知识图谱研究进展

随着大数据时代的到来,知识工程受到了广泛关注,如何从海量的数据中提取有用的知识,是大数据分析的关键。知识图谱技术提供了一种从海量文本和图像中抽取结构化知识的手段,从而具有广阔的应用前景。本文首先简要回顾知识图谱的历史,探讨知识图谱研究的意义。其次,介绍知识图谱构建的关键技术,包括实体关系识别技术、知识融合技术、实体链接技术和知识推理技术等。然后,给出现有开放的知识图谱数据集的介绍。最后,给出知识图谱在情报分析中的应用案例。     

新世纪国际人工智能研究领域可视化分析

目的：全面了解新世纪国际人工智能领域的研究现状与研究热点。方法：运用TDA软件,利用文献统计分析、关键词共现分析的方法揭示研究热点。结果：国际人工智能领域文献量呈上升趋势,其中美国发文量排名第一,中国位居第六。人工智能的主研究领域包括计算机科学、工程学、自动化与控制系统3个学科。研究热点为遗传算法、神经网络和机器学习。结论：新世纪国际人工智能研究涉及多个学科,研究热点集中在知识获取、知识表示和问题求解3个宏观层面,神经网络、遗传算法和机器学习是研究者们关注的核心。     

对我国情报学研究中三个重要问题的反思

对我国情报学研究中的情报、情报学和竞争情报三个基本问题谈了我的理解,以期促进我国情报工作和情报学研究的发展,推动中国情报界对这些问题的反思。     

中小企业情境知识应用调查分析及建模探讨——以河南新乡地区中小企业为例

以中小企业中IC工作部门的设置、人员组成结构及高层对此项工作的态度等诸多现实问题为切入点,在阐述知识管理中情境的概念、特点和分类基础上,分析情境与知识管理的关系,对区域性企业KM与IC工作开展的现状和存在的问题进行调研。为在企业知识管理中系统有效地开展KM和应用情境,利用集成情境知识管理(KMIC)方法及目标,实现企业KMIC的关键技术,并构建企业KMIC系统,使KM和竞争情报工作更加切合本地企业的实际状况提供一定的佐证。最后证明企业的发展是由对IC工作的重视程度以及KMIC的效果来决定的。     

Criticism From Artificial Agents: Prior Interaction Reduces Negative Effects

The present study explored interaction between humans and artificial agents from a relational perspective. According to theories on interpersonal communication and relationships, criticism by known agents, as compared to unknown agents, could be less negative (the buffering effect of relationship) or more negative (the expectancy violation effect) to human-agent relationships. Using online survey tools, participants who had interacted with an agent were led to one of the same or different agent conditions, in which the same or a new agent gave negative comments (criticism) to participants. Participants evaluated the agent and the interaction with it before and after receiving the criticism. Results showed that a brief acquaintanceship with agents lessened the negative effects of criticism. Participants’ evaluations decreased to a lesser degree when the negative comments were given by the acquaintance agent rather than the stranger agent. Thus, the buffering effect of relationship was supported in human-agent relationships.     

How and where is artificial intelligence in the public sector going? A literature review and research agenda

To obtain benefits in the provision of public services, managers of public organizations have considerably increased the adoption of artificial intelligence (AI) systems. However, research on AI is still scarce, and the advance of this technology in the public sector, as well as the applications and results of this strategy, need to be systematized. With this goal in mind, this paper examines research related to AI as applied to the public sector. A review of the literature covering articles available in five research databases was completed using the PRISMA protocol for literature reviews. The search process yielded 59 articles within the scope of the study out of a total of 1682 studies. Results show a growing trend of interest in AI in the public sector, with India and the US as the most active countries. General public service, economic affairs, and environmental protection are the functions of government with the most studies related to AI. The Artificial Neural Networks (ANN) technique is the most recurrent in the investigated studies and was pointed out as a technique that provides positive results in several areas of its application. A research framework for AI solutions for the public sector is presented, where it is demonstrated that policies and ethical implications of the use of AI permeate all layers of application of this technology and the solutions can generate value for functions of government. However, for this, a prior debate with society about the use of AI in the public sector is recommended.     

图书馆自行开发人脸识别应用系统的实践与思考——以中国计量大学图书馆为例

在智慧图书馆建设的大背景下,以人脸识别为代表的人工智能技术正成为我国图书馆信息化研究的热点之一。本文分析了我国图书馆人脸识别技术研究和应用现状,介绍了中国计量大学图书馆自行开发人脸识别应用系统的设计理念和实施情况,并结合这一实践过程,探讨了图书馆开发人脸识别应用系统应该注意的问题,以期为我国高校图书馆自行开发此类应用系统提供参考借鉴。     

Nonparametric functional analysis under joint estimation with applications to identifying highly cited papers

This article introduces a nonparametric methodology combining the strengths of binary regression and latent variable formulations, while overcoming their disadvantages. The mathematical results are implemented through a novel Bayesian Hierarchical estimation methodology called Latent Adaptive Hierarchical Expectation Maximization Like algorithm. Requiring minimal assumptions, it extends extant methodologies, and in simulation studies gives better prediction and inference performances for asymmetric data generating processes. A new classification statistic, called Adjusted Receiver Operating Curve Statistic is also introduced. Utilizing it we demonstrate better overall model fit, inference and prediction performance of the proposed methodology over widely used existing methods in the sciences. In addition, the methodology can be used to perform model diagnostics for any model specification. This is a highly useful result, and it extends existing work for categorical model diagnostics broadly across the sciences. Furthermore, the mathematical results also highlight important new findings regarding the interplay of statistical significance and scientific significance. Finally, the methodology is applied to identifying highly-cited papers in the social sciences in a joint estimation framework. The results indicate that the methodology outperforms widely used existing artificial intelligence and machine learning models with very few Monte Carlo iterations. In Scientometric application, it finds Journal Impact Factor to be more important than Keyword Popularity parameters for explaining citation outcomes in select social science fields. It further finds that the percentage change in Published Popularity may also help to explain citation outcomes in the field. The findings appear to be new to the Scientometric field.     

企业竞争情报管理系统的构建研究*

从企业信息管理系统的发展演变出发, 介绍了企业竞争情报管理系统的概念和特点, 重点论述了企业竞争情报管理系统及其子系统结构、功能及其实现方式, 并介绍了相关的企业竞争情报源问题。     

Psychological Well-Being as a Function of Affectionate Communication and Emotional Intelligence

This study examined indicators of psychological well-being as a function of affectionate communication and emotional intelligence. Two hundred and seventeen college-aged students completed measures of expressed and received affection; mental health, happiness, self-esteem, depression, and stress; and emotional intelligence. Results indicated that expressed affection is negatively associated with stress; received affection is positively associated with mental health and self-esteem and negatively associated with depression and stress. Furthermore, results provided support for models in which emotional intelligence impacts psychological well-being after accounting for the variance explained by expressed and received affection.