Showing papers by "Payam Barnaghi published in 2008"
01 Jan 2008
TL;DR: The European project SENSEI plays a leading role within the current efforts to create an underlying architecture and services for the future Internet and to realize the vision of the real world Internet as mentioned in this paper.
Abstract: The Internet extends its reach to the real world through innovations collectively termed the Internet of Things (IoT). The IoT aims at integrating technologies such as radio frequency identification, wireless sensor and actuator networks (WSANs), and networked embedded devices. Recent ideas envision the Internet as an all encompassing infrastructure that connects the physical into the digital world: the real world Internet (RWI). The European project SENSEI plays a leading role within the current efforts to create an underlying architecture and services for the future Internet and to realize the vision of the RWI.
124 citations
01 Jun 2008
TL;DR: A generalised semantic search framework is formalised based on which a number of pilot projects and corresponding practical systems focusing on their objectives, methodologies and most distinctive characteristics are investigated.
Abstract: Research on semantic search aims to improve conventional information search and retrieval methods, and facilitate information acquisition, processing, storage and retrieval on the semantic web. The past ten years have seen a number of implemented semantic search systems and various proposed frameworks. A comprehensive survey is needed to gain an overall view of current research trends in this field. We have investigated a number of pilot projects and corresponding practical systems focusing on their objectives, methodologies and most distinctive characteristics. In this paper, we report our study and findings based on which a generalised semantic search framework is formalised. Further, we describe issues with regards to future research in this area.
36 citations
01 Jan 2008
TL;DR: Issues related to knowledge acquisition for semantic search systems are discussed, in particular, ontology learning from unstructured text corpus, which is an automatic knowledge acquisition process using different techniques.
Abstract: Semantic search extends the scope of conventional information search and retrieval paradigms from documentoriented and to entity and knowledge-centric search and retrieval. By attempting to provide direct and intuitive answers such systems alleviate information overload problem and reduce information seekers’ cognitive overhead. Ontologies and knowledge bases are fundamental cornerstones in semantic search systems based on which sophisticated search mechanisms and efficient search services are designed. Nevertheless, acquisition of quality knowledge from heterogeneous sources on the Web is never a trivial task. Transformation of data in existing databases seems a promising bootstrapping approach, while information providers may refuse to do so because of intellectual property issues. In this article we discuss issues related to knowledge acquisition for semantic search systems. In particular, we discuss ontology learning from unstructured text corpus, which is an automatic knowledge acquisition process using different techniques.
2 citations