首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献,搜索用时 78 毫秒
1.
首先根据产品创新任务对创新团队所获取的知识进行处理和组织;然后应用信息检索技术,对知识的内容进行管理,计算团队产生的主意和知识之间的相似度;根据相似度的大小实现知识的自动排序,帮助团队找出与主意最相关的知识;最终以人机结合的方式,由专家通过比较主意与最相关的知识快速识别出创新性主意。  相似文献   

2.
重大工程是极度复杂的社会经济系统,存在多维复杂性,包括组织复杂性、任务复杂性、技术复杂性、环境复杂性、制度复杂性和社会复杂性。本文基于案例推理(CBR)法,在案例检索过程中,结合熵权法和网络分析法(ANP)优化案例属性权重,通过相似度计算得出目标项目的相似源案例,利用源案例的技术经验解决目标项目的技术复杂性难题。同时,从目标项目的组织复杂性和任务复杂性方面,总结相关的复杂性治理策略,为探寻适合重大工程项目复杂性治理策略提供理论依据。  相似文献   

3.
对隐性知识外显案例实施有效适配,于知识应用乃至创新、实现乃至增值知识资源的价值,具有重要意义。本文深入研究了隐性知识外显案例适配机理。首先,采用毕达哥拉斯模糊集对知识属性值进行处理,建立知识表达系统;接着,运用K-Means算法对FCM聚类算法进行改进,压缩匹配空间、提升案例匹配效率;而后,基于PFS相关系数求解知识供需间的视图相似度,从而获得适配案例集。在此基础上,构建随机森林适配模型,并采用粒子群算法对其优化,以确保适配效果。与传统算法的对比实验验证了本文算法的比较优势。  相似文献   

4.
盛秋艳 《情报科学》2012,(8):1238-1241
本体技术作为一种能在语义和知识层次上描述概念体系的有效工具,给词语间相似度计算带来了新的机会。词语相似度的研究,是知识表示以及信息检索领域中的一个重要内容。本文利用本体来组织概念,计算概念之间的语义相似度,将语义相似度分成概念相似度和描述相似度,把概念相似度和描述相似度进行合并,生成最终的语义相似度。依据《中国分类主题词表》建立的计算机领域本体,验证了语义相似度计算方法的有效性。  相似文献   

5.
我国引进外资,一个主要的目的是学习外资的先进技术和先进管理技能,为此我国需要对外资企业进行深入的研究。本文对瑞泰公司的知识管理进行深入考察,其最大特点是推行规范化的知识软科学管理,来培育在华的核心竞争力;该种管理模式值得我国企业去思考和借鉴。  相似文献   

6.
刘岩  蔡虹  裴云龙 《科学学研究》2019,37(8):1471-1480
关键研发者是组织内核心技术的创造者,从知识角度研究影响企业成为关键研发者的因素具有重要研究意义。基于我国80家电子信息行业企业自2009年至2015年申请的发明专利数据,利用Logistic回归模型研究了企业技术知识基础相关与非相关多元度、知识整合能力对其成为关键研发者的影响。研究发现,企业技术知识基础相关多元度对其成为关键研发者具有正向作用;技术知识基础非相关多元度存在倒U型的影响。同时,知识整合能力在企业技术知识基础多元度与其成为关键研发者的影响中起到中介作用。  相似文献   

7.
【目的/意义】研究从用户节点和网络全局两个视角出发,基于用户相似度与信任度对虚拟学术社区中学者 进行推荐,提高学者推荐的质量。【方法/过程】首先,利用 LDA 主题模型挖掘学者发表的博文主题,计算博文相似 度;通过学者共同好友比例计算好友相似度;然后将博文相似度和好友相似度融合计算用户相似度;最后,融合用 户相似度和信任度进行学者推荐。【结果/结论】提出虚拟学术社区中基于用户相似度与信任度的学者推荐方法,综 合利用用户节点和网络全局信息,为虚拟学术社区用户进行学者推荐。【创新/局限】从用户节点和网络全局两个角 度进行学者信息融合,有效提高了虚拟学术社区中学者推荐的质量。局限在于本文主要考虑的是学者在网络全局 中的信任度,用户节点间的交互信任关系还有待进一步研究。  相似文献   

8.
郭京京 《科研管理》2014,35(11):35-43
通过四家产业集群企业的案例研究,本文考察了技术学习惯例在外部知识获取策略与企业创新绩效之间的中介作用机制。研究发现:技术学习惯例强度在深度优先的外部知识获取策略影响企业技术创新绩效的机制中起中介作用,技术学习惯例多样性在广度优先的外部知识获取策略影响企业技术创新绩效的机制中起中介作用。论文工作深化了对产业集群企业内部技术学习行为和创新过程的理解,拓展了组织惯例领域的实证研究。  相似文献   

9.
[目的/意义]专利引文分析是专利分析研究的重要内容。传统专利引文分析仅分析专利文献中明确标示的物理引用专利数据,不能够准确真实反映专利之间的引用关系,难以准确揭示专利之间的技术相似度。专利语义引用识别有利于准确真实揭示专利间的潜在语义联系,为专利的继承与创新评价提供参考,有助于专利授权前的专利审核和专利授权后的专利评价。[方法/过程]首先,基于规则和句法分析抽取了专利的特征知识元;其次,利用Sentence-BERT和Word2Vec对专利特征知识元及专利标题摘要文本进行向量化表示;再次,根据余弦相似度计算专利的特征相似度和整体相似度,结合专利申请日期的先后顺序得到专利的语义引用专利集;最后,采用量子计算领域专利数据进行了实验研究。[结果/结论]该专利语义引用识别方法能够实现语义引用专利的有效识别,有利于评价专利的技术新颖性、创造性和实用性,为专利审核和专利价值评估工作提供支持。  相似文献   

10.
知识检索概念辨析   总被引:7,自引:1,他引:6  
本文对已有的一些知识检索的定义进行了辨析,给出了知识检索的一个新定义,指出知识检索与智能信息检索在内涵方面相似但不完全相同,并对知识管理技术在知识检索中的作用进行了讨论。  相似文献   

11.
Aspect level sentiment analysis is important for numerous opinion mining and market analysis applications. In this paper, we study the problem of identifying and rating review aspects, which is the fundamental task in aspect level sentiment analysis. Previous review aspect analysis methods seldom consider entity or rating but only 2-tuples, i.e., head and modifier pair, e.g., in the phrase “nice room”, “room” is the head and “nice” is the modifier. To solve this problem, we novelly present a Quad-tuple Probability Latent Semantic Analysis (QPLSA), which incorporates entity and its rating together with the 2-tuples into the PLSA model. Specifically, QPLSA not only generates fine-granularity aspects, but also captures the correlations between words and ratings. We also develop two novel prediction approaches, the Quad-tuple Prediction (from the global perspective) and the Expectation Prediction (from the local perspective). For evaluation, systematic experiments show that: Quad-tuple PLSA outperforms 2-tuple PLSA significantly on both aspect identification and aspect rating prediction for publication datasets. Moreover, for aspect rating prediction, QPLSA shows significant superiority over state-of-the-art baseline methods. Besides, the Quad-tuple Prediction and the Expectation Prediction also show their strong ability in aspect rating on different datasets.  相似文献   

12.
Most document clustering algorithms operate in a high dimensional bag-of-words space. The inherent presence of noise in such representation obviously degrades the performance of most of these approaches. In this paper we investigate an unsupervised dimensionality reduction technique for document clustering. This technique is based upon the assumption that terms co-occurring in the same context with the same frequencies are semantically related. On the basis of this assumption we first find term clusters using a classification version of the EM algorithm. Documents are then represented in the space of these term clusters and a multinomial mixture model (MM) is used to build document clusters. We empirically show on four document collections, Reuters-21578, Reuters RCV2-French, 20Newsgroups and WebKB, that this new text representation noticeably increases the performance of the MM model. By relating the proposed approach to the Probabilistic Latent Semantic Analysis (PLSA) model we further propose an extension of the latter in which an extra latent variable allows the model to co-cluster documents and terms simultaneously. We show on these four datasets that the proposed extended version of the PLSA model produces statistically significant improvements with respect to two clustering measures over all variants of the original PLSA and the MM models.  相似文献   

13.
在计算机系统应用越来越广泛的今天,应用软件的规模不断扩大,复杂度不断提高,过程化程序设计、面向对象程序设计等传统的软件开发方法已渐渐不能适应这种变化。于是,一种新的程序开发方法:面向方面的编程(AOP:Aspect Oriented Programming)研究引起了国内外广泛关注。阐述了AOP产生的背景,介绍了Java程序设计相关的AOP主要框架及其应用实例。  相似文献   

14.
Online review mining has been used to help manufacturers and service providers improve their products and services, and to provide valuable support for consumer decision making. Product aspect extraction is fundamental to online review mining. This research is aimed to improve the performance of aspect extraction from online consumer reviews. To this end, we augment a frequency-based extraction method with PMI-IR, which utilizes web search in measuring the semantic similarity between aspect candidates and target entities. In addition, we extend RCut, an algorithm originally developed for text classification, to learn the threshold for selecting candidate aspects. Experiment results with Chinese online reviews show that our proposed method not only outperforms the state of the art frequency-based method for aspect extraction but also generalizes across different product domains and various data sizes.  相似文献   

15.
Aspect mining, which aims to extract ad hoc aspects from online reviews and predict rating or opinion on each aspect, can satisfy the personalized needs for evaluation of specific aspect on product quality. Recently, with the increase of related research, how to effectively integrate rating and review information has become the key issue for addressing this problem. Considering that matrix factorization is an effective tool for rating prediction and topic modeling is widely used for review processing, it is a natural idea to combine matrix factorization and topic modeling for aspect mining (or called aspect rating prediction). However, this idea faces several challenges on how to address suitable sharing factors, scale mismatch, and dependency relation of rating and review information. In this paper, we propose a novel model to effectively integrate Matrix factorization and Topic modeling for Aspect rating prediction (MaToAsp). To overcome the above challenges and ensure the performance, MaToAsp employs items as the sharing factors to combine matrix factorization and topic modeling, and introduces an interpretive preference probability to eliminate scale mismatch. In the hybrid model, we establish a dependency relation from ratings to sentiment terms in phrases. The experiments on two real datasets including Chinese Dianping and English Tripadvisor prove that MaToAsp not only obtains reasonable aspect identification but also achieves the best aspect rating prediction performance, compared to recent representative baselines.  相似文献   

16.
Customers commonly share opinions and experiences about products via the internet by means of social media and networking sites. The generated textual data is often analysed by means of Sentiment Analysis (SA) as means to assess customer opinions on product features more efficiently than through surveys. To enable a more objective product target setting, the impact of product feature performance changes on customer satisfaction is essential. Kano et al. (1984) presented a survey-based model to classify product features based on their impact on customer satisfaction to aid designers in their product target setting. Approaches extending the Kano model rely on customer surveys as input data. In addition, existing studies classifying extracted product features from textual data (e.g. product reviews) rarely provide a clear separation in terms of Kano categories. Thus, the impact of identified product features on customer satisfaction remains unknown to product designers. This paper presents a methodology for autonomously classifying extracted aspects from textual data into Kano categories. For verification purposes, two examples using coffee machine and smartphone user reviews are presented. Results indicate that the proposed methodology efficiently provides product designers with insightful customer information through the proposed aspect categorization.  相似文献   

17.
Sentiment analysis is a text classification branch, which is defined as the process of extracting sentiment terms (i.e. feature/aspect, or opinion) and determining their opinion semantic orientation. At aspect level, aspect extraction is the core task for sentiment analysis which can either be implicit or explicit aspects. The growth of sentiment analysis has resulted in the emergence of various techniques for both explicit and implicit aspect extraction. However, majority of the research attempts targeted explicit aspect extraction, which indicates that there is a lack of research on implicit aspect extraction. This research provides a review of implicit aspect/features extraction techniques from different perspectives. The first perspective is making a comparison analysis for the techniques available for implicit term extraction with a brief summary of each technique. The second perspective is classifying and comparing the performance, datasets, language used, and shortcomings of the available techniques. In this study, over 50 articles have been reviewed, however, only 45 articles on implicit aspect extraction that span from 2005 to 2016 were analyzed and discussed. Majority of the researchers on implicit aspects extraction rely heavily on unsupervised methods in their research, which makes about 64% of the 45 articles, followed by supervised methods of about 27%, and lastly semi-supervised of 9%. In addition, 25 articles conducted the research work solely on product reviews, and 5 articles conducted their research work using product reviews jointly with other types of data, which makes product review datasets the most frequently used data type compared to other types. Furthermore, research on implicit aspect features extraction has focused on English and Chinese languages compared to other languages. Finally, this review also provides recommendations for future research directions and open problems.  相似文献   

18.
黄河源区玛多县草地覆被变化分析   总被引:6,自引:1,他引:5  
张帅  邵全琴  刘纪远  徐新良 《资源科学》2008,30(10):1547-1554
草地是黄河源区最主要的覆被类型,采用黄河源区玛多县1977年、1990年和2003年3期夏季时相的MSS/TM遥感影像,通过人机交互式目视解译方法,首次采用草地覆被变化分类系统,提取了玛多县1977年~2003年间草地覆被时空变化特征,在此基础上分析了玛多县20世纪70年代以来的草地覆被时空变化特征,以及草地覆被变化与高程、坡度和坡向之间的关系。主要结论有:①玛多县的草地退化格局在20世纪70年代已经基本形成,之后退化过程一直持续发生,1990年~2003年间退化程度略有加强;②草地覆被变化以轻度和中度退化为主,主要类型为破碎化和覆盖度下降,这2种类型对应的草地面积比重为27.62%;③草地覆被变化主要发生在海拔4 200m~4 700m的范围;④草地覆被变化主要发生在坡度0°~15°范围内,在此以上的坡度发生的变化较少,坡度在5°以下时,各种草地覆被变化类型的发生随着坡度的增加而增加,当坡度大于12°时,变化的发生随着坡度的增加而减少;⑤随着坡向的变化,草地覆被的变化的发生没有太大的差异。  相似文献   

19.
Aspect-based sentiment analysis technologies may be a very practical methodology for securities trading, commodity sales, movie rating websites, etc. Most recent studies adopt the recurrent neural network or attention-based neural network methods to infer aspect sentiment using opinion context terms and sentence dependency trees. However, due to a sentence often having multiple aspects sentiment representation, these models are hard to achieve satisfactory classification results. In this paper, we discuss these problems by encoding sentence syntax tree, words relations and opinion dictionary information in a unified framework. We called this method heterogeneous graph neural networks (Hete_GNNs). Firstly, we adopt the interactive aspect words and contexts to encode the sentence sequence information for parameter sharing. Then, we utilized a novel heterogeneous graph neural network for encoding these sentences’ syntax dependency tree, prior sentiment dictionary, and some part-of-speech tagging information for sentiment prediction. We perform the Hete_GNNs sentiment judgment and report the experiments on five domain datasets, and the results confirm that the heterogeneous context information can be better captured with heterogeneous graph neural networks. The improvement of the proposed method is demonstrated by aspect sentiment classification task comparison.  相似文献   

20.
物联网是什么?物联网不是互联网、传感网、产品电子代码,也不单纯是一种技术应用。物联网将“互联网”和“物”连接在一起,就意味着把破坏性创新引进到当今的信息和通信技术世界。与互联网不同,物联网是物、网络、语义等视角的综合而形成的集网络、应用服务于一体的技术融合系统。在物联网语境中,物联网技术像人一样形成了人为的自主特征。物联网意味着一种潜在的技术异化的环境:个人隐私以多种方式受到威胁。而现有对隐私的制度规约存在诸多的不完备性。  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号