their latent class information. An extensive set of experiments was conducted on TREC-KBA-2013 dataset. The results show that the model can significantly achieve a better performance gain compared to state-of-the-art models in CCR.

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.

Content-Based Hybrid Deep Neural Network Citation Recommendation Method

The rapid growth of scientific papers makes it difficult to query related papers efficiently,accurately and with high coverage.Traditional citation recommendation algorithms rely heavily on the metadata of query documents,which leads to the low quality of recommendation results.In this paper,DeepCite,a content-based hybrid neural network citation recommendation method is proposed.First,the BERT model was used to extract the high-level semantic representation vectors in the text,then the multi-scale CNN model and BiLSTM model were used to obtain the local information and the sequence information of the context in the sentence,and the text vectors were matched in depth to generate candidate sets.Further,the depth neural network was used to rerank the candidate sets by combining the score of candidate sets and multisource features.In the reranking stage,a variety of Metapath features were extracted from the citation network,and added to the deep neural network to learn,and the ranking of recommendation results were optimized.Compared with PWFC,ClusCite,BM25,RW,NNRank models,the results of the Deepcite algorithm presented in the ANN datasets show that the precision(P@20),recall rate(R@20),MRR and MAP indexesrise by 2.3%,3.9%,2.4%and 2.1%respectively.Experimental results on DBLP datasets show that the improvement is 2.4%,4.3%,1.8%and 1.2%respectively.Therefore,the algorithm proposed in this paper effectively improves the quality of citation recommendation.

Science Citation Index Expanded (SCI-E)及其检索技巧

简要介绍了SCI-E网络数据库及特点,并结合实例说明SCI-E数据库的检索技巧及分析功能的应用。

Recommending Authors and Papers Based on ACTTM Community and Bilayer Citation Network

Citation network is often used for academic recommendation. However, it is difficult to achieve high recommendation accuracy and low time complexity because it is often very large and sparse and different citations have different purposes. What's more, some citations include unreasonable information, such as in case of intentional self-citation. To improve the accuracy of citation network-based academic recommendation and reduce the time complexity, we propose an academic recommendation method for recommending authors and papers. In which, an author-paper bilayer citation network is built, then an enhanced topic model, Author Community Topic Time Model(ACTTM) is proposed to detect high quality author communities in the author layer, and a set of attributes are proposed to comprehensively depict the author/paper nodes in the bilayer citation network. Experimental results prove that the proposed ACTTM can detect high quality author communities and facilitate low time complexity, and the proposed academic recommendation method can effectively improve the recommendation accuracy.

Hybrid scientific article recommendation system with COOT optimization

Today, recommendation systems are everywhere, making a variety of activities considerably more manageable. These systems help users by personalizing their suggestions to their interests and needs. They can propose various goods, including music, courses, articles, agricultural products, fertilizers, books, movies, and foods. In the case of research articles, recommendation algorithms play an essential role in minimizing the time required for researchers to find relevant articles. Despite multiple challenges, these systems must solve serious issues such as the cold-start problem, article privacy, and changing user interests. This research addresses these issues through the use of two techniques: hybrid recommendation systems and COOT optimization. To generate article recommendations, a hybrid recommendation system integrates features from content-based and graph-based recommendation systems. COOT optimization is used to optimize the results, inspired by the movement of water birds. The proposed method combines a graph-based recommendation system with COOT optimization to increase accuracy and reduce result inaccuracies. When compared to the baseline approaches described, the model provided in this study improves precision by 2.3%, recall by 1.6%, and mean reciprocal rank (MRR) by 5.7%.

基于层间融合滤波器与社交神经引文网络的推荐算法

推荐算法是一种用于解决信息过载问题的方法,引文推荐通过引文上下文能够自动匹配候选论文列表。现有基于神经引文网络模型在引文上下文数据预处理的过程中,存在文本噪声和上下文学习不充分的问题。为此,提出一种基于层间融合滤波器和社交神经引文网络的推荐算法FS-Rec。首先,利用具有层间融合滤波器的BERT模型预处理引文上下文,在频域内从所有频率中提取有意义的特征,缓解引文上下文数据的噪声,同时在频域中对多层信息进行融合,增强上下文表示学习的能力;然后,在引文作者嵌入中引入社交关系,与其他引文信息嵌入通过编码器获得表示,将这些表示与经过BERT预训练的引文上下文表示进行融合,得到最终表示;最后,根据最终表示生成引文文本预测。实验结果表明,相较于现有的上下文引文推荐模型,FS-Rec在2个基准数据集arXivCS和PubMed取得了更高的召回率和平均倒数排名(MMR),证明了模型的有效性。

引文情感识别研究进展及评述

[目的/意义]引文情感识别是全文本计量时代引文内容分析的重要研究议题之一,它与引文动机/功能识别、引文主题分析、引文摘要自动生成等存在较强的关联性,可为学术评价、知识图谱构建/绘制等问题的解决提供有效的研究支撑,具有较高研究价值。[方法/过程]通过文献调研分析,从引文语料集创建、情感词典使用、情感识别算法应用及存在问题4个方面,对国内外引文情感识别的研究进展进行全面梳理和分析评述。[结果/结论]引文情感识别已从早期的基于情感词典方法发展到当前基于机器学习算法的新阶段,并正由传统机器学习进一步向深度学习推进。亟待解决的主要问题有:(1)缺乏大规模高质量的引文语料集,对引文语料蕴含的特有价值(引文特征)的挖掘利用严重不足;(2)情感词典方法严重依赖情感词典自身的完备性,机器学习算法(分类模型)的参数优化及识别效果仍有提升空间,对两类方法的有机融合利用尚需深入探索;(3)更细粒度和更多维度的引文情感识别研究及相关应用实践有待进一步拓展和深化。

引文分析视角下的建筑学期刊评价困境与对策研究

文章指出中国建筑学期刊面临三大困境:学科分类体制障碍;建筑学内涵的交叉学科特征;主要基于引用数量的评价系统障碍。进而,文章通过对引文内容的分析为切入,对建筑学、设计学、土木工程学三大学科的代表期刊进行实证分析,以初步揭示针对建筑学学术论文及其载体“期刊”的引用规律特征与内在逻辑,及与共同分类相关学科的区别与联系。研究发现,建筑学期刊引用行为特征与土木工程学具有结构性差异,而与设计学期刊具有类似性;以科学研究微观单位“引文”特征,具体揭示分类问题、学科特征与引文数量评价问题,从而提出针对性对策与建议。

科技情报数据挖掘技术的应用研究

随着科技领域研究成果的快速增加,如何有效管理和挖掘大量科技情报数据成为研究的重点。文中探索了科技情报数据挖掘技术的应用,以提高演化路径分析的准确性和效率。通过构建基于目标领域引文网络的信息处理方法,详细介绍了获取待处理网络,确定元素权重,通过融合权重确定目标领域演化路径的全过程。实验利用真实的科技情报数据集进行验证,实验结果显示,该方法能有效识别关键科技事件和领域演化趋势,更准确地揭示科技领域的发展脉络。该研究为科技情报数据的深入分析提供了新的视角和工具,也为相关领域的决策提供了依据。

Bibliometry-Aware and Domain-Specific Features for Discovering Publication Hierarchically-Ordered Contexts and Scholarly-Communication Structures

Discovering publication hierarchically-ordered contexts is the main task in context-based searching paradigm. The proposed techniques to discover publication contexts relies on the availability of domain-specific inputs, namely a pre-specified ontology terms. A problem with this technique is that the needed domain-specific inputs may not be available in some scientific disciplines. In this paper, we propose utilizing a powerful input that is naturally available in any scientific discipline to discover the hierarchically-ordered contexts of it, namely paper citation and co-authorship graphs. More specifically, we propose a set of domain-specific bibliometry-aware features that are automatically computable instead of domain-specific inputs that need experts’ efforts to prepare. Another benefit behind considering bibliometric-features to adapt to the special characteristics of the literature environment being targeted, which in turn facilitates contexts membership decision making. One key advantage of our proposal is that it considers temporal changes of the targeted publication set.

融合知识图谱和图注意力网络的引文推荐算法

针对传统协同过滤(CF)中的数据稀疏和冷启动问题,以及元路径、随机游走算法没有充分利用节点信息的问题,提出融合知识图谱和图注意力网络的引文推荐算法(C-KGAT)。首先,使用TransR算法将知识图谱信息映射为低维稠密向量,以获取节点的嵌入特征表示;其次,利用图注意力网络通过多通道融合机制聚合邻居节点信息以丰富目标节点的语义,并捕获节点间高阶连通性;接着,在不影响网络的深度或宽度的情况下,引入动态卷积层动态地聚合邻居节点信息以提升模型的表达能力;最后,通过预测层计算用户和引文的交互概率。在公开数据集AAN(ACL Anthology Network)和计算机科学文献库(DBLP)上的实验结果表明,所提算法的效果优于所有对比模型,所提算法的MRR(Mean Reciprocal Rank)相较于次优模型NNSelect分别提升了6.0和3.4个百分点,所提算法的精确率和召回率指标也有不同程度的提升,验证了算法的有效性。

Incorporating User’s Preferences into Scholarly Publications Recommendation

Over the years, there has been increasing growth in academic digital libraries. It has therefore become overwhelming for researchers to determine important research materials. In most existing research works that consider scholarly paper recommendation, the researcher’s preference is left out. In this paper, therefore, Frequent Pattern (FP) Growth Algorithm is employed on potential papers generated from the researcher’s preferences to create a list of ranked papers based on citation features. The purpose is to provide a recommender system that is user oriented. A walk through algorithm is implemented to generate all possible frequent patterns from the FP-tree after which an output of ordered recommended papers combining subjective and objective factors of the researchers is produced. Experimental results with a scholarly paper recommendation dataset show that the proposed method is very promising, as it outperforms recommendation baselines as measured with nDCG and MRR.

Knowledge Driven Paper Recommendation Using Heterogeneous Network Embedding Method

We search a variety of things over the Internet in our daily lives, and numerous search engines are available to get us more relevant results. With the rapid technological advancement, the internet has become a major source of obtaining information. Further, the advent of the Web2.0 era has led to an increased interaction between the user and the website. It has become challenging to provide information to users as per their interests. Because of copyright restrictions, most of existing research studies are confronting the lack of availability of the content of candidates recommending articles. The content of such articles is not always available freely and hence leads to inadequate recommendation results. Moreover, various research studies base recommendation on user profiles. Therefore, their recommendation needs a significant number of registered users in the system. In recent years, research work proves that Knowledge graphs have yielded better in generating quality recommendation results and alleviating sparsity and cold start issues. Network embedding techniques try to learn high quality feature vectors automatically from network structures, enabling vector-based measurers of node relatedness. Keeping the strength of Network embedding techniques, the proposed citation-based recommendation approach makes use of heterogeneous network embedding in generating recommendation results. The novelty of this paper is in exploiting the performance of a network embedding approach i.e., matapath2vec to generate paper recommendations. Unlike existing approaches, the proposed method has the capability of learning low-dimensional latent representation of nodes (i.e., research papers) in a network. We apply metapath2vec on a knowledge network built by the ACL Anthology Network (all about NLP) and use the node relatedness to generate item (research article) recommendations.

基于VBA语言的WebofScience数据库论文引文检索软件设计与实践

在分析国内现有论文查收查引软件情况的基础上,介绍了一种基于VBA语言、通过SeleniumBasic技术实现批量引文检索的软件的详细设计、实现过程和关键代码,通过应用效果对比,指出该软件在引文检索上有较强的快捷性、可移植性和高效性,并提出了该软件的扩展方向。

词汇位置功能视角下的交叉领域知识生长研究

挖掘记载科学知识的交叉领域文献,可以探究交叉领域形成和发展中的知识流动和知识创造规律。本文依据词汇在交叉领域文献中的位置功能,提出了“文献空间观”和交叉领域知识生长过程模型,包括知识吸纳、知识内化和知识创新三大环节,进而构建一种全文本分析方法框架实现对交叉领域知识生长过程的量化分析。以生物信息学领域作为案例开展了实证分析,研究结果发现,知识内化与知识吸纳高度相关,数量差距约6倍,但变化趋势相同;领域知识创新第一次高峰出现时间晚于知识吸纳和内化4年左右;随着学科不断成熟,即时内化率保持相对稳定,总内化率降低,新增吸纳知识的内化时滞降低,内化知识激发知识创新的效率越来越高。本文所提出的面向交叉领域知识生长的全文本分析方法框架,能够丰富学术文献全文本内容分析方法体系。