A citation-based document retrieval system for finding research expertise

Current citation-based document retrieval systems generally offer only limited search facilities, such as author search. In order to facilitate more advanced search functions, we have developed a significantly improved system that employs two novel techniques: Context-based Cluster Analysis (CCA) and Context-based Ontology Generation frAmework (COGA). CCA aims to extract relevant information from clusters originally obtained from disparate clustering methods by building relationships between them. The built relationships are then represented as formal context using the Formal Concept Analysis (FCA) technique. COGA aims to generate ontology from clusters relationship built by CCA. By combining these two techniques, we are able to perform ontology learning from a citation database using clustering results. We have implemented the improved system and have demonstrated its use for finding research domain expertise. We have also conducted performance evaluation on the system and the results are encouraging.     

Investigating effectiveness and user acceptance of semantic social tagging for knowledge sharing

Social tagging systems enable users to assign arbitrary tags to various digital resources. However, they face vague-meaning problems when users retrieve or present resources with the keyword-based tags. In order to solve these problems, this study takes advantage of Semantic Web technology and the topological characteristics of knowledge maps to develop a system that comprises a semantic tagging mechanism and triple-pattern and visual searching mechanisms. A field experiment was conducted to evaluate the effectiveness and user acceptance of these mechanisms in a knowledge sharing context. The results show that the semantic social tagging system is more effective than a keyword-based system. The visualized knowledge map helps users capture an overview of the knowledge domain, reduce cognitive effort for the search, and obtain more enjoyment. Traditional keyword tagging with a keyword search still has the advantage of ease of use and the users had higher intention to use it. This study also proposes directions for future development of semantic social tagging systems.     

Ontology-driven approach for KPI meta-modelling,selection and reasoning

A key challenge in current Business Analytics (BA) is the selection of suitable indicators for business objectives. This requires the exploration of business data through data-driven approaches, while modelling business strategies together with domain experts in order to represent domain knowledge. In particular, Key Performance Indicators (KPIs) allow human experts to properly model ambiguous enterprise goals by means of quantitative variables with numeric ranges and clear thresholds. Besides business-related domains, the usefulness of KPIs has been shown in multiple domains, such as: Education, Healthcare and Agriculture. However, finding accurate KPIs for a given strategic goal still remains a complex task, specially due to the discrepancy between domain assumptions and data facts. In this regard, the semantic web emerges as a powerful technology for knowledge representation and data modeling through explicit representation formats and standards such as RDF(S) and OWL. By using this technology, the semantic annotation of indicators of business objectives would enrich the strategic model obtained. With this motivation, an ontology-driven approach is proposed to formally conceptualize essential elements of indicators, covering: performance, results, measures, goals and relationships of a given business strategy. In this way, all the data involved in the selection and analysis of KPIs are then integrated and stored in common repositories, hence enabling sophisticated querying and reasoning for semantic validation. The proposed semantic model is evaluated on a real-world case study on water management. A series of data analysis and reasoning tasks are conducted to show how the ontological model is able to detect semantic conflicts in actual correlations of selected indicators.     

The data scientist profile and its representativeness in the European e-Competence framework and the skills framework for the information age

The activities in our current world are mainly supported by data-driven web applications, making extensive use of databases and data services. Such phenomenon led to the rise of Data Scientists as professionals of major relevance, which extract value from data and create state-of-the-art data artifacts that generate even more increased value. During the last years, the term Data Scientist attracted significant attention. Consequently, it is relevant to understand its origin, knowledge base and skills set, in order to adequately describe its profile and distinguish it from others like Business Analyst. This work proposes a conceptual model for the professional profile of a Data Scientist and evaluates the representativeness of this profile in two commonly recognized competences/skills frameworks in the field of Information and Communications Technology (ICT), namely in the European e-Competence (e-CF) framework and the Skills Framework for the Information Age (SFIA). The results indicate that a significant part of the knowledge base and skills set of Data Scientists are related with ICT competences/skills, including programming, machine learning and databases. The Data Scientist professional profile has an adequate representativeness in these two frameworks, but it is mainly seen as a multi-disciplinary profile, combining contributes from different areas, such as computer science, statistics and mathematics.     

科技计划实施中的知识管理策略和框架研究

分析国家科技计划决策管理、项目实施、评审评估、监督监管等过程中的知识需求和活动规律。在实施要求、活动主体、任务层级、应用体系等方面研究分析科技计划知识管理的内涵,并提出面向整体绩效、实施过程、创新能力、知识资源体系等的知识管理策略。建立面向科技计划实施的知识管理框架,提出创新导向的组织要素、分布式知识链、面向实施周期的知识服务等要素。为推动科技计划的知识管理提供理论基础。     

A value-based perspective on supporting and hindering factors for research data management

Research data management (RDM) is an important prerequisite for a substantial and sustainable contribution to knowledge. There is a pressing need to examine why researchers hesitate to store, annotate, share and manage their research data. To model underlying psychological factors influencing researchers’ refusal to conduct RDM, the social exchange theory is extended with elements from prospect theory. Thus, it allows psychological insights into researchers’ decision-making, and illustrates the role of cost and benefit evaluations under uncertainty. Data management policies of a major funding agency were presented to a homogeneous group of researchers from the Information Systems community in Germany. The findings show that many researchers see a high value in RDM but are still held back by uncertainty. While the benefits seem to outweigh the costs, we ascertain the uncertainty factors which hinder researchers’ intention from conducting RDM in the future. The perceived fear of losing control over one's data is identified as a major hindering factor, while the fear of losing one's unique value did not prevail. The study provides novel insights for executives, administrators, and developers in higher education institutions, which are especially important for furthering RDM implementation strategies, as well as for system development.     

知识地图技术的发展趋势及评价研究

本文对知识地图的从导航和查询两种功能出发,研究知识地图的技术发展趋势。同时,根据传统的检索技术评价指标和知识地图自身的技术特点,提出了知识地图的评价指标。     

PPP项目残值风险本体知识库的构建与应用

从PPP项目残值风险的知识管理的角度出发,构建残值风险的本体知识库并结合具体案例进行应用分析。通过对PPP项目残值风险的文献回顾,对该风险的形成机理进行研究,建立PPP项目残值风险的知识框架。同时,定义知识框架里各个概念及其结构层次,并对概念之间的关系和属性进行知识表示,再根据SWRL语言对概念关系进行适当推理。最后,论文采用本体编辑软件Protégé构建Sky大桥项目的残值风险本体知识库,对知识进行更有效的表达,促进其可共享性和可传递性,实现更深入的信息查询和数据处理功能。     

A concept–relationship acquisition and inference approach for hierarchical taxonomy construction from tags

Taxonomy construction is a resource-demanding, top–down, and time consuming effort. It does not always cater for the prevailing context of the captured information. This paper proposes a novel approach to automatically convert tags into a hierarchical taxonomy. Folksonomy describes the process by which many users add metadata in the form of keywords or tags to shared content. Using folksonomy as a knowledge source for nominating tags, the proposed method first converts the tags into a hierarchy. This serves to harness a core set of taxonomy terms; the generated hierarchical structure facilitates users’ information navigation behavior and permits personalizations. Newly acquired tags are then progressively integrated into a taxonomy in a largely automated way to complete the taxonomy creation process. Common taxonomy construction techniques are based on 3 main approaches: clustering, lexico-syntactic pattern matching, and automatic acquisition from machine-readable dictionaries. In contrast to these prevailing approaches, this paper proposes a taxonomy construction analysis based on heuristic rules and deep syntactic analysis. The proposed method requires only a relatively small corpus to create a preliminary taxonomy. The approach has been evaluated using an expert-defined taxonomy in the environmental protection domain and encouraging results were yielded.     

广东创新驱动发展能力监测评价研究

在深入阐述创新驱动发展能力内涵及创新驱动发展机理的基础上,构建广东创新驱动发展能力监测评价指标体系,并基于时间序列及横向对比等维度开展对广东的创新驱动发展能力监测评价。评价结果显示,广东存在社会研发能力较弱、科技人力资源发展水平相对较低、创新财政投入力度不大等问题。提出广东下一步推进创新驱动发展的对策建议。     

新型研发机构监测系统模型构建与机制设计

针对新型研发机构及相关用户,设计和开发一种能够支持"按需申请监测服务、监测源集中采集、监测信息分散化管理、准确评估信息价值、信息内容可定制"的动态监测服务系统。建立基于Hadoop分布式系统的基础架构网络,采用MapReduce分布式运算方法,构建以动态监测模块、深度分析模块和功能响应模块为内核的模型框架,实现数据的"搜寻—计算—反馈"。在系统管理运营上,提出系统的运营机制、资源配置机制和安全管理机制。基于以上理论,开发并运营…"广东省新型研发机构创新监测平台",实践表明监测成效显著。     

微信公众号知识共享能力云评价模型构建方法及其应用

从社会认知理论、社会资本理论和技术接受理论综合考虑知识共享能力,以知识生成能力和知识扩散能力两大层面为出发点,构建包含2个一级指标、6个二级指标和23个指标属性的微信公众号知识共享能力评价指标体系;利用熵权法确定各级指标权重,建立微信公众号知识共享能力加权综合云评价模型。以微信公众号“环球科学”为例,详细阐述应用综合云评价模型进行知识共享能力评价的具体流程,得到“环球科学”知识共享能力的评价值,并根据评价结果对“环球科学”进一步增强知识共享能力提出合理化建议。     

Balancing breadth and depth of expertise for innovation: A 3M story

This study examines how inventors’ breadth and depth of expertise influence innovation in 3M, a company renowned for sustained innovation for over a century. While prior research tends to examine a single indicator – the technical success achieved by the inventor – our study differentiates between three indicators of a successful inventor: (1) the number of inventions generated; (2) the extent to which the inventor has a significant impact in his or her technical domain; and (3) the inventor's career success, in terms of the commercial value they have brought by converting their inventions into products that generate sales for commercial organizations. We found that breadth of inventor expertise relates to the generation of many inventions, but not necessarily to those that are technically influential. Depth of inventor expertise enables individuals to generate technically influential inventions, as measured by patents granted. However, both breadth and depth of expertise are required for innovators to be deemed highly valuable, based on their records of effectively converting inventions into commercially successful products. Our study extends prior research on innovation in two ways. We provide a comprehensive view of how inventors’ expertise influences innovation and also show how inventors with different expertise profiles can contribute in unique ways to their organization.     

创新驱动发展能力内涵及评价研究述评

创新驱动发展战略是我国全局发展的核心,正确理解创新驱动发展的内涵及监测评价指标,对全面推动创新驱动发展战略具有重要意义。本文多维度深入探讨创新驱动发展内涵,全面梳理创新驱动发展能力评价的研究进展,总结当前研究进展特点及存在问题,提出下一步开展相关研究的展望     

The basis for bibliomining: Frameworks for bringing together usage-based data mining and bibliometrics through data warehousing in digital library services

Over the past few years, data mining has moved from corporations to other organizations. This paper looks at the integration of data mining in digital library services. First, bibliomining, or the combination of bibliometrics and data mining techniques to understand library services, is defined and the concept explored. Second, the conceptual frameworks for bibliomining from the viewpoint of the library decision-maker and the library researcher are presented and compared. Finally, a research agenda to resolve many of the common bibliomining issues and to move the field forward in a mindful manner is developed. The result is not only a roadmap for understanding the integration of data mining in digital library services, but also a template for other cross-discipline data mining researchers to follow for systematic exploration in their own subject domains.     

新型研发机构监测系统的模型构建与机制设计

针对新型研发机构及相关用户,设计和开发一种能够支持“按需申请监测服务、监测源集中采集、监测信息分散化管理、准确评估信息价值、信息内容可定制”的动态监测服务系统。建立基于Hadoop分布式系统的基础架构网络,采用mapReduce分布式运算方法,构建以动态监测模块、深度分析模块和功能响应模块为内核的模型框架,实现数据的“搜寻－计算－反馈”。在系统管理运营上,提出系统的运营机制、资源配置机制和安全管理机制。基于以上理论,开发并运营了“广东省新型研发机构创新监测平台”,实践表明监测成效显著。     

高校实施发展性教师评价的必要性

为了保证高等教育健康、稳定的发展．建立一支高质量、高水平的教师队伍势在l必行,一套科学、有效的教师评价机制是实现高等教育健康、稳定发展的前提,而科学的教师评价体系的构建是高校管理工作的关键。文章归纳了高校实施发展性教师评价制度的意义．分析了高校实施发展性教师评价的可能性．提出高校实施发展性教师评价的策略．     

大学教师知识效能评价指标和影响因素体系的构建研究

在回顾国内外大学教师评价研究文献的基础上,对知识效能进行了理论分析,解释了大学教师知识效能评价的涵义.根据调查统计数据,运用单一样本T检验和探索性因子分析的方法,构建了大学教师知识效能评价指标和影响因素体系.     

中西信用评价技术发展的逻辑及其比较研究

摘要：我国信用评价技术长期围绕“评价指标体系”的思辩式重复研究的现状和水平,不仅远远落后于西方发达国家,而且也远不能满足国内蓬勃发展的信用建设事业的实践需要,更无力承担当前金融危机下信用风险防范和未来信用秩序提升的应有功能。鉴此,本文试图在梳理西方信用评价技术发展逻辑的基础上,探索信用评价技术发展的一般规律;并进一步通过比较分析中西信用评价技术发展的内在动因和外在条件的差异,剖析我国信用技术研究落后的深层原因;最终得出内在基础、必要条件和充分条件是我国信用评价技术创新发展三大必备要件的基本结论。     

国立科研机构形象资产管理体系的规划与思考

形象资源的建设与维护是当代公共机构必须面对的使命,是组织影响力和发展空间的重要组成部分。而将组织的形象资源转化为形象资产进行管理则是新的理论及实践探索,其理论主要借鉴自无形资产计量方法和智力资本管理理论。文章诠释了形象资产管理的核心概念与内涵,思考了相应的建设路径,在理论解析基础上,将建设视野聚焦于国立科研机构这一"国有资产+科技共同体"的专有类型,期望提炼出将该类型机构的形象资源转化为形象资产进行管理的路径特征。文章以中科院形象资产体系的架构和工作规划清单的先期探索为案例,希望能为国立科研机构形象资产管理体系建设提供管理方法与推进路径的参考。