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.

Data Driven Ontology Evaluation

Abstract: The evaluation of ontologies is vital for the growth of the Semantic Web. We consider a number of problems in evaluating a knowledge artifact like an ontology. We propose in this paper that one approach to ontology evaluation should be corpus or data driven. A corpus is the most accessible form of knowledge and its use allows a measure to be derived of the ‘fit’ between an ontology and a domain of knowledge. We consider a number of methods for measuring this ‘fit’ and propose a measure to evaluate structural fit, and a probabilistic approach to identifying the best ontology.

MAFRA - A MApping FRAmework for Distributed Ontologies

Abstract: Ontologies as means for conceptualizing and structuring domain knowledge within a community of interest are seen as a key to realize the Semantic Web vision. However, the decentralized nature of the Web makes achieving this consensus across communities difficult, thus, hampering efficient knowledge sharing between them. In order to balance the autonomy of each community with the need for interoperability, mapping mechanisms between distributed ontologies in the Semantic Web are required. In this paper we present MAFRA, an interactive, incremental and dynamic framework for mapping distributed ontologies.

Importing the Semantic Web in UDDI

Abstract: The web is moving from being a collection of pages toward a collection of services that interoperate through the Internet. A fundamental step toward this interoperation is the ability of automatically locating services on the bases of the functionalities that they provide. Such a functionality would allow services to locate each other and automatically interoperate. Location of web services is inherently a semantic problem, because it has to abstract from the superficial differences between representations of the services provided, and the services requested to recognize semantic similarities between the two.Current Web Services technology based on UDDI and WSDL does not make any use of semantic information and therefore fails to address the problem of matching between capabilities of services and allowing service location on the bases of what functionalities are sought, failing therefore to address the problem of locating web services. Nevertheless, previous work within DAML-S, a DAML-based language for service description, shows how ontological information collected through the semantic web can be used to match service capabilities. This work expands on previous work by showing how DAML-S Service Profiles, that describe service capabilities within DAML-S, can be mapped into UDDI records providing therefore a way to record semantic information within UDDI records. Furthermore we show how this encoded information can be used within the UDDI registry to perform semantic matching.

GoodRelations: An Ontology for Describing Products and Services Offers on the Web

Abstract: A promising application domain for Semantic Web technology is the annotation of products and services offerings on the Web so that consumers and enterprises can search for suitable suppliers using products and services ontologies. While there has been substantial progress in developing ontologies for typesof products and services, namely eClassOWL, this alone does not provide the representational means required for e-commerce on the Semantic Web. Particularly missing is an ontology that allows describing the relationships between (1) Web resources, (2) offerings made by means of those Web resources, (3) legal entities, (4) prices, (5) terms and conditions, and the aforementioned ontologies for products and services (6). For example, we must be able to say that a particular Web site describes an offer to sell cell phones of a certain make and model at a certain price, that a piano house offers maintenance for pianos that weigh less than 150 kg, or that a car rental company leases out cars of a certain make and model from a set of branches across the country. In this paper, we analyze the complexity of product description on the Semantic Web and define the GoodRelations ontology that covers the representational needs of typical business scenarios for commodity products and services.

Piazza: data management infrastructure for semantic web applications

Abstract: The Semantic Web envisions a World Wide Web in which data is described with rich semantics and applications can pose complex queries. To this point, researchers have defined new languages for specifying meanings for concepts and developed techniques for reasoning about them, using RDF as the data model. To flourish, the Semantic Web needs to be able to accommodate the huge amounts of existing data and the applications operating on them. To achieve this, we are faced with two problems. First, most of the world's data is available not in RDF but in XML; XML and the applications consuming it rely not only on the domain structure of the data, but also on its document structure. Hence, to provide interoperability between such sources, we must map between both their domain structures and their document structures. Second, data management practitioners often prefer to exchange data through local point-to-point data translations, rather than mapping to common mediated schemas or ontologies.This paper describes the Piazza system, which addresses these challenges. Piazza offers a language for mediating between data sources on the Semantic Web, which maps both the domain structure and document structure. Piazza also enables interoperation of XML data with RDF data that is accompanied by rich OWL ontologies. Mappings in Piazza are provided at a local scale between small sets of nodes, and our query answering algorithm is able to chain sets mappings together to obtain relevant data from across the Piazza network. We also describe an implemented scenario in Piazza and the lessons we learned from it.