一种基于用户兴趣的微博实体链接方法*

本文针对微博内容较短、歧义较大的问题,利用概率主题模型对用户的兴趣进行建模,提出了一种基于用户兴趣的微博实体链接方法。具体地,本文首先利用现有的主题模型从知识库的大量数据中训练实体与上下文词汇的语义关联,然后提出用户兴趣主题模型来建模用户对实体的兴趣以及微博的语义,并完成实体链接的任务。此外,本文在真实数据集上进行了大量实验和分析,取得了87.6%的实体链接准确率,实验结果表明,与现有方法相比,该方法通过用户兴趣的建模更好地刻画了微博的语义,因而也取得了更高的实体链接准确率。     

基于CNN和深层语义匹配的中文实体链接模型

实体链接是知识图谱领域的重要研究内容,现有的实体链接模型研究大多集中在对手工特征的选择上,不能很好地利用实体间的语义信息来实现更高效的实体链接效果。故提出一个基于深度语义匹配模型和卷积神经网络的实体链接模型,候选实体生成阶段采用构造同名字典,并基于上下文进行字典扩充,通过匹配来选择候选实体集。通过卷积神经网络来捕获深层语义信息,进行特征提取,并将其作为语义匹配模型的输入,通过模型训练学习选择出最佳参数,并输出语义相似度最高的候选实体作为实体链接的结果。在NLP & CC2014_ERL 数据集上较Ranking SVM模型准确率提升了3.9%,达到86.7%。实验结果表明了提出的新模型性能优于当前的主流模型。     

网络信息采集中链接与主题相关性的判定研究

面向主题的Web信息采集需判断提取的URL链接主题相关性。基于主题链接上下文提取,主题型语义块采用提取链接周围一定长度的文本,目录型和图片型语义块利用DOM树层次结构,对链接数据进行URL相关性判定;利用知网基于语义相似度的链接判定,给出一种综合内容和链接结构分析的URL主题相关性判定NPR算法,比较PageRank算法能提供更精确的主题页面。其成果对我国信息机构进行学科网络信息资源的深度建设有实用价值。     

基于信息网模型的Web实体语义信息搜索平台

Web上实体信息过于分散且缺乏语义,传统基于关键词匹配的搜索引擎往往因缺少上下文等语义信息,无法搜索到精确的结果。为了对Web数据进行精确查找,使用信息网模型(INM)对Web数据进行语义表示和建模,将实体的所有语义信息组织在一个对象中,快速获取实体完整的语义信息。基于INM构建复杂语义数据库,设计实现一个可对教育领域相关实体信息进行精确搜索的TLDW系统。实验结果表明,该系统初次查询时间均在100 ms内,其搜索结果包含实体的上下文关系等多种语义信息,缓存优化后的搜索结果可在20 ms内完成。     

图数据挖掘技术的研究与进展

生物信息学(蛋白质结构分析、基因组识别)、社会网络(实体间的联系)、Web分析(Web链接结构分析、Web内容挖掘和Web日志搜索)以及文本信息检索等的迅速发展积累了大量图数据,对于图数据的挖掘逐渐成为研究领域的热点。一些诸如聚类、分类、频繁模式挖掘的传统数据挖掘研究逐渐拓展到图数据领域。通过介绍现阶段图数据挖掘技术的研究进展,总结了图数据挖掘的特点、现实意义、主要问题以及应用场景,讨论并预测了图数据,尤其是不确定图数据研究的发展趋势和热点。     

结合多重嵌入表示的中文知识图谱补全

近年来，随着知识图谱相关技术的不断发展，各方面研究对知识图谱本身的需求也不断加强。然而现有的知识图谱无法完全覆盖整个真实世界，同时在知识正确性以及时效性等方面存在问题，这使得知识图谱补全越来越受到研究者的关注。在中文环境下，知识图谱补全任务又呈现出与英文图谱补全任务不同的特性。该文对中/英知识图谱补全任务进行了对比分析，将中文图谱中出现的错误进行了归类。根据该分析结果，该文提出将三元组中实体和关系嵌入表示、实体和关系描述文本嵌入表示结合的链接预测方法MER-Tuck,该方法利用外部的语义补充来加强矩阵分解模型的学习能力。为了验证该方法的有效性，该文为中文知识图谱补全任务构建了新数据集。在该数据集上将该文的方法与主流的链接预测方法进行比较，实验结果表明该文所提方法是有效的。     

民航领域突发事件的实体链接方法

实体链接的相关研究主要集中于医疗、生物和新闻领域,但在民航领域的研究较少。因此针对民航领域实体链接任务进行了研究,发现在实体链接中存在实体变体多、歧义少等问题。为解决上述问题,提出了一种基于语义推断的实体链接框架以及一种用于增强框架鲁棒性的负采样策略。在民航领域数据集上进行了对比实验,结果表明所提框架链接效果优于现有基准框架,并通过消融实验,验证了负采样策略的有效性。在负采样策略的作用下,该实体链接框架的Acc@top1高达0.875。     

融合多特征的中文集成实体链接方法

实体链接技术是将文本中的实体指称项正确链接到知识库中实体对象的过程，对知识库扩容起着关键作用。针对传统的实体链接方法主要利用上下文相似度等表层特征，而且忽略共现实体间的语义相关性，提出一种融合多特征的集成实体链接方法。首先结合同义词表、同名词表产生候选实体集，然后从多角度抽取语义特征，并将语义特征融合到构建的实体相关图中，最后对候选实体排序，选取top1实体作为链接目标。在NLP&CC2013中文微博实体链接评测数据集上进行实验，获得90.97%的准确率，与NLP&CC2013中文微博实体链接评测的最优系统相比，本文系统具有一定的优势。     

面向视频的细粒度多模态实体链接

随着互联网和大数据的飞速发展,数据规模越来越大,种类也越来越多.视频作为其中重要的一种信息方式,随着近期短视频的发展,占比越来越大.如何对这些大规模视频进行理解分析,成为学界关注的热点.实体链接作为一种背景知识补全方式,可以提供丰富的外部知识.视频上的实体链接可以有效地帮助理解视频内容,从而实现对视频内容的分类、检索、推荐等.但是现有的视频链接数据集和方法的粒度过粗,因此提出面向视频的细粒度实体链接,并立足于直播场景,构建了细粒度视频实体链接数据集.此外,依据细粒度视频链接任务的难点,提出利用大模型抽取视频中的实体及其属性,并利用对比学习得到视频和对应实体的更好表示.实验结果表明,该方法能够有效地处理视频上的细粒度实体链接任务.     

语义物联网中链接传感器数据发布系统

针对传感器网络数据的表示和传输标准不统一以及单一数据资源无法满足应用程序的需求问题,提出将传感器网络数据发布为链接传感器数据的方法。在分析已有链接传感器数据发布方法的基础上,首先利用本体标注技术为传感器网络数据添加语义信息,然后提出基于继承关系概念组集的关联数据查询处理方法以找到相关Web数据集(RWD)和基于启发式属性的图相似性比较方法实现传感器网络数据与相关Web数据的链接,最后构建了一种链接传感器数据发布系统(LSDPS)。通过与其他经典链接传感器数据发布系统比较,表明本系统将建立与相关数据的链接的准确率提高了9%。将传感器网络数据发布为链接传感器数据,使得应用程序不仅可以理解和利用传感器网络数据,而且可以根据链接传感器数据集间的资源描述框架(RDF)链接获得更多相关资源。     

Semantic Web search based on ontological conjunctive queries

Many experts predict that the next huge step forward in Web information technology will be achieved by adding semantics to Web data, and will possibly consist of (some form of) the Semantic Web. In this paper, we present a novel approach to Semantic Web search, called Serene, which allows for a semantic processing of Web search queries, and for evaluating complex Web search queries that involve reasoning over the Web. More specifically, we first add ontological structure and semantics to Web pages, which then allows for both attaching a meaning to Web search queries and Web pages, and for formulating and processing ontology-based complex Web search queries (i.e., conjunctive queries) that involve reasoning over the Web. Here, we assume the existence of an underlying ontology (in a lightweight ontology language) relative to which Web pages are annotated and Web search queries are formulated. Depending on whether we use a general or a specialized ontology, we thus obtain a general or a vertical Semantic Web search interface, respectively. That is, we are actually mapping the Web into an ontological knowledge base, which then allows for Semantic Web search relative to the underlying ontology. The latter is then realized by reduction to standard Web search on standard Web pages and logically completed ontological annotations. That is, standard Web search engines are used as the main inference motor for ontology-based Semantic Web search. We develop the formal model behind this approach and also provide an implementation in desktop search. Furthermore, we report on extensive experiments, including an implemented Semantic Web search on the Internet Movie Database.     

Towards semantically enhanced Web service repositories

The success of the Web services technology has brought topics as software reuse and discovery once again on the agenda of software engineers. While there are several efforts towards automating Web service discovery and composition, many developers still search for services via online Web service repositories and then combine them manually. However, from our analysis of these online repositories, it yields that, unlike traditional software libraries, they rely on little metadata to support service discovery. We believe that the major cause is the difficulty of automatically deriving metadata that would describe rapidly changing Web service collections. In this paper, we discuss the major shortcomings of state of the art Web service repositories and as a solution, we report on ongoing work and ideas on how to use techniques developed in the context of the Semantic Web (ontology learning, matching, metadata based presentation) to improve the current situation.     

Semantic Web Services, Part 2

In part 2 of this Trends & Controversies installment, we continue exploring the state of the art, current practices, and future directions for Semantic Web services. SWS aims to bring Semantic Web technology - for representing, sharing, and reasoning about knowledge - to bear in Web service contexts. The objective is to enable a fuller, more flexible automation of service provision and use and the construction of more powerful tools and methodologies for working with services.     

语义网站点的发现与排序

随着语义网中RDF数据的大量涌现,语义搜索引擎为用户搜索RDF数据带来了便利.但是,如何自动地发现包含语义网信息资源的站点,并高效地在语义网站点中收集语义网信息资源,一直是语义搜索引擎所面临的问题.首先介绍了语义网站点的链接模型.该模型刻画了语义网站点、语义网信息资源、RDF模型和语义网实体之间的关系.基于该模型讨论了语义网实体的归属问题,并进一步定义了语义网站点的发现规则;另外,从站点链接模型出发,定义了语义网站点依赖图,并给出了对语义网站点进行排序的算法.将相关算法在一个真实的语义搜索引擎中进行了初步测试.实验结果表明,所提出的方法可以有效地发现语义网站点并对站点进行排序.     

语义网环境下的知识集成系统设计

语义网近年来发展迅速,为网络环境下实现知识集成提供理论支撑和技术路线,可以为知识数据建立关联并增强系统的可扩展性。在资源描述框架下构建知识本体,采用Java语言和Jena API对知识集成系统进行设计和开发,构建知识集成框架实现知识内容显示和关联查询。系统设计突出了语义网的功能和特色,应用多种语义网体系技术和工具,提高知识集成系统的数据分析能力和用户体验效果。     

Relevance feedback between hypertext and Semantic Web search: Frameworks and evaluation

We investigate the possibility of using Semantic Web data to improve hypertext Web search. In particular, we use relevance feedback to create a ‘virtuous cycle’ between data gathered from the Semantic Web of Linked Data and web-pages gathered from the hypertext Web. Previous approaches have generally considered the searching over the Semantic Web and hypertext Web to be entirely disparate, indexing, and searching over different domains. While relevance feedback has traditionally improved information retrieval performance, relevance feedback is normally used to improve rankings over a single data-set. Our novel approach is to use relevance feedback from hypertext Web results to improve Semantic Web search, and results from the Semantic Web to improve the retrieval of hypertext Web data. In both cases, an evaluation is performed based on certain kinds of informational queries (abstract concepts, people, and places) selected from a real-life query log and checked by human judges. We evaluate our work over a wide range of algorithms and options, and show it improves baseline performance on these queries for deployed systems as well, such as the Semantic Web Search engine FALCON-S and Yahoo! Web search. We further show that the use of Semantic Web inference seems to hurt performance, while the pseudo-relevance feedback increases performance in both cases, although not as much as actual relevance feedback. Lastly, our evaluation is the first rigorous ‘Cranfield’ evaluation of Semantic Web search.     

Ontology-based semantic search on the Web and its combination with the power of inductive reasoning

Semantic Web search is currently one of the hottest research topics in both Web search and the Semantic Web. In previous work, we have presented a novel approach to Semantic Web search, which allows for evaluating ontology-based complex queries that involve reasoning over the Web relative to an underlying background ontology. We have developed the formal model behind this approach, and provided a technique for processing Semantic Web search queries, which consists of an offline ontological inference step and an online reduction to standard Web search. In this paper, we continue this line of research. We further enhance the above approach by the use of inductive rather than deductive reasoning in the offline inference step. This increases the robustness of Semantic Web search, as it adds the important ability to handle inconsistencies, noise, and incompleteness, which are all very likely to occur in distributed and heterogeneous environments such as the Web. The inductive variant also allows to infer new (not logically deducible) knowledge (from training individuals). We report on a prototype implementation of (both the deductive and) the inductive variant of our approach in desktop search, and we provide extensive new experimental results, especially on the running time and the precision and the recall of our new?approach.     

基于语义Web上知识表示的研究及其应用

随着对语义研究的深入,人们越来越关注在W EB上信息内容的表示问题,通过分析语义W EB上知识表示的特点后指出,以RDF为基础的知识表示语言可较好地实现语义W EB的知识表示。最后通过语义W EB上的语义检索应用给予了说明。