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.

Discovering simple mappings between relational database schemas and ontologies

Abstract: Ontologies proliferate with the growth of the Semantic Web. However, most of data on the Web are still stored in relational databases. Therefore, it is important to establish interoperability between relational databases and ontologies for creating a Web of data. An effective way to achieve interoperability is finding mappings between relational database schemas and ontologies. In this paper, we propose a new approach to discovering simple mappings between a relational database schema and an ontology. It exploits simple mappings based on virtual documents, and eliminates incorrect mappings via validating mapping consistency. Additionally, it also constructs a special type of semantic mappings, called contextual mappings, which is useful for practical applications. Experimental results demonstrate that our approach performs well on several data sets from real world domains.

Microformats: the next (small) thing on the semantic Web?

Abstract: Clever application of existing XHTML elements and class attributes can make it easier to describe people, places, events, and other semistructured information in human-readable form. In this paper, the author takes a more detailed look at some examples of microformats, the general principles by which they can be constructed, and how a community of users is forming around these seemingly ad hoc specifications to advance the cause of what some call an alternative to the semantic Web, the "lowercase semantic Web".

A Semantic IoT Early Warning System for Natural Environment Crisis Management

Abstract: An early warning system (EWS) is a core type of data driven Internet of Things (IoTs) system used for environment disaster risk and effect management. The potential benefits of using a semantic-type EWS include easier sensor and data source plug-and-play, simpler, richer, and more dynamic metadata-driven data analysis and easier service interoperability and orchestration. The challenges faced during practical deployments of semantic EWSs are the need for scalable time-sensitive data exchange and processing (especially involving heterogeneous data sources) and the need for resilience to changing ICT resource constraints in crisis zones. We present a novel IoT EWS system framework that addresses these challenges, based upon a multisemantic representation model. We use lightweight semantics for metadata to enhance rich sensor data acquisition. We use heavyweight semantics for top level W3C Web Ontology Language ontology models describing multileveled knowledge-bases and semantically driven decision support and workflow orchestration. This approach is validated through determining both system related metrics and a case study involving an advanced prototype system of the semantic EWS, integrated with a deployed EWS infrastructure.

Resource space model, OWL and database: Mapping and integration

Abstract: Semantics exhibits diversity in the real world, mental abstraction world, document world, and machine world. Studying mappings between different forms of semantics helps unveil the uniformity in the diversity. This article investigates the mappings between three typical semantic models: the Web ontology language (OWL), relational database model, and resource space model (a classification-based semantic model). By establishing mappings between the semantic primitives of the three models, we study the mapping from OWL description onto resource space and analyze the normal forms of the generated resource space. Mapping back from resource space onto OWL description is then discussed. Further, we investigate the mapping between OWL description and relational database, as well as the mapping between relational database and resource space. Normal forms of the generated relational tables are analyzed. To support advanced applications on the future Web, we suggest integrating the resource space, OWL, and databases to form a powerful semantic platform that enables different semantic models to enhance each other.

Managing Co-reference on the Semantic Web

Abstract: Co-reference resolution, or the determination of ‘equivalent’ URIs referring to the same concept or entity, is a significant hurdle to overcome in the realisation of large scale Semantic Web applications. However, it has only recently gained the attention of research communities in the Semantic Web context, and while activities are now underway in identifying co-referent or conflated URIs, little consideration has been given to tools and techniques for storing, manipulating, and reusing co-reference information. This paper provides an overview of the specification, implementation, interactions and experiences in using the Co-reference Resolution Service (CRS)to facilitate rigorous management of URI co-reference data, and enable interoperation between multiple Linked Open Data sources. Comparisons are made throughout the paper contrasting the differences in the way the CRS manages multiple URIs for the same resource with the emerging practice of using owl:sameAs to identify duplicate URIs. The advantages and benefits that have been gained from deploying the CRS on a site with multiple Linked Data repositories are also highlighted.