A framework for semantic grouping in P2P databases

Sharing of structured data in decentralized environments is a challenging problem, especially in the absence of a global schema. Social network structures map network links to semantic relations between participants in order to assist in efficient resource discovery and information exchange. In this work, we propose a scheme that automates the process of creating schema synopses from semantic clusters of peers which own autonomous relational databases. The resulting mediated schemas can be used as global interfaces for relevant queries. Active nodes are able to initiate the group schema creation process, which produces a mediated schema representative of nodes with similar semantics. Group schemas are then propagated in the overlay and used as a single interface for relevant queries. This increases both the quality and the quantity of the retrieved answers and allows for fast discovery of interest groups by joining peers. As our experimental evaluations show, this method increases both the quality and the quantity of the retrieved answers and allows for faster discovery of semantic groups by joining peers.     

DSACNN: Dynamically local self-attention CNN for 3D point cloud analysis

The point cloud is a common 3D representation widely applied in CAX engineering due to its simple data representation and rich semantic information. However, discrete and unordered 3D data structures make it difficult for point clouds to understand semantic information and make them unsuitable for applying standard operators. In this paper, to enhance machine perception of 3D semantic information, we propose a novel approach that can not only directly process point cloud data by a novel convolution-like operator but also dynamically pay attention to local semantic information. First, we design a novel dynamic local self-attention mechanism that can dynamically and flexibly focus on top-level information of the receptive field to learn and understand subtle features. Second, we propose a dynamic self-attention learning block, which adopts the proposed dynamic local self-attention learning convolution operation to directly deal with disordered and irregular point clouds to learn global and local point features while dynamically learning the important local semantic information. Third, the proposed operation can be compatibly applied as an independent component in popular architectures to improve the perception of local semantic information. Numerous experiments demonstrate the advantage of our method for point cloud tasks on datasets from both CAD data and scan data of complex real-world scenes.     

基于混合P2P网络模型的语义检索方法研究

在语义理解的基础上检索出满足用户需求的信息,是P2P走向更广泛应用的关键技术之一.提出了一种支持语义的混合P2P网络模型M-Chord,采用基于元数据规范模板(MST)的语义描述模型,结合Chord和语义覆盖网的技术特点,对基于MST的语义覆盖网动态生成方法进行了设计,提出了语义扩展路由的概念,并在上述研究的基础上提出了语义检索方法.通过实验分析表明,M-Chord具有较好的扩展性和语义检索性能.     

FMNN:融合多神经网络的文本分类模型

文本分类是自然语言处理中一项基本且重要的任务.基于深度学习的文本分类方法大多只针对单一的模型结构进行深入研究,这种单一的结构缺乏同时捕获并利用全局语义特征与局部语义特征的能力,且网络的加深会损失更多的语义信息.对此,提出了一种融合多神经网络的文本分类模型FMNN(A Text Classification Model ...     

Learning the semantics of structured data sources

Information sources such as relational databases, spreadsheets, XML, JSON, and Web APIs contain a tremendous amount of structured data that can be leveraged to build and augment knowledge graphs. However, they rarely provide a semantic model to describe their contents. Semantic models of data sources represent the implicit meaning of the data by specifying the concepts and the relationships within the data. Such models are the key ingredients to automatically publish the data into knowledge graphs. Manually modeling the semantics of data sources requires significant effort and expertise, and although desirable, building these models automatically is a challenging problem. Most of the related work focuses on semantic annotation of the data fields (source attributes). However, constructing a semantic model that explicitly describes the relationships between the attributes in addition to their semantic types is critical.We present a novel approach that exploits the knowledge from a domain ontology and the semantic models of previously modeled sources to automatically learn a rich semantic model for a new source. This model represents the semantics of the new source in terms of the concepts and relationships defined by the domain ontology. Given some sample data from the new source, we leverage the knowledge in the domain ontology and the known semantic models to construct a weighted graph that represents the space of plausible semantic models for the new source. Then, we compute the top k candidate semantic models and suggest to the user a ranked list of the semantic models for the new source. The approach takes into account user corrections to learn more accurate semantic models on future data sources. Our evaluation shows that our method generates expressive semantic models for data sources and services with minimal user input. These precise models make it possible to automatically integrate the data across sources and provide rich support for source discovery and service composition. They also make it possible to automatically publish semantic data into knowledge graphs.     

基于XML Schema的异构数据源集成技术研究

异构数据源集成过程中所面对的首要问题是异构问题,包括系统异构、模式异构和语义异构三个。以W3C的XML Schema标准作为异构数据源的全局模式,借助XML Schema强的数据描述能力,通过实现关系模式的提取、关系模式到XML Schema的转化和附加语义约束,实现了异构数据源的集成。     

Interschema correspondence establishment in a cooperative OWL-based multi-information server grid environment

Establishing interschema semantic knowledge between corresponding elements in a cooperating OWL-based multi-information server grid environment requires deep knowledge, not only about the structure of the data represented in each server, but also about the commonly occurring differences in the intended semantics of this data. The same information could be represented in various incompatible structures, and more importantly the same structure could be used to represent data with many diverse and incompatible semantics. In a grid environment interschema semantic knowledge can only be detected if both the structural and semantic properties of the schemas of the cooperating servers are made explicit and formally represented in a way that a computer system can process. Unfortunately, very often there is lack of such knowledge and the underlying grid information servers (ISs) schemas, being semantically weak as a consequence of the limited expressiveness of traditional data models, do not help the acquisition of this knowledge. The solution to overcome this limitation is primarily to upgrade the semantic level of the IS local schemas through a semantic enrichment process by augmenting the local schemas of grid ISs to semantically enriched schema models, then to use these models in detecting and representing correspondences between classes belonging to different schemas. In this paper, we investigate the possibility of using OWL-based domain ontologies both for building semantically rich schema models, and for expressing interschema knowledge and reasoning about it. We believe that the use of OWL/RDF in this setting has two important advantages. On the one hand, it enables a semantic approach for interschema knowledge specification, by concentrating on expressing conceptual and semantic correspondences between both the conceptual (intensional) definition and the set of instances (extension) of classes represented in different schemas. On the other hand, it is exactly this semantic nature of our approach that allows us to devise reasoning mechanisms for discovering and reusing interschema knowledge when the need arises to compare and combine it.     

结合拓扑势与TextRank算法的关键词提取方法

传统的TextRank算法进行关键词提取时词语之间的连接边采用权值均分的形式进行加权,未考虑词语的语义信息。针对这种情况,提出结合拓扑势与TextRank算法的关键词提取方法。方法使用词频和词语在文中的分布情况对词语加权作为词语的全局影响;使用拓扑势的思想结合词语全局影响计算词语间的转移概率作为词语的局部影响;将转移概率矩阵应用于传统TextRank算法中。实验表明,考虑词语全局及局部重要性等语义信息可有效提升TextRank算法的准确率和召回率。     

结合主动学习的多记录网页属性抽取方法*

属性抽取可分为对齐和语义标注两个过程,现有对齐方法中部分含有相同标签不同语义的属性会错分到同一个组,而且为了提高语义标注的精度,通常需要大量的人工标注训练集.为此,文中提出结合主动学习的多记录网页属性抽取方法.针对属性错分问题,引入属性的浅层语义,减少相同标签语义不一致的影响.在语义标注阶段,基于网页的文本、视觉和全局特征,采用基于主动学习的SVM分类方法获得带有语义的结构化数据.同时在主动学习的策略选择方面,通过引入样本整体信息,构建基于不确定性度量的策略,选择语义分类预测不准的样本进行标注.实验表明,在论坛、微博等多个数据集上,相比现有方法,文中方法抽取效果更好.     

基于语义模型的企业数据检索*

为了支持企业在决策时从企业数据中通过检索获得有意义的数据,提出了基于语义模型的语义检索方法。该方法首先基于概念树描述语义模型,通过概念映射将数据源与语义模型进行语义关联。在此基础上,建立语义模型和支持描述逻辑推理的知识模型之间的映射,通过调用描述逻辑推理机完成语义检索,检索结果再通过语义模型映射对应数据源信息,最终返回语义一致益于决策的数据视图。     

基于Markov网络及laplacian映射的快速相似性检索方法

快速相似性检索技术对于各种信息检索应用都具有很大的意义,其中基于语义哈希的快速相似性检索即是一个合理有效的检索方式,其检索模型能够在保证语义相关的基础上将高维空间中大量相关的文档数据,映射在低维空间中.虽然近年来许多关于语义哈希的研究都表现了不错的实验结果,但是都没有考虑到利用文档集合自身的信息来加强文档间的相关信息.为了有效利用文档自身信息,提出结合强化文档间邻接关系的马尔可夫迁移过程及使用保留局部信息的拉普拉斯映射方法的相似性检索方式.     

Finding association rules in semantic web data

The amount of ontologies and semantic annotations available on the Web is constantly growing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/(S) and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of query patterns. Initial experiments performed on semantic data of a biomedical application show the usefulness and efficiency of the approach.     

基于改进语义熵最大化模型的服务发现算法

语义匹配与发现是语义Web的核心内容之一。提出一种新的基于语义熵的服务发现与匹配算法。该算法通过引入语义熵的概念,把最大熵原理运用到语义识别与匹配领域,并对传统的熵最大模型进行了经验修正。通过实验对比分析,可以看出修正后的最大熵模型在服务发现计算方面具有较好的性能,该模型在一个真实的中文语义Web的语义识别项目中得到了应用,也体现出较好的精确度和性能。     

中文文本自动校对的语义级查错研究

目前中文文本自动校对的研究集中在词级和句法查错两方面,语义级查错仍是其中的薄弱环节。文章讨论了中文文本自动校对中的语义错误校对技术,综合使用了基于实例、基于统计和基于规则的搭配关系进行检查,提出统计和规则相结合的校对策略,既能检查局部语义限制,也能检查长距离的语义搭配,收到了较好的效果,也为中文自动校对的发展提供了新的思路。     

Relevance feedback for category search in music retrieval based on semantic concept learning

Traditional content-based music retrieval systems retrieve a specific music object which is similar to what a user has requested. However, the need exists for the development of category search for the retrieval of a specific category of music objects which share a common semantic concept. The concept of category search in content-based music retrieval is subjective and dynamic. Therefore, this paper investigates a relevance feedback mechanism for category search of polyphonic symbolic music based on semantic concept learning. For the consideration of both global and local properties of music objects, a segment-based music object modeling approach is presented. Furthermore, in order to discover the user semantic concept in terms of discriminative features of discriminative segments, a concept learning mechanism based on data mining techniques is proposed to find the discriminative characteristics between relevant and irrelevant objects. Moreover, three strategies, the Most-Positive, the Most-Informative, and the Hybrid, to return music objects concerning user relevance judgments are investigated. Finally, comparative experiments are conducted to evaluate the effectiveness of the proposed relevance feedback mechanism. Experimental results show that, for a database of 215 polyphonic music objects, 60% average precision can be achieved through the use of the proposed relevance feedback mechanism.

SemSearchPro – Using semantics throughout the search process

Semantic search attempts to go beyond the current state of the art in information access by addressing information needs on the semantic level, i.e. considering the meaning of users’ queries and the available resources. In recent years, there have been significant advances in developing and applying semantic technologies to the problem of semantic search. To collate these various approaches and to better understand what the concept of semantic search entails, we study semantic search under a general model. Extending this model, we introduce the notion of process-based semantic search, where semantics is exploited not only for query processing, but might be involved in all steps of the search process. We propose a particular approach that instantiates this process-based model. The usefulness of using semantics throughout the search process is finally assessed via a task-based evaluation performed in a real world scenario.     

基于局部颜色-空间特征的图像语义概念检测

针对基于语义的图像检索系统,提出了一种基于局部颜色-空间特征的图像语义概念检测方法。各种基于颜色、纹理和形状的全局特征都存在着众多信息冗余项和干扰项,而该文提出的局部颜色-空间特征则是利用语义概念层的先验知识进行特征降维后提取出的特征,它能更好地描述图像的语义内容,且具有容易提取、计算复杂度低的优点。实验结果表明,基于局部颜色-空间特征的概念检测方法优于基于全局特征的概念检测方法,将其用于图像检索后的检索精度比采用基于全局颜色特征的方法提高了36.4%。     

基于语义密度的名词消歧算法

提出了一种以概念相关性为主要依据的名词消歧算法。与现有算法不同的是,该算法在WordNet上对两个语义之间的语义距离进行了拓展,定义了一组语义之间的语义密度,从而量化了一组语义之间的相关性。将相关性转化为语义密度后,再进行消歧。还提出了一种在WordNet上的类似LSH的语义哈希,从而大大降低了语义密度的计算复杂度以及整个消歧算法的计算复杂度。在SemCor上对该算法进行了测试和评估。     

基于语义扩展的P2P网络

针对当前P2P系统中多数为媒体文件,而对应描述信息有限的问题,提出了一个通过Web信息挖掘来扩展语义的算法.同时提出了一个基于语义跳表的多层环网络结构,帮助用户进行相关内容推荐.实验表明,用本文所提出的方法,在消息量很小的情况下,与传统的基于中心服务器的检索精度很相近,具有实用价值.