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.

A context-based mediation approach to compose semantic Web services

Abstract: Web services composition is a keystone in the development of interoperable systems. However, despite the widespread adoption of Web services, several obstacles still hinder their smooth automatic semantic reconciliation when being composed. Consistent understanding of data exchanged between composed Web services is hampered by various implicit modeling assumptions and representations. Our contribution in this article revolves around context and how it enriches data exchange between Web services. In particular, a context-based mediation approach to solve semantic heterogeneities between composed Web services is presented.

The Personal Reader: Personalizing and Enriching Learning Resources Using Semantic Web Technologies

Abstract: Traditional adaptive hypermedia systems have focused on providing adaptation functionality on a closed corpus, while Web search interfaces have delivered non-personalized information to users. In this paper, we show how we integrate closed corpus adaptation and global context provision in a Personal Reader environment. The local context consists of individually optimized recommendations to learning materials within the given corpus; the global context provides individually optimized recommendations to resources found on the Web, e. g., FAQs, student exercises, simulations, etc. The adaptive local context of a learning resource is generated by applying methods from adaptive educational hypermedia in a semantic web setting. The adaptive global context is generated by constructing appropriate queries, enrich them based on available user profile information, and, if necessary, relax them during the querying process according to available metadata.

Hierarchical situation modeling and reasoning for pervasive computing

Abstract: Situation awareness is one of the most fundamental features of entities in pervasive computing environments to dynamically adapt their behavior to situation changes to satisfy user requirements, including security and privacy. In order to support situation-aware adaptation, it is necessary to model and specify context and situation in a way such that multiple entities can easily exchange, share and reuse their knowledge on context and situation. In this paper, an OWL-based situation ontology to model situation hierarchically to facilitate sharing and reusing of situation knowledge and logic inferences is presented. The conversion of OWL situation ontology specifications to the first-order logic (FOL) representations, and the performance of FOL rule-based reasoning in terms of problem size and time are discussed.

A self-training approach for resolving object coreference on the semantic web

Abstract: An object on the Semantic Web is likely to be denoted with multiple URIs by different parties. Object coreference resolution is to identify "equivalent" URIs that denote the same object. Driven by the Linking Open Data (LOD) initiative, millions of URIs have been explicitly linked with owl:sameAs statements, but potentially coreferent ones are still considerable. Existing approaches address the problem mainly from two directions: one is based upon equivalence inference mandated by OWL semantics, which finds semantically coreferent URIs but probably omits many potential ones; the other is via similarity computation between property-value pairs, which is not always accurate enough. In this paper, we propose a self-training approach for object coreference resolution on the Semantic Web, which leverages the two classes of approaches to bridge the gap between semantically coreferent URIs and potential candidates. For an object URI, we firstly establish a kernel that consists of semantically coreferent URIs based on owl:sameAs, (inverse) functional properties and (max-)cardinalities, and then extend such kernel iteratively in terms of discriminative property-value pairs in the descriptions of URIs. In particular, the discriminability is learnt with a statistical measurement, which not only exploits key characteristics for representing an object, but also takes into account the matchability between properties from pragmatics. In addition, frequent property combinations are mined to improve the accuracy of the resolution. We implement a scalable system and demonstrate that our approach achieves good precision and recall for resolving object coreference, on both benchmark and large-scale datasets.

Applying semantic web services to bioinformatics: experiences gained, lessons learnt

Abstract: We have seen an increasing amount of interest in the application of Semantic Web technologies to Web services. The aim is to support automated discovery and composition of the services allowing seamless and transparent interoperability. In this paper we discuss three projects that are applying such technologies to bioinformatics: Grid, MOBY-Services and Semantic-MOBY. Through an examination of the differences and similarities between the solutions produced, we highlight some of the practical difficulties in developing Semantic Web services and suggest that the experiences with these projects have implications for the development of Semantic Web services as a whole.