The scientific and technological cross-space: Is technological diversification driven by scientific endogenous capacity?

The principle of relatedness allows us to explore the likelihood that territories diversify their current technological portfolios based on the global co-occurrence patterns of technologies. Countries that excel at developing semiconductors should develop mobile phones because both technologies require similar endogenous capacities, including scientific knowledge. However, thus far, studies have been mostly limited to the knowledge common base assumption and have not questioned enough whether different scientific endogenous capacities may be behind similar diversification performances. To address this question, we introduce the concept of scientific and technological cross-density, which we define as the average proximity of a new potential technology to a country's scientific and technological portfolio. To conceptualize and measure the effect that scientific and technological cross-density may have on technological diversification, we applied a two-stage methodology to a sample of 182 countries during the 1988–2014 period. First, we build a network, the science and technology cross-space, (sci-tech cross-space), which relates knowledge and technologies based on co-occurrence values. Second, we estimate the effect of scientific-technological cross-density and technological density on technological diversification at the country level. We find that the more a new technology is related to a country's scientific portfolio, the greater its entry probability; additionally, the effect of technological density on technological diversification is greater than the effect of scientific and technological density.     

知识主题演化预测研究

【目的/意义】针对现有主题演化方法难以满足预测目的的需求,本文从知识动态发展的角度出发,构建知 识主题演化预测模型,为探究科学领域发展脉络与研究趋势提供方法。【方法/过程】通过Lda模型抽取知识主题,利 用马尔可夫和隐马尔可夫构建主题稳态与主题热度的演化预测模型。【结果/结论】以云计算领域的科学文献作为 实证分析对象,结果表明本模型可以根据历史数据来预测知识主题稳态分布情况与未来热度趋势,且在热度预测 精度上较灰色模型更高。【创新/局限】本文只考虑了横向主题内部的热度高低变化,没有进行纵向维度上各知识主 题间的对比。     

The geography of scientific citations

Science’s main norms prescribe scientists to use citations as acknowledgements of cognitive content irrespective of geographical location. Previous studies, however, suggested that there is a considerable geographical bias in scientific citations. We argue that this geographical bias does not, in itself, falsify the notion that citations reflect acknowledgement of cognitive content, because cognitively related knowledge may be geographically concentrated as well. We analyse the role of organizational, regional and national co-location on citation likelihood for 5.5 million article pairs, and find that the geographical bias in citations is weak once cognitive relatedness is accounted for. Furthermore, we find that the effect of co-location on citation likelihood is strongest at the organizational level, weaker at the regional level, and weakest at the national level. In addition, we show that geographical co-location particularly increases the citation likelihood between two papers when knowledge relatedness between articles is low, suggesting that interdisciplinary research benefits most from co-location. Finally, we find that, when knowledge relatedness is high, the effect of geographical co-location on citation likelihood is non-existent. We discuss the implications regarding policies aimed to discourage strategic citations and to foster interdisciplinary research.     

Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis

Topic evolution has been described by many approaches from a macro level to a detail level, by extracting topic dynamics from text in literature and other media types. However, why the evolution happens is less studied. In this paper, we focus on whether and how the keyword semantics can invoke or affect the topic evolution. We assume that the semantic relatedness among the keywords can affect topic popularity during literature surveying and citing process, thus invoking evolution. However, the assumption is needed to be confirmed in an approach that fully considers the semantic interactions among topics. Traditional topic evolution analyses in scientometric domains cannot provide such support because of using limited semantic meanings. To address this problem, we apply the Google Word2Vec, a deep learning language model, to enhance the keywords with more complete semantic information. We further develop the semantic space as an urban geographic space. We analyze the topic evolution geographically using the measures of spatial autocorrelation, as if keywords are the changing lands in an evolving city. The keyword citations (keyword citation counts one when the paper containing this keyword obtains a citation) are used as an indicator of keyword popularity. Using the bibliographical datasets of the geographical natural hazard field, experimental results demonstrate that in some local areas, the popularity of keywords is affecting that of the surrounding keywords. However, there are no significant impacts on the evolution of all keywords. The spatial autocorrelation analysis identifies the interaction patterns (including High-High leading, High-Low suppressing) among the keywords in local areas. This approach can be regarded as an analyzing framework borrowed from geospatial modeling. Moreover, the prediction results in local areas are demonstrated to be more accurate if considering the spatial autocorrelations.     

学科研究主题发展等级划分与趋势预测方法——以我国智慧图书馆研究为例

【目的/意义】主题发展等级划分是信息组织研究的基础性问题,也是科研人员和科研管理部门进行研究选题和学科服务的重要工作,对学科研究主题进行高效的发展等级划分与趋势预测,能够帮助相关科研人员和机构把握学科领域研究态势,准确做出科研决策。【方法/过程】本文结合主题模型、Sen’s斜率估计法、Mann-Kendall法、指数平滑法,提出一种学科研究主题发展等级划分与趋势预测方法。首先,在主题识别的基础上,形成主题发文度和主题引文度两个指标,并参考波士顿矩阵对学科研究主题发展等级进行划分;然后,融合研究主题发文量、被引量和下载量,形成主题热力度指标,采用指数平滑法对研究主题未来发展态势进行预测。【结果/结论】以我国“智慧图书馆”研究的实验表明,本文所提方法能够对学科领域研究主题进行全方位、细粒度地发展等级划分和趋势预测。【创新/局限】本文所提方法对其他学科研究主题的分析具有普适性,为实现动态情报分析提供了新的视角,局限在于需要提高主题建模的可解读性,并进一步优化趋势预测方法。     

Mining user interests over active topics on social networks

Inferring users’ interests from their activities on social networks has been an emerging research topic in the recent years. Most existing approaches heavily rely on the explicit contributions (posts) of a user and overlook users’ implicit interests, i.e., those potential user interests that the user did not explicitly mention but might have interest in. Given a set of active topics present in a social network in a specified time interval, our goal is to build an interest profile for a user over these topics by considering both explicit and implicit interests of the user. The reason for this is that the interests of free-riders and cold start users who constitute a large majority of social network users, cannot be directly identified from their explicit contributions to the social network. Specifically, to infer users’ implicit interests, we propose a graph-based link prediction schema that operates over a representation model consisting of three types of information: user explicit contributions to topics, relationships between users, and the relatedness between topics. Through extensive experiments on different variants of our representation model and considering both homogeneous and heterogeneous link prediction, we investigate how topic relatedness and users’ homophily relation impact the quality of inferring users’ implicit interests. Comparison with state-of-the-art baselines on a real-world Twitter dataset demonstrates the effectiveness of our model in inferring users’ interests in terms of perplexity and in the context of retweet prediction application. Moreover, we further show that the impact of our work is especially meaningful when considered in case of free-riders and cold start users.     

基于非期望产出的不同职能城市土地利用效率分异及驱动因素探究

研究非期望产出条件下不同职能城市土地利用效率分异特征及其驱动因素,对于城市可持续发展具有重要意义。本文采用DEA中改进的EBM模型,测度了2004—2015年中国五大类268个城市的土地利用效率,并运用灰色关联模型探究了其主要驱动因素。结果发现：①忽视城市土地利用过程中的生态负效应,会引起对土地利用效率的高估,而考虑非期望产出,能更加真实地测算出土地利用效率;各职能城市的土地利用效率均存在较大差异,说明按城市职能类型来测度土地利用效率的科学性。②在不考虑非期望产出条件下,各职能城市的土地利用效率值曲线相对陡峭且周期性变化较多,而在考虑非期望产出条件下,效率值曲线变化相对平缓且周期性变化较少。③综合型城市主要受产业结构、土地市场化程度和环境治理能力的影响,资源型城市主要受产业结构、对外依赖程度和政府规制的影响,工业型城市主要受产业结构、土地市场化程度和地区能源结构的影响,文娱型城市主要受城镇化水平、经济发展水平和环境治理能力的影响,地方型城市主要受经济发展水平、城镇化水平和政府规制的影响。鉴于此,在提升城市土地利用效率时,应充分考虑土地的非期望产出和城市的异质性,制定出差异化的土地管理政策。     

New firm formation and regional knowledge production modes: Italian evidence

According to the knowledge-spillovers theory of entrepreneurship (KSTE), local knowledge spillovers affect entrepreneurial dynamics, because of knowledge asymmetries and uncertainty. Most of the empirical literature has tested this hypothesis using a measure of local knowledge stock. This paper is aimed at extending the framework by showing that the domains over which local knowledge spans are also important. The paper investigates the impact of the configuration of local knowledge bases on new firm formation dynamics by combining the KSTE framework with the recombinant knowledge approach. Local knowledge bases emerge from the combination of different knowledge inputs. These inputs may be closely or loosely related to one another. Technological differentiation and the relatedness degree of local competences can be interpreted as characteristics of the local knowledge base interacting with the knowledge filter and the entrepreneurial absorptive capacity. The paper proposes a taxonomy of regional modes of knowledge production and investigates new firm formation in 92 Italian NUTS 3 regions observed over the 1995–2009 time span. The results confirm that the availability of local knowledge pools is important, and show that the ‘rich integration’ mode is the configuration that favours the entrepreneurial process. Finally, the policy implications and avenues for further research are presented and discussed.     

Entrepreneurship in Cities

Impactful, growth-oriented entrepreneurship is a major research and policy focus. Building on arguments put forward by Jane Jacobs more than 50 years ago, we propose that local knowledge spillovers in a city are enhanced by human agency in that city (e.g. local psychological openness). This effect is critically amplified by the catalyst function of a favorable structural city environment that not only connects these agentic people (via urban density), but also facilitates the production and flow of new knowledge for these connected agentic people (via a diverse industry mix). This three-way interaction effect was confirmed in our empirical investigation of quality entrepreneurship across the MSAs (cities) in the US, using a large-scale dataset of the psychological profiles of millions of people. Local openness shows a robust positive effect on the level of quality entrepreneurship. This effect is further strengthened by a favorable structural city environment (i.e. high density and diversity) by up to 35%. Reviving Jacobs’ people focus, the results indicate that the best performing cities in terms of knowledge spillovers and economic performance are those that are not only home to, and attract, agentic people, but also empower these people by means of a physical and industrial city landscape that enables them to act in more innovative and entrepreneurial ways, as envisioned by Jacobs. We discuss the policy implications of our findings and an agenda for future research.     

Web环境下的科技新闻主题挖掘——以国际极地考察领域为例

提出一种基于LDA主题模型的科技新闻主题分析方法,选取2009—2018年中、澳、英、美4国极地科考新闻数据,从主题类型和主题强度角度进行主题演化分析。在中文新闻中,极地测绘等主题的热度上升,极地冰川科考主题的热度下降;在英文新闻中,热门主题为极地冰川科考与极地海洋科考;其余主题热度相对稳定。研究结果表明,该方法可以有效识别科技新闻主题并揭示其演化趋势,可以有效改善网络环境下科技情报分析的自动化程度。     

Topic discovery based on text mining techniques

In this paper, we present a topic discovery system aimed to reveal the implicit knowledge present in news streams. This knowledge is expressed as a hierarchy of topic/subtopics, where each topic contains the set of documents that are related to it and a summary extracted from these documents. Summaries so built are useful to browse and select topics of interest from the generated hierarchies. Our proposal consists of a new incremental hierarchical clustering algorithm, which combines both partitional and agglomerative approaches, taking the main benefits from them. Finally, a new summarization method based on Testor Theory has been proposed to build the topic summaries. Experimental results in the TDT2 collection demonstrate its usefulness and effectiveness not only as a topic detection system, but also as a classification and summarization tool.     

Mining social influence in science and vice-versa: A topic correlation approach

There is no doubt that scientific discoveries have always brought changes to society. New technologies help solve social problems such as transportation and education, while research brings benefits such as curing diseases and improving food production. Despite the impacts caused by science and society on each other, this relationship is rarely studied and they are often seen as different universes. Previous literature focuses only on a single domain, detecting social demands or research fronts for example, without ever crossing the results for new insights. In this work, we create a system that is able to assess the relationship between social and scholar data using the topics discussed in social networks and research topics. We use the articles as science sensors and humans as social sensors via social networks. Topic modeling algorithms are used to extract and label social subjects and research themes and then topic correlation metrics are used to create links between them if they have a significant relationship. The proposed system is based on topic modeling, labeling and correlation from heterogeneous sources, so it can be used in a variety of scenarios. We make an evaluation of the approach using a large-scale Twitter corpus combined with a PubMed article corpus. In both of them, we work with data of the Zika epidemic in the world, as this scenario provides topics and discussions on both domains. Our work was capable of discovering links between various topics of different domains, which suggests that some of the relationships can be automatically inferred by the sensors. Results can open new opportunities for forecasting social behavior, assess community interest in a scientific subject or directing research to the population welfare.     

北部湾城市群科技服务业空间分工及对外功能测度

基于已有研究成果和研究方法,采用产值密度、专业化指数、相对专业化指数和城市流强度等指标,对2012—2017年北部湾城市群科技服务业的空间集聚程度及其对外功能等进行测度。实证研究发现:(1)北部湾城市群科技服务业的集聚程度处于偏低状态,其中广东城市的空间集聚提高程度明显快于广西和海南的城市;(2)专业化集聚水平提高态势初步呈现,且在一定程度下产值密度较大的城市专业化水平未必较高,产值密度较小的城市专业化水平反而较高;(3)细分优势行业集聚表现为差异化态势,且外向功能差异较大,其中广西和广东的城市集聚优势明显,海南的城市集聚程度较低;(4)根据城市科技服务业对外功能总量和城市流强度大小,可将北部湾城市群划分为中心城市、受中心城市辐射较强及与其关联较为紧密的城市、中小城市等3个层次。最后,提出着力提高科技服务业优势行业集聚水平、充分利用中心城市辐射功能、加强与珠三角城市经济联系以及加强城市间经济联系网络构建等,促进北部湾城市群科技服务业集聚发展的政策建议。     

基于主题语义关联的扩展参照检索

相关概念的关联参照检索是概念检索的重要研究内容。本文提出了一种基于主题的语义关联的参照检索模型，通过融合语义网、本体论的相关知识及信息提取等语言处理技术，提取关于特定主题的文档的主题概念及概念之间的关联构成该主题的语义关联模型，并辅助于参照检索过程。     

A link-bridged topic model for cross-domain document classification

Transfer learning utilizes labeled data available from some related domain (source domain) for achieving effective knowledge transformation to the target domain. However, most state-of-the-art cross-domain classification methods treat documents as plain text and ignore the hyperlink (or citation) relationship existing among the documents. In this paper, we propose a novel cross-domain document classification approach called Link-Bridged Topic model (LBT). LBT consists of two key steps. Firstly, LBT utilizes an auxiliary link network to discover the direct or indirect co-citation relationship among documents by embedding the background knowledge into a graph kernel. The mined co-citation relationship is leveraged to bridge the gap across different domains. Secondly, LBT simultaneously combines the content information and link structures into a unified latent topic model. The model is based on an assumption that the documents of source and target domains share some common topics from the point of view of both content information and link structure. By mapping both domains data into the latent topic spaces, LBT encodes the knowledge about domain commonality and difference as the shared topics with associated differential probabilities. The learned latent topics must be consistent with the source and target data, as well as content and link statistics. Then the shared topics act as the bridge to facilitate knowledge transfer from the source to the target domains. Experiments on different types of datasets show that our algorithm significantly improves the generalization performance of cross-domain document classification.     

Identifying emerging topics in science and technology

The identification of emerging topics is of current interest to decision makers in both government and industry. Although many case studies present retrospective analyses of emerging topics, few studies actually nominate emerging topics for consideration by decision makers. We present a novel approach to identifying emerging topics in science and technology. Two large scale models of the scientific literature, one based on direct citation, and the other based on co-citation, are combined to nominate emerging topics using a difference function that rewards clusters that are new and growing rapidly. The top 25 emergent topics are identified for each year 2007 through 2010. These topics are classified and characterized in various ways in order to understand the motive forces behind their emergence, whether scientific discovery, technological innovation, or exogenous events. Topics are evaluated by searching for recent major awards associated with the topic or its key researchers. The evidence presented suggests that the methodology nominates a viable list of emerging topics suitable for inspection by decision makers.     

基于趋势分析的学科研究主题优先级战略坐标

对学科领域研究主题优先级进行战略分析,能够帮助科研人员及科研管理决策部门快速了解学科领域的研究态势、发现科学前沿,对提高科研产出起到积极的支持和促进作用。本文以图书情报学研究主题为例,采用主题提取与趋势分析相结合的方法,在提取学科主题基础上,从发文趋势和引文趋势两个维度,绘制含“研究贫乏区、热点区、冷点区、过热区”的我国图书情报学领域研究主题战略坐标。研究表明：本文提出的趋势战略坐标能够有效展示学科领域不同研究主题的发展阶段,全面、细致地呈现不同研究主题的发展等级。     

Studies on a multidimensional public opinion network model and its topic detection algorithm

“We the Media” networks are real time and open, and such networks lack a gatekeeper system. As netizens’ comments on emergency events are disseminated, negative public opinion topics and confrontations concerning those events also spread widely on “We the Media” networks. Gradually, this phenomenon has attracted scholarly attention, and all social circles attach importance to the phenomenon as well. In existing topic detection studies, a topic is mainly defined as an "event" from the perspective of news-media information flow, but in the “We the Media” era, there are often many different views or topics surrounding a specific public opinion event. In this paper, a study on the detection of public opinion topics in “We the Media” networks is presented, starting with the characteristics of the elements found in public opinions on “We the Media” networks; such public opinions are multidimensional, multilayered and possess multiple attributes. By categorizing the elements’ attributes using social psychology and system science categories as references, we build a multidimensional network model oriented toward the topology of public opinions on “We the Media” networks. Based on the real process by which multiple topics concerning the same event are generated and disseminated, we designed a topic detection algorithm that works on these multidimensional public opinion networks. As a case study, the “Explosion in Tianjin Port on August 12, 2015″ accident was selected to conduct empirical analyses on the algorithm's effectiveness. The theoretical and empirical research findings of this paper are summarized along the following three aspects. 1. The multidimensional network model can be used to effectively characterize the communication characteristics of multiple topics on “We the Media” networks, and it provided the modeling ideas for the present paper and for other related studies on “We the Media” public opinion networks. 2. Using the multidimensional topic detection algorithm, 70% of the public opinion topics concerning the case study event were effectively detected, which shows that the algorithm is effective at detecting topics from the information flow on “We the Media” networks. 3. By defining the psychological scores of single and paired Chinese keywords in public opinion information, the topic detection algorithm can also be used to judge the sentiment tendencies of each topic, which can facilitate a timely understanding of public opinion and reveal negative topics under discussion on “We the Media” networks.     

Scientific cities

Most scientific work is done in cities of differing sizes. One element of a national science policy may be deciding which cities are either over- or under-represented in terms of scientific concentration. Up till now, most decisions on this question have been political or economic in nature. However, it is possible to measure one aspect of this concentration so as to allow decision-makers a more rational basis for their determinations. The data used were the number of primary authors publishing in approximately 3700 major scientific journals. Because the scientific concentration can vary greatly with the size of city, the latter were divided into five categories. The cities which led each of these population classifications were Washington, Munich, Prague, Madison (Wisconsin), and Cambridge (Massachusetts). The data can also be used as a measure of the centralization of science in different countries. Of the eight major scientific nations, the USSR and Japan have the highest degree of centralization. However, if the effect on the degree of the major scientific city in each country is removed, Canada has the highest centralization. These conclusions may have ramifications for national science policies.     

应用型普通高校建筑学专业毕业设计选题探究

本文针对应用型普通高校建筑学专业毕业设计选题上存在的问题,在综合分析其原因的基础上,提出了建筑学专业毕业设计选题应充分考虑科学性、多样性、真实性等几方面因素,做到选题大小与难易应适中,并提倡“真题假做”,及针对学生具体情况灵活选题等科学选题建议,以达到提高普通高校建筑学专业毕业设计质量的目的。