Mapping Relational Databases to the Semantic Web with Original Meaning

Most of nowadays Web content is stored in relational databases. It is important to develop ways for representation of this information in the Semantic Web to allow software agents to process it intelligently. The paper presents an approach to translation of data, schema and the most important constraints from relational databases into the Semantic Web without any extensions to the Semantic Web languages.     

KAoS policy management for semantic Web services

Web Services power through explicit representations of Web resources underlying semantics and the development of an intelligent Web infrastructure that can fully exploit them. Semantic Web languages, such as OWL, extend RDF to let users specify ontologies comprising taxonomies of classes and inference rules. Both people and software agents can effectively use Semantic Web Services.' Agents will increasingly use the combination of semantic markup languages and Semantic Web Services to understand and autonomously manipulate Web content in significant ways. Agents will discover, communicate, and cooperate with other agents and services and-as we' 11 describe -will rely on policy-based management and control mechanisms to ensure respect for human-imposed constraints on agent interaction. Policy-based controls of Semantic Web Services can also help govern interaction with traditional (nonagent) clients.     

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.     

一种基于Agent的语义Web服务编制框架

针对动态、开放网络环境下复杂多变的业务需求,本文在前期语义编程语言SPL研究的基础上,提出一种基于Agent和本体的语义Web服务编制方法,形成一个以语义编程语言SPL为核心的、可适用于将语义Web服务与多A-gent技术无缝集成起来的,进行语义Web服务编制的技术框架。框架实现基于SPL开发和运行支撑平台,该平台为语义Web服务和软件Agent提供必要的运行支持。     

Comparing keyword search to semantic search: a case study in solving crossword puzzles using the Google™ API

Keyword‐based search engines such as Google? index Web pages for human consumption. Sophisticated as such engines have become, surveys indicate almost 25% of Web searchers are unable to find useful results in the first set of URLs returned (Technology Review, March 2004). The lack of machine‐interpretable information on the Web limits software agents from matching human searches to desirable results. Tim Berners‐Lee, inventor of the Web, has architected the Semantic Web in which machine‐interpretable information provides an automated means to traversing the Web. A necessary cornerstone application is the search engine capable of bringing the Semantic Web together into a searchable landscape. We implemented a Semantic Web Search Engine (SWSE) that performs semantic search, providing predictable and accurate results to queries. To compare keyword search to semantic search, we constructed the Google CruciVerbalist (GCV), which solves crossword puzzles by reformulating clues into Google queries processed via the Google API. Candidate answers are extracted from query results. Integrating GCV with SWSE, we quantitatively show how semantic search improves upon keyword search. Mimicking the human brain's ability to create and traverse relationships between facts, our techniques enable Web applications to ‘think’ using semantic reasoning, opening the door to intelligent search applications that utilize the Semantic Web. Copyright © 2007 John Wiley & Sons, Ltd.     

Semantic Web Mining: State of the art and future directions

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: More and more researchers are working on improving the results of Web Mining by exploiting semantic structures in the Web, and they make use of Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself.The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports the user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of these resources. Therefore, automated schemes for learning the relevant information are increasingly being used. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web sites and navigation behavior are becoming more and more common. Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.     

基于元数据的语义搜索技术研究

描述了为现有的Web资源加入元数据语义描述信息,从而可提高基于语义的搜索引擎的查准率;提出一种搜索引擎和外界智能设备或终端交互的接口形式;最后展望语义Web和语义搜索引擎相关技术进一步发展的方向。     

Semantic Web based innovative design knowledge modeling for collaborative design

This paper describes a study about how to use the Semantic Web technologies for innovative design knowledge modeling in a multi-agent distributed design environment. Semantic Web based knowledge modeling for innovative design is proposed as prelude to the meaningful agent communication and knowledge reuse for collaborative work among multidisciplinary organizations. A model for innovative design is proposed at first, based on which a knowledge schema is brought forward. For sharing the design knowledge among an internet-based or distributed work team, even globally, A RDF-based knowledge model is presented to realize its representation on Semantic Web. A Semantic Web based repository for innovative design and its API for topper Semantic Web applications have been also constructed. The proposed knowledge modeling extends traditional product modeling with capabilities of innovative design, knowledge sharing and distributed problem solving, and is employed as a content language within the messages in the proposed multi-agent system architecture. The proposed approach is viewed as a promising knowledge management method that facilitates the implementation of computer supported cooperative work in innovative design of Semantic Web applications.     

Piggy Bank: Experience the Semantic Web inside your web browser

The Semantic Web Initiative envisions a Web wherein information is offered free of presentation, allowing more effective exchange and mixing across web sites and across web pages. But without substantial Semantic Web content, few tools will be written to consume it; without many such tools, there is little appeal to publish Semantic Web content.To break this chicken-and-egg problem, thus enabling more flexible information access, we have created a web browser extension called Piggy Bank that lets users make use of Semantic Web content within Web content as users browse the Web. Wherever Semantic Web content is not available, Piggy Bank can invoke screenscrapers to re-structure information within web pages into Semantic Web format. Through the use of Semantic Web technologies, Piggy Bank provides direct, immediate benefits to users in their use of the existing Web. Thus, the existence of even just a few Semantic Web-enabled sites or a few scrapers already benefits users. Piggy Bank thereby offers an easy, incremental upgrade path to users without requiring a wholesale adoption of the Semantic Web's vision.To further improve this Semantic Web experience, we have created Semantic Bank, a web server application that lets Piggy Bank users share the Semantic Web information they have collected, enabling collaborative efforts to build sophisticated Semantic Web information repositories through simple, everyday's use of Piggy Bank.     

Mining the Semantic Web

In the Semantic Web vision of the World Wide Web, content will not only be accessible to humans but will also be available in machine interpretable form as ontological knowledge bases. Ontological knowledge bases enable formal querying and reasoning and, consequently, a main research focus has been the investigation of how deductive reasoning can be utilized in ontological representations to enable more advanced applications. However, purely logic methods have not yet proven to be very effective for several reasons: First, there still is the unsolved problem of scalability of reasoning to Web scale. Second, logical reasoning has problems with uncertain information, which is abundant on Semantic Web data due to its distributed and heterogeneous nature. Third, the construction of ontological knowledge bases suitable for advanced reasoning techniques is complex, which ultimately results in a lack of such expressive real-world data sets with large amounts of instance data. From another perspective, the more expressive structured representations open up new opportunities for data mining, knowledge extraction and machine learning techniques. If moving towards the idea that part of the knowledge already lies in the data, inductive methods appear promising, in particular since inductive methods can inherently handle noisy, inconsistent, uncertain and missing data. While there has been broad coverage of inducing concept structures from less structured sources (text, Web pages), like in ontology learning, given the problems mentioned above, we focus on new methods for dealing with Semantic Web knowledge bases, relying on statistical inference on their standard representations. We argue that machine learning research has to offer a wide variety of methods applicable to different expressivity levels of Semantic Web knowledge bases: ranging from weakly expressive but widely available knowledge bases in RDF to highly expressive first-order knowledge bases, this paper surveys statistical approaches to mining the Semantic Web. We specifically cover similarity and distance-based methods, kernel machines, multivariate prediction models, relational graphical models and first-order probabilistic learning approaches and discuss their applicability to Semantic Web representations. Finally we present selected experiments which were conducted on Semantic Web mining tasks for some of the algorithms presented before. This is intended to show the breadth and general potential of this exiting new research and application area for data mining.     

Autonomous Semantic Web services

The Web is a collection of human-readable pages that are virtually unintelligible to computer programs. While the Web emerged as a global repository of digitized information, this very information is, by and large, unavailable for automatic computation. Two parallel efforts have emerged in recent years that could overcome this paradox: the Semantic Web is providing tools for explicit markup of Web content, and Web services could create a network in which programs act as independent agents that produce and consume information, enabling automated business transactions. The DARPA Agent Markup Language for Services (DAML-S) provides a mechanism that begins to bridge the gap between the Web services infrastructure and the Semantic Web.     

Adaptive systems in the era of the semantic and social web, a survey

In this paper we provide a classification of adaptive systems with respect to the kind of semantic technology they exploit to accomplish or improve specific adaptation and user modeling tasks. This classification is based on a distinction between strong semantic techniques and weak semantic techniques. The former are techniques based on the Semantic Web, while the latter regard technologies that, in different ways, annotate resources, enriching their meaning. This second category includes, in particular, Web 2.0 social annotations and mixed approaches between social annotations and Semantic Web techniques. While the impact of the Semantic Web on adaptive systems has been discussed in several survey papers, the potential of weak semantic technologies has, so far, received little attention. The aim of this analysis is to fill this gap. Therefore, we will discuss contributions and limits of both approaches, but we will focus special attention on weak semantic adaptive systems.     

Proof explanation for a nonmonotonic Semantic Web rules language

In this work, we present the design and implementation of a system for proof explanation in the Semantic Web, based on defeasible reasoning. Trust is a vital feature for Semantic Web. If users (humans and agents) are to use and integrate system answers, they must trust them. Thus, systems should be able to explain their actions, sources, and beliefs. Our system produces automatically proof explanations using a popular logic programming system (XSB), by interpreting the output from the proof’s trace and converting it into a meaningful representation. It also supports an XML representation for agent communication, which is a common scenario in the Semantic Web. In this paper, we present the design and implementation of the system, a RuleML language extension for the representation of a proof explanation, and we give some examples of the system. The system in essence implements a proof layer for nonmonotonic rules on the Semantic Web.     

WebOWL: A Semantic Web search engine development experiment

This paper presents WebOWL, an experiment in using the latest technologies to develop a Semantic Web search engine. WebOWL consists of a community of intelligent agents, acting as crawlers, that are able to discover and learn the locations of Semantic Web neighborhoods on the Web, a semantic database to store data from different ontologies, a query mechanism that supports semantic queries in OWL, and a ranking algorithm that determines the order of the returned results based on the semantic relationships of classes and individuals. The system has been implemented using Jade, Jena and the db4o object database engine and has successfully stored over one million OWL classes, individuals and properties.     

Collective knowledge systems: Where the Social Web meets the Semantic Web

What can happen if we combine the best ideas from the Social Web and Semantic Web? The Social Web is an ecosystem of participation, where value is created by the aggregation of many individual user contributions. The Semantic Web is an ecosystem of data, where value is created by the integration of structured data from many sources. What applications can best synthesize the strengths of these two approaches, to create a new level of value that is both rich with human participation and powered by well-structured information? This paper proposes a class of applications called collective knowledge systems, which unlock the “collective intelligence” of the Social Web with knowledge representation and reasoning techniques of the Semantic Web.     

Towards Ontology Generation from Tables

At the heart of today's information-explosion problems are issues involving semantics, mutual understanding, concept matching, and interoperability. Ontologies and the Semantic Web are offered as a potential solution, but creating ontologies for real-world knowledge is nontrivial. If we could automate the process, we could significantly improve our chances of making the Semantic Web a reality. While understanding natural language is difficult, tables and other structured information make it easier to interpret new items and relations. In this paper we introduce an approach to generating ontologies based on table analysis. We thus call our approach TANGO (Table ANalysis for Generating Ontologies). Based on conceptual modeling extraction techniques, TANGO attempts to (i) understand a table's structure and conceptual content; (ii) discover the constraints that hold between concepts extracted from the table; (iii) match the recognized concepts with ones from a more general specification of related concepts; and (iv) merge the resulting structure with other similar knowledge representations. TANGO is thus a formalized method of processing the format and content of tables that can serve to incrementally build a relevant reusable conceptual ontology.     

Search on the Semantic Web

To help human users and software agents find relevant knowledge on the Semantic Web, the Swoogle search engine discovers, indexes, and analyzes the ontologies and facts that are encoded in Semantic Web documents.     

基于语义距离度量模型的语义Web服务匹配排序机制

随着语义Web服务技术研究工作的不断深入,因特网上语义Web服务数量急剧增加。如何快速便捷地定位可用语义Web服务已经成为一个迫切且关键的问题。在语义Web服务匹配技术研究中,其中一个重要的研究主题就是语义Web服务匹配结果的排序机制。本文在综合概括和分析各种关于语义Web服务匹配结果排序机制的基础上,提出了一种基于语义距离度量模型的语义Web服务匹配结果排序机制,利用该排序机制,计算待匹配语义Web服务的语义相似度量,并依据此度量对语义Web服务匹配结果进行排序。该度量模型将语义Web服务引用概念间的语义关系转换成可精确比较的量化度量值,对属于相同语义匹配类型的匹配候选服务也能够分别计算语义距离,区分出相同匹配类型的候选服务与服务请求的匹配程度,从而达到改善用户对语义Web服务的搜索体验的目的。