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Semantic Web

About: Semantic Web is a research topic. Over the lifetime, 26987 publications have been published within this topic receiving 534275 citations. The topic is also known as: Sem Web & SemWeb.


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Proceedings ArticleDOI
03 Dec 2006
TL;DR: The role of ontologies in facilitating simulation modeling and the technical challenges in distributed simulation modeling are outlined and how ontology-based methods may be applied to address these challenges are described.
Abstract: Ontological analysis has been shown to be an effective first step in the construction of robust knowledge based systems. However, the modeling and simulation community has not taken advantage of the benefits of ontology management methods and tools. Moreover, the popularity of semantic technologies and the semantic web has provided several beneficial opportunities for the modeling and simulation communities of interest. This paper describes the role of ontologies in facilitating simulation modeling. It outlines the technical challenges in distributed simulation modeling and describes how ontology-based methods may be applied to address these challenges. The paper concludes by describing an ontology-based solution framework for simulation modeling and analysis and outlining the benefits of this solution approach.

97 citations

Proceedings ArticleDOI
03 Sep 2004
TL;DR: The architecture and implementation of a prototype semantic infrastructure, which uses semantic Web technologies to represent and retrieve tuples from a tuple space is discussed, by making use of a Web ontology language and RACER, a description-logic reasoning engine.
Abstract: Tuple spaces offer a coordination infrastructure for communication between autonomous entities by providing a logically shared memory along with data persistence, transactional security as well as temporal and spatial decoupling - properties that make it desirable in distributed systems for e-commerce and pervasive computing applications. In most tuple space implementations, tuples are retrieved by employing type-value matching of ordered tuples, object-based polymorphic matching, or XML-style pattern matching. In a heterogeneous environment, this can pose several limitations. This paper discusses the architecture and implementation of a prototype semantic infrastructure, which uses semantic Web technologies to represent and retrieve tuples from a tuple space. Semantic tuple spaces (sTuples) overcomes limitations of the JavaSpaces tuple space implementation, by making use of a Web ontology language and RACER, a description-logic reasoning engine. The sTuples infrastructure extends and integrates with Vigil, a secure framework for communication and access of intelligent services in a pervasive environment. Specialized agents, such as the tuple-recommender agent, task-execution agent and publish-subscribe agent, which have a better understanding of the environment, reside on the tuple space and play an important role in providing user-centric reasoning.

97 citations

Journal Article
TL;DR: A framework for evaluating the quality of ontologies based on the SQuaRE standard for software quality evaluation is proposed, which requires the definition of both a quality model and quality metrics for evaluatingThe quality of the ontology.
Abstract: The development of the Semantic Web has provoked an increasing interest in the development of ontologies. There are, however, few mechanisms for guiding users in making informed decisions on which ontology to use under given circumstances. In this paper, we propose a framework for evaluating the quality of ontologies based on the SQuaRE standard for software quality evaluation. This method requires the definition of both a quality model and quality metrics for evaluating the quality of the ontology. The quality model is divided into a series of quality dimensions or charac ter istics, such as structure or functional adequacy, which are organized into subcharacteristics, such as cohesion or tangledness. Thus, each subcharacteristic is evaluated by applying a series of quality metrics, which are automatically measured. Finally, each characteristic is evaluated by combining values of its subcharacteristics. This work also includes the application of this frame work for the evaluation of ontologies in two application domains.

97 citations

Journal ArticleDOI
TL;DR: The present quantitative confirmation sheds light on the connection between the success of the latest Web-mining techniques and the small world topology of the Web, with encouraging implications for the design of better crawling algorithms.
Abstract: Recent Web-searching and -mining tools are combining text and link analysis to improve ranking and crawling algorithms. The central assumption behind such approaches is that there is a correlation between the graph structure of the Web and the text and meaning of pages. Here I formalize and empirically evaluate two general conjectures drawing connections from link information to lexical and semantic Web content. The link-content conjecture states that a page is similar to the pages that link to it, and the link-cluster conjecture that pages about the same topic are clustered together. These conjectures are often simply assumed to hold, and Web search tools are built on such assumptions. The present quantitative confirmation sheds light on the connection between the success of the latest Web-mining techniques and the small world topology of the Web, with encouraging implications for the design of better crawling algorithms.

97 citations

Book ChapterDOI
11 Nov 2007
TL;DR: This work investigates how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data through decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned.
Abstract: The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.

97 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023116
2022348
2021412
2020612
2019782
2018881