基于树状结构的语义相似度计算方法分析

文中提出了基于树状结构的语义相似度计算方法.结合概念节点之间的语义关系、语义距离、概念节点的深度、密度对语义相似度的影响,利用树的层次关系来表达概念节点之间的语义信息,并对概念节点密度的计算进行改进,加入了可调节的参数,以适应不同的情景.通过实验验证了该算法在查准率方面具有较强的优越性.     

一种基于WordNet的短文本语义相似性算法

 短文本语义相似性计算在文献检索、信息抽取、文本挖掘等方面应用日益广泛.本文提出了一种短文本语义相似性计算算法ST-CW.此算法使用WordNet和Brown文集来计算文本中的概念相似性,在此基础上提出了一个新的方法综合考虑概念、句法等信息来计算短文本的语义相似性.在R&;B及Miller数据集上进行实验,实验结果验证了算法的有效性.     

一种新的复合核函数及在问句检索中的应用

问句检索在问答系统中有着重要的作用,其核心问题在于研究查询问句与候选问句之间的相似性计算问题,实现问句之间的高精度匹配。该文采用树核函数的方法计算问句之间的结构相似性,并针对原有算法的不足,做了相应的改进。为降低句法解析器性能对树核函数的影响,该文在改进的树核函数基础上,将其与字符串核结合,提出了一种能同时融合问句的句法信息,词性信息和词序信息的复合核函数,用以计算问句之间的综合语义相似性。在社区问答系统Yahoo!Answer的数据上进行测试,相对传统的基于词频的特征向量法,问句检索平均准确率提高了24.02%。     

Demand-aware mobile bike-sharing service using collaborative computing and information fusion in 5G IoT environment

Mobile bike-sharing services have been prevalently used in many cities as an important urban commuting service and a promising way to build smart cities, especially in the new era of 5G and Internet-of-Things (IoT) environments. A mobile bike-sharing service makes commuting convenient for people and imparts new vitality to urban transportation systems. In the real world, the problems of no docks or no bikes at bike-sharing stations often arise because of several inevitable reasons such as the uncertainty of bike usage. In addition to pure manual rebalancing, in several works, attempts were made to predict the demand for bikes. In this paper, we devised a bike-sharing service with highly accurate demand prediction using collaborative computing and information fusion. We combined the information of bike demands at different time periods and the locations between stations and proposed a dynamical clustering algorithm for station clustering. We carefully analyzed and discovered the group of features that impact the demand of bikes, from historical bike-sharing records and 5G IoT environment data. We combined the discovered information and proposed an XGBoost-based regression model to predict the rental and return demand. We performed sufficient experiments on two real-world datasets. The results confirm that compared to some existing methods, our method produces superior prediction results and performance and improves the availability of bike-sharing service in 5G IoT environments.     

基于语义路径的异质网络社区发现方法

社区发现是社会网络研究的热点问题,综合利用社会网络中不同对象间的异质信息,可以更加有效地挖掘网络中的社区结构。针对传统的社区发现方法无法有效地利用异质信息的问题,本文提出了一种基于语义路径的异质网络社区发现方法,该方法首先定义网络中的语义路径,通过语义路径来衡量不同类型对象间的异质信息相似度,然后以此构造可靠性矩阵,作为半监督非负矩阵分解的正则化约束项,进而实现异质网络的社区划分。在真实数据集上的实验结果表明,所提出的方法能够更准确地发现异质网络中的社区结构。     

An improved hybrid semantic matching algorithm with lexical similarity

In this paper, we proposed an improved hybrid semantic matching algorithm combining Input/Output (I/O) semantic matching with text lexical similarity to overcome the disadvantage that the existing semantic matching algorithms were unable to distinguish those services with the same I/O by only performing I/O based service signature matching in semantic web service discovery techniques. The improved algorithm consists of two steps, the first is logic based I/O concept ontology matching, through which the candidate service set is obtained and the second is the service name matching with lexical similarity against the candidate service set, through which the final precise matching result is concluded. Using Ontology Web Language for Services (OWL-S) test collection, we tested our hybrid algorithm and compared it with OWL-S Matchmaker-X (OWLS-MX), the experimental results have shown that the proposed algorithm could pick out the most suitable advertised service corresponding to user’s request from very similar ones and provide better matching precision and efficiency than OWLS-MX.     

ESOT: a new privacy model for preserving location privacy in Internet of Things

The Internet of Things (IoT) means connecting everything with every other thing through the Internet. In IoT, millions of devices communicate to exchange data and information with each other. During communication, security and privacy issues arise which need to be addressed. To protect information about users’ location, an efficient technique should be devised. Several techniques have already been proposed for preserving location privacy in IoT. However, the existing research lags in preserving location privacy in IoT and has highlighted several issues such as being specific or being restricted to a certain location. In this paper, we propose a new location privacy technique called the enhanced semantic obfuscation technique (ESOT) to preserve the location information of a user. Experimental results show that ESOT achieves improved location privacy and service utility when compared with a well-known existing approach, the semantic obfuscation technique.     

基于DBSACN聚类算法的XML文档聚类

为了满足对XML文档集合进行数据挖掘需求,本文提出了根据XML文档树的语义信息和结构信息来计算其结构相似度,通过结构相似度构造其结构相似度矩阵,在此基础上应用DBSCAN算法来对XML文档集合进行聚类.与其他聚类算法相比,其聚类的速度得到了很大的提高.     

Flexible user-centric service selection algorithm for Internet of Things services

In Internet of Things (IoT), the similar functional services are evolving in different quality of services (QoS) due to the widespread deployment of spatially distributed things on dynamic networks through the web. Therefore, the user involvement in service selection systems becomes a vital role to enhance the system performance by taking into account of subjective factors. This paper proposes a flexible QoS-based service selection algorithm (FQSA) which mainly targets to the users to be able to give their subjective preferences in an easy and friendly manner. The FQSA algorithm selects the services based on subjective information provided by the service consumers and objective information supported by the service providers. It adopts artificial neural network backpropagation algorithm to find the objective factors and applies similarity aggregation method to evaluate the creditability of the user subjective factors which have already been evaluated by ontological reasoning with the help of proposed QoS ontology. The comparative study and experimental results show that our FQSA algorithm is superior to other service selection approaches.     

基于OWL-S的服务发现语义匹配机制

 本文提出一种基于OWL-S的语义web服务匹配系统的机制,将语义元素引入UDDI系统中,从而在UDDI中保存语义信息.系统通过语义服务匹配算法提高web服务匹配的准确度和召回率,并通过数据映射机制保证模型对于当前的服务发现标准UDDI基础架构的兼容以及UDDI标准操作接口的透明性.系统使用本体概念的索引机制提高服务发现的效率.而且,系统在建立和维护索引的过程、或服务的匹配过程中使用近似概念搜寻算法进一步提高本体概念搜寻和服务匹配的效率.     

Information mining and similarity computation for semi- / un-structured sentences from the social data

In recent years, with the development of the social Internet of Things (IoT), all kinds of data accumulated on the network. These data, which contain a lot of social information and opinions. However, these data are rarely fully analyzed, which is a major obstacle to the intelligent development of the social IoT. In this paper, we propose a sentence similarity analysis model to analyze the similarity in people’s opinions on hot topics in social media and news pages. Most of these data are unstructured or semi-structured sentences, so the accuracy of sentence similarity analysis largely determines the model’s performance. For the purpose of improving accuracy, we propose a novel method of sentence similarity computation to extract the syntactic and semantic information of the semi-structured and unstructured sentences. We mainly consider the subjects, predicates and objects of sentence pairs and use Stanford Parser to classify the dependency relation triples to calculate the syntactic and semantic similarity between two sentences. Finally, we verify the performance of the model with the Microsoft Research Paraphrase Corpus (MRPC), which consists of 4076 pairs of training sentences and 1725 pairs of test sentences, and most of the data came from the news of social data. Extensive simulations demonstrate that our method outperforms other state-of-the-art methods regarding the correlation coefficient and the mean deviation.     

基于商密SM9算法的物联网安全平台设计与应用

物联网普遍存在遭受网络攻击类型多样化、没有安全保护标准、数据极易被截获或破解等安全风险,核心问题在于缺乏设备、服务提供者、应用、数据、交易等物的安全认证机制。因此,有必要建立一种提供认证鉴权和数据保护的方案体系,建立物与物、物与人之间的信任。密码技术是解决核心安全问题的基础理论和技术,而传统的证书体系并不适应于物联网环境,基于商密SM9的算法才是目前物联网安全认证的最佳选择。物联网安全平台依赖商密SM9算法的优势,有效克服了传统算法中密钥分发安全性弱等问题,深入物联网行业终端与应用层面,建立了面向物联网业务的端到端安全。     

An information Content-Based Approach for Measuring Concept Semantic Similarity in WordNet

Computing information content (IC) of a concept is a core issue for semantic similarity measures of IC-based. So far, little works focused on calculating the IC of multiple inheritance nodes. So in this paper, a new IC computing model is proposed to calculate the IC of node (including single inheritance node and multiple inheritances node) in WordNet. This model calculates the IC of the concept through the parameters hypernyms, hyponyms, relative depth, maximum nodes and siblings. Experimental results of “poison” snippet in WordNet taxonomy shows that this model can effectively deal with the cases of single inheritance and multiple inheritance. Meanwhile, the results indicate this model is sensitive to distinguish the IC value of nodes while one of relative depth, hyponym, hypernym or sibling is different. Based on proposed model, a taxonomical semantic similarity measure is proposed to compute the semantic similarity of multiple inheritance nodes. Finally, this paper compares proposed approach with other similarity measures based IC in the given fragment of WordNet classification tree, and the results show that the proposed similarity approach acquires good performance.     

物联网海量异构数据存储与共享策略研究

随着物联网向各行业的深入发展,各行业的信息化进程也进入了快车道.信息服务作为物联网在各行业应用中重要的公共服务之一,一直受到广泛关注.然而,当前物联网信息服务系统面对物联网海量异构数据存在性能低下、共享困难等问题.因此,本文提出了一种基于NoSQL、REST以及国家物联网标识管理公共服务平台(NIOT)的存储与共享策略,并着重对该系统的构成、逻辑设计进行了详尽阐述.针对性能改进的策略设计了适当的量化评测,实验结果表明提出策略具有较好的效果,基于实验结果对进一步的优化进行了讨论.     

科技项目申请书关键词提取方法

关键词提取在文本相似度计算得到应用。传统的关键词提取方法忽略文本中的未登录词以及缺乏对词语语义的理解。针对科技项目申请书,研究提出一种基于未登录词识别与语义的关键词提取方法。应用Lucene和统计相融合的方法进行分词,并识别未登录词作为申请书关键词的一部分;依据社会网络理论构建词语语义相似度网络,并计算词语关联度提取申请书其他关键词。实验结果表明,与传统的关键词提取方法相比,新方法能提取更准确的关键词,有更好的科技项目相似性检查效果。     

Interoperability in semantic Web of Things: Design issues and solutions

The significant improvement in processing power, communication, energy consumption, and the size of computational devices has led to the emergence of the Internet of Things (IoT). IoT projects raise many challenges, such as the interoperability between IoT applications because of the high number of sensors, actuators, services, protocols, and data associated with these systems. Semantics solves this problem by using annotations that define the role of each IoT element and reduces the ambiguity of information exchanged between the devices. This work presents SWoTPAD, a semantic framework that helps in the development of IoT projects. The framework is designer oriented and provides a semantic language that is more user‐friendly than OWL‐S and WSML and allows the IoT designer to specify devices, services, environment, and requests. Following this, it makes use of these specifications and maps them for RESTful services. Additionally, it generates an automatic service composition engine that is able to combine services needed to handle complex user requests. We validated this approach with two case studies. The former concerns a residential security system and the latter, the cloud application deployment. The average time required for service discovery and automatic service composition corresponds to 72.9% of the service execution time in the case study 1 and 64.4% in the case study 2.     

物联网中基于相似性计算的传感器搜索

物联网逐渐成为学术界研究的热点领域,无处不在的传感器设备促进了传感器搜索服务的产生。物联网中搜索的强时空性、海量数据的异构性与传感器节点的资源受限性,给物联网搜索引擎高效地查询传感器提出了挑战。该文提出基于传感器定量数值的线性分段拟合相似性(PLSS)搜索算法。PLSS算法通过分段和线性拟合的方法,构建传感器定量数值的相似性计算模型,从而计算传感器的相似度,根据相似度查找最相似的传感器集群。与模糊集(FUZZY)算法和最小二乘法相比,PLSS算法平均查询精度和查询效率较高。与原数据相比,PLSS算法的存储开销至少降低了两个数量级。     

基于敏感属性熵的微聚集算法

在聚类过程中,不合适的距离度量会导致匿名过程中不必要的信息损失,因此对于不同类型的属性定义一个适当的距离度量一直是个难以解决的问题.本文提出语义属性的概念,并提出编码层次树来表示语义属性,有效地降低了匿名过程中的信息损失.在p-敏感k-匿名模型中,敏感属性值在聚类结果中分布不均匀会导致敏感信息泄露,因此本文提出一种基于敏感属性熵的微聚集算法,并提出匿名保护指数来描述隐私保护程度,在聚类过程中通过保证匿名保护指数最大,来提高敏感属性在聚类结果中分布的均匀程度,以应对背景知识攻击,降低隐私泄漏的风险.最后,通过实验验证了算法的合理性和有效性.     

融合TextRank算法的中文短文本相似度计算

传统的VSM向量空间模型忽略了文本语义,构建的文本特征矩阵具有稀疏性。基于深度学习词向量技术,文中提出一种融合改进TextRank算法的相似度计算方法。该方法利用词向量嵌入的技术来构建文本向量空间,使得构建的向量空间模型具有了语义相关性,同时采用改进的TextRank算法提取文本关键字,增强了文本特征的表达并消除了大量冗余信息,降低了文本特征矩阵的稀疏性,使文本相似度的计算更加高效。不同模型的仿真实验结果表明,融合改进的TextRank算法与Bert词向量技术的方法具有更好的文本相似度计算性能。