基于语义的Web挖掘

基于语义的Web挖掘是使用从现有Web数据中抽取的语义或直接使用Web数据中已有的语义结构来帮助Web挖掘。它有效地结合了语义网和Web挖掘两个领域的研究成果,既可以通过开发新的语义结构来帮助Web挖掘,又可以利用挖掘结果促进语义网的创建。本文介绍了基于语义的Web挖掘的基本思想和研究现状,分析了语义网和Web挖掘相结合的优势,并详细论述了国际上关于利用数据挖掘技术创建语义网,利用语义挖掘Web数据和直接挖掘语义网三个方面的研究工作。     

Ontologies for ecoinformatics

Rapid advances in information technologies continue to drive a flood of data and analysis techniques in ecological and environmental sciences. Using these resources more effectively and taking advantage of associated cross-disciplinary research opportunities poses a major challenge to both scientists and information technologists. These challenges are now being addressed in projects that apply knowledge representation and Semantic Web technologies to problems in discovering and integrating ecological data and data analysis techniques. In this paper, we present an overview of the major ontological components of our project, SEEK (“Science Environment for Ecological Knowledge”). We describe the concepts and models that are represented in each, and present a discussion of potential applications of these ontologies on the Semantic Web.     

语义Web及其应用

语言文Web是下一代Interuet的发展方向。语义Web的定义、分层结构进行了概述，详细总结和研究了语义Web在Web服务、P2P网络、知识管理、E-learning、智能信息检索和语义Web挖掘、网格计算等多个领域的应用。     

Semantic Web computing in industry

The Semantic Web has attracted significant attention during the last decade. On the one hand, many research groups have changed their focus towards Semantic Web research and research funding agencies particularly in Europe have explicitly mentioned Semantic Web in their calls for proposals. On the other hand, industry has also begun to watch developments with interest and a number of large companies have started to experiment with Semantic Web technologies to ascertain if these new technologies can be leveraged to add more value for their customers or internally within the company, while there are already several offers of vendors of Semantic Web solutions on the market. The essence of the Semantic Web is to structure Web-based information to make it more interoperable, machine-readable and thereafter to provide a means to relate various information concepts more easily and in a reusable way. The Semantic Web acts as an additional layer on the top of the Web, and is built around explicit representations of information concepts and their relationships such as ontologies and taxonomies. Furthermore, Semantic Web technologies are not only valuable on an open environment like the Web, but also in closed systems such as in industrial settings. Hence, these technologies can be efficiently deployed for domains including Web Services, Enterprise Application Integration, Knowledge Management and E-Commerce, fulfilling existing gaps in current applications. This paper focuses on this synthesis between Semantic Web technologies and systems problems within industrial applications. There will be a short review of Semantic Web standards, languages and technologies followed by a more detailed review of applications of Semantic Web computing in industry. The paper covers theoretical considerations as well as use cases and experience reports on the topic, and we also present some current challenges and opportunities in the domain.     

Explaining answers from the Semantic Web: the Inference Web approach

The Semantic Web lacks support for explaining answers from web applications. When applications return answers, many users do not know what information sources were used, when they were updated, how reliable the source was, or what information was looked up versus derived. Many users also do not know how implicit answers were derived. The Inference Web (IW) aims to take opaque query answers and make the answers more transparent by providing infrastructure for presenting and managing explanations. The explanations include information concerning where answers came from (knowledge provenance) and how they were derived (or retrieved). In this article we describe an infrastructure for IW explanations. The infrastructure includes: IWBase — an extensible web-based registry containing details about information sources, reasoners, languages, and rewrite rules; PML — the Proof Markup Language specification and API used for encoding portable proofs; IW browser — a tool supporting navigation and presentations of proofs and their explanations; and a new explanation dialogue component. Source information in the IWBase is used to convey knowledge provenance. Representation and reasoning language axioms and rewrite rules in the IWBase are used to support proofs, proof combination, and Semantic Web agent interoperability. The Inference Web is in use by four Semantic Web agents, three of them using embedded reasoning engines fully registered in the IW. Inference Web also provides explanation infrastructure for a number of DARPA and ARDA projects.     

Extracting significant Website Key Objects: A Semantic Web mining approach

Web mining has been traditionally used in different application domains in order to enhance the content that Web users are accessing. Likewise, Website administrators are interested in finding new approaches to improve their Website content according to their users' preferences. Furthermore, the Semantic Web has been considered as an alternative to represent Web content in a way which can be used by intelligent techniques to provide the organization, meaning, and definition of Web content. In this work, we define the Website Key Object Extraction problem, whose solution is based on a Semantic Web mining approach to extract from a given Website core ontology, new relations between objects according to their Web user interests. This methodology was applied to a real Website, whose results showed that the automatic extraction of Key Objects is highly competitive against traditional surveys applied to Web users.     

Web挖掘研究

因特网目前是一个巨大,分布广泛,全球性的信息服务中心,它涉及新闻,广告,消费信息,金融管理,教育,政府,电子商务和许多其它信息服务,Web包含了丰富和动态的超链接信息,以及Web页面的访问和使用信息,这为数据挖掘提供了丰富的资源,Web挖掘就是从Web活动中抽取感兴趣的潜在有用模式和隐藏的信息,对Web挖掘最新技术及发展方向做了全面分析,包括Web结构挖掘,多层次Ｗeb数据仓库方法以及Ｗ eb,Log挖掘等。     

基于Web的数据挖掘研究综述

基于Web数据挖掘是一个结合了数据挖掘和WWW的热门研究主题。文章介绍了Web数据挖掘最流行的分类；Web内容挖掘，Web结构挖掘和Web使用记录挖掘，根据Web数据挖掘的最近研究状况，总结了几个研究热点，并介绍了一个Web使用记录挖掘的框架WebSIFT.     

基于Web代理的空间数据挖掘框架及实现研究

论文在分析数据挖掘、空间数据挖掘、web代理的概念和技术特点的基础上,进一步给出了其基本框架,并利用相应工具对其进行了实现验证。     

Towards the Web of Embeddings: Integrating multiple knowledge graph embedding spaces with FedCoder

The Semantic Web is distributed yet interoperable: Distributed since resources are created and published by a variety of producers, tailored to their specific needs and knowledge; Interoperable as entities are linked across resources, allowing to use resources from different providers in concord. Complementary to the explicit usage of Semantic Web resources, embedding methods made them applicable to machine learning tasks. Subsequently, embedding models for numerous tasks and structures have been developed, and embedding spaces for various resources have been published. The ecosystem of embedding spaces is distributed but not interoperable: Entity embeddings are not readily comparable across different spaces. To parallel the Web of Data with a Web of Embeddings, we must thus integrate available embedding spaces into a uniform space.Current integration approaches are limited to two spaces and presume that both of them were embedded with the same method — both assumptions are unlikely to hold in the context of a Web of Embeddings. In this paper, we present FedCoder— an approach that integrates multiple embedding spaces via a latent space. We assert that linked entities have a similar representation in the latent space so that entities become comparable across embedding spaces. FedCoder employs an autoencoder to learn this latent space from linked as well as non-linked entities.Our experiments show that FedCoder substantially outperforms state-of-the-art approaches when faced with different embedding models, that it scales better than previous methods in the number of embedding spaces, and that it improves with more graphs being integrated whilst performing comparably with current approaches that assumed joint learning of the embeddings and were, usually, limited to two sources. Our results demonstrate that FedCoder is well adapted to integrate the distributed, diverse, and large ecosystem of embeddings spaces into an interoperable Web of Embeddings.     

ON KNOWLEDGE GRID AND GRID INTELLIGENCE: A SURVEY

The next generation Web Intelligence (WI) aims at enabling users to go beyond the existing online information search and knowledge queries functionalities and to gain, from the Web, practical wisdom for problem solving. To support such a Wisdom Web, we envision that a grid-like computing infrastructure with intelligent service agencies is needed, where these agencies can interact, self-organize, learn, and evolve their course of actions, identities, and interrelationships for new knowledge creation, as well as scientific and social evolution. In this paper, we first provide an overview of recent development in WI and Semantic/Knowledge Grid. Then, the fundamental capabilities of the Wisdom Web as well as the conceptual architecture of an intelligent Grid for supporting it are described. Technical challenges for realizing Grid Intelligence are highlighted and the recent advancements in related research areas are reviewed.     

语义Web链接结构分析之综述

随着语义Web研究的发展,其数据量也不断增长,要实现语义Web追求的目标——数据的共享和重用,语义Web上的实体搜索和文档搜索必不可少。而面对这样不断增长的数据以及不同于传统Web的搜索要求,就需要使用链接结构分析来指导语义Web上的搜索。同时,语义Web的发展现状也无时无刻不吸引着研究人员的关注,而链接结构分析对于揭示其宏观结构起着关键作用。分别从实体和文档两个粒度对面向语义Web链接结构分析的研究进行总结,特别关注链接模型的构建以及链接结构分析方法的应用。     

语义Web的实现：概念标记与概念系统

语义Web是对Web的扩展.Web是被格式标记的信息的集合,语义Web则是被概念标记的信息的集合,扩展的两项措施为信息采用概念标记和计算机内置概念系统.语义由直接语义和引中语义构成,前者为人脑中的观念,后者与语境有关.鏊于目前计算机的能力尚不能模拟语境,不得不"搁置引申语义"和"以概念近似观念",于是就有"语义=概念".概念标记与概念系统是实现语义Web的两大支撑.概念标记就是用概念标记符对将要交由计算机处理的信息进行标记,其面临的主要难点有信息的切分、概念标记符的选用和标记过程的自动化.概念系统就是Ontology,其主要功用是读出信息的直接语义并进行概念推理,后者才是语义Web的最突出特征.概念系统的研究目前主要针对规模庞大、持续性维护、分布式及其集成应用等问题.     

网络的Web应用开发技术研究

互联网络应用的普及使得数据挖掘技术的重点已经从传统的基于数据库的应用转移到了基于Web的应用。Web数据挖掘旨在改进网络系统性能,提高运行效率,具有良好的发展和应用前景,必将得到越来越多的关注。从Web应用开发技术入手,就Web挖掘技术的概念、步骤及Web应用开发的过程和测试技术作了较为详细的阐述。     

利用语义进行Web结构挖掘

本文论述了利用语义挖掘Web结构、Web使用挖掘进行了基于语义挖掘方法的探讨,并对PageRank算法进行了分析,针对该算法的不足之处进行了改进。     

利用语义进行Web结构挖掘

本文论述了利用语义挖掘Web结构、Web使用挖掘进行了基于语义挖掘方法的探讨，并对PageRank算法进行了分析，针对该算法的不足之处进行了改进。     

语义网在文本分类中的应用

随着因特网上信息的大量增加,如果不依靠自动分类而完全通过手工进行文本分类,文本分类是不可能完成的。因此,文本自动分类成为一个重要的研究领域。首先介绍语义网及其相关技术,最后介绍基于本体技术的语义网的自动分类器。     

Web使用挖掘系统研制中的主要问题和应对策略

With the rapid development of WWW,Web Usage Mining,as well as Web Mining,has become a hot direction in academic and industrial circles.It is generally believed that there are three tasks,preprocessing,knowledge discovery and pattern analysis,in Web Usage Mining.Though Web Usage Mining is still ranged in the application of traditional data mining techniques,in view of changes in application environment and operated data concerned,some new difficulties have arisen accordingly.This paper takes efforts to address such challenges in the three phases and introduces some proposed solutions simultaneously.