基于作者共被引的馆藏资源深度聚合模式与服务探析——以CSSCI中图书情报领域本体研究为例

利用作者共被引分析,探索基于作者共被引的馆藏资源聚合模式,并在实现资源聚合的基础上,探析聚合服务的途径。以图书情报领域研究本体的作者为例进行实证分析,聚合得到5个学术共同体以及各组的研究主题,包括理论研究、面向数字图书馆、基于概念格、知识地图、模块化、叙词表的本体研究等。结果显示基于作者共被引的馆藏资源聚合是一种有效的馆藏资源聚合方式。     

Tax-folk混合导航:社会化标注系统资源聚合的新模型

Web2.0环境下,专家分类法与大众分类法呈现出互补和融合的态势,tax-folk混合导航已成为优化社会化标注系统资源聚合的一种新思路。本文通过剖析tax-folk混合导航模型生成机理,采用形式概念分析方法,构建出包含数据准备、概念格构建、概念格分析、tax-folk映射、tax-folk混合导航树和输出与评价六个模块的tax-folk混合导航模型。研究发现,tax-folk混合导航模型兼顾了专家分类法和大众分类法的优势,实现了树状“干强枝繁叶茂”的资源聚合,有效提高了社会化标注系统中资源的可查找性。图10。表1。参考文献11。     

Folksonomy与SNA资源聚合类比研究

随着国内外数字资源多维度聚合研究的不断深入,资源聚合方法的改进和融合也成为学术研究热点。在对分众分类和社会网络的基本结构进行简要阐述的基础上,文章对Folksonomy和SNA两种资源聚合方法,从聚合理念、维度、实现技术和具体应用四个方面进行比较分析,指出Folksonomy和SNA在资源聚合中的优势和不足,并分析了资源聚合中Folksonomy和SNA的融合情景,以期为相关研究和后续的实践应用工作提供理论指导。     

Semi-supervised ranking aggregation

Ranking aggregation is a task of combining multiple ranking lists given by several experts or simple rankers to get a hopefully better ranking. It is applicable in several fields such as meta search and collaborative filtering. Most of the existing work is under an unsupervised framework. In these methods, the performances are usually limited especially in unreliable case since labeled information is not involved in. In this paper, we propose a semi-supervised ranking aggregation method, in which preference constraints of several item pairs are given. In our method, the aggregation function is learned based on the ordering agreement of different rankers. The ranking scores assigned by this ranking function on the labeled data should be consistent with the given pairwise order constraints while the ranking scores on the unlabeled data obey the intrinsic manifold structure of the rank items. The experimental results on toy data and the OHSUMED data are presented to illustrate the validity of our method.     

基于Web3．0的个性化信息聚合技术研究

通过分析个性化信息聚合的现状,揭示其在满足用户个性化信息需求上的缺陷,阐释基于Web3．0的个性化信息聚合的必要性,并进一步剖析基于Web3．0的个性化信息聚合的关键技术,在此基础上,提出基于Web3．0的个性化信息聚合平台的基本架构。     

Differential risk for developmental reading disorders in the offspring of compensated versus noncompensated parents

When one or both parents have a history of developmental reading disorder (RD) in childhood, the risk to their offspring for developing reading problems is substantially increased. However, risk research has usually assumed a stability of reading problems across the lifespan (i.e., if a parent was affected in childhood, he or she remains affected in adulthood). Yet, some individuals with RD in childhood compensate for the disorder as they grow older. Both an environmental and genetic hypothesis would predict that the risk for RD in offspring will vary as a function of parental compensation. This study examined whether risk to offspring was dependent on the parents' successful or unsuccessful compensation for their childhood reading problems. Two large family data sets were analyzed (N=907). Diagnoses with either an age discrepant or IQ discrepant criteria essentially showed that having at least one still affected parent (i.e., RD both as a child and as an adult) put the offspring at a higher risk for RD than having at least one compensated parent (i.e., RD as a child but not as an adult). The lowest risk to an offspring occurred when both parents were never affected (i.e., not RD as a child or as an adult). The implications of these findings are discussed with regard to counseling and early diagnosis of reading problems.     

基于概念交互关系原理的组织知识形成过程表述

本文分析了组织认知与思维的过程及内容,介绍了基于概念交互原理的组织知识形成过程的表述框架体系。概念交互原理以语言符号构造和语言符号分析为手段,将组织共同概念集作为组织知识表达的基元,组织共同概念集中概念与概念之间通过各种关系联结成为一个有机的系统,解决了组织知识形成过程的表述研究中所需的各种背景知识的构造、管理和使用问题。从而为组织知识管理建立起了有效的组织内在认知基础,也为我们解决知识管理工作中的其他难题提供了一个十分可行的方法论基础。     

人才聚集中自我冲突的消减与人才聚集效应研究

当人才流动达到一定规模就会产生人才聚集现象，同时也会发生人才自我冲突，形成人才聚集的负效应，但若对冲突化解和消减得当，也会形成人才聚集的正效应。在分析人才聚集现象与人才聚集效应后，研究了在人才聚集过程中影响人才聚集现象向人才聚集效应转变的人才自我；中突的动因，并提出了化解和消减人才自我冲突的方式。     

Effect of H-type Aggregation on the Photoelectric Conversion of Tetrasulfonated Phthalocyanine Adsorbed on Nanostructured TiO_2 Electrode

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Sentiment aggregation of targeted features by capturing their dependencies: Making sense from customer reviews

Ideation is an important phase in the new product development process at which product designers innovate and select novel ideas that can be added as features to an existing product. One way to find novel ideas is to transfer uncommon features of products of other domains and integrate them into the product to be improved. However, before incorporating such targeted features into the product, they need to be evaluated against the customers’ acceptance in social media using sentiment aggregation tools. Despite the many studies in sentiment analysis, mapping the customers’ opinions towards both high-level and technical features of a product extracted from social media to their best corresponding component in that product is still a challenge. Furthermore, none of the existing approaches ascertains the sentiment value of a targeted feature by capturing its dependencies on other features. In this paper, to address these drawbacks, we propose the sentiment aggregation framework for targeted features (SA-TF). SA-TF determines the sentiment of a targeted feature by assisting product designers in the tasks of mapping the features discussed in the reviews to the right product components, sentiment aggregation and considering feature dependencies to determine their polarity. The superiority of the different phases of SA-TF is demonstrated with experiments and comparing it with an existing approach.