Ontology based knowledge formulation and an interpretation engine for intelligent devices in pervasive environments

Ongoing device miniaturization makes it possible to manufacture very small devices; therefore more of them can be embedded in one space. Pervasive computing con- cepts, envisioning computers distributed in a space and hidden from users' sight, presented by Weiser in 1991 are becoming more realistic and feasible to implement. A technology supporting pervasive computing and Ambient Intelligence also needs to follow miniaturization. The Ambient Intelligence domain was mainly focused on supercomputers with large computation power and it is now moving towards smaller devices, with limited computation power, and takes inspiration from dis- tributed systems, ad-hoc networks and emergent computing. The ability to process knowledge, understand network protocols, adapt and learn is becoming a required capability from fairly small and energy-frugal devices. This research project con- sists of two main parts. The first part of the project has created a context aware generic knowledgebase interpretation engine that enables autonomous devices to pervasively manage smart spaces using Communicating Sequential Processes as the underlying design methodology. In the second part a knowledgebase containing all the information that is needed for a device to cooperate, make decisions and react was designed and constructed. The interpretation engine is designed to be suitable for devices from different vendors, as it enables semantic interoperability based on the use of ontologies. The knowledge, that the engine interprets, is drawn from an ontology and the model of the chosen ontology is fixed in the engine. This project has investigated, designed and built a prototype of the knowledge base interpretation engine. Functional testing was performed using a simulation implemented in JCSP. The implementation simulates many autonomous devices running in parallel, communicating using a broadcast-based protocol, self-organizing into sub-networks and reacting to users' requests. The main goal of the project was to design and investigate the knowledge interpretation engine, determine the number of functions that the engine performs, to enable hardware realisation, and investigate the knowledgebase represented with use of RDF triples and chosen ontology model. This project was undertaken in collaboration with NXP Semiconductor Research Eindhoven, The Netherlands.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Ontology based knowledge formulation and an interpretation engine for intelligent devices in pervasive environments

Ongoing device miniaturization makes it possible to manufacture very small devices; therefore more of them can be embedded in one space. Pervasive computing concepts, envisioning computers distributed in a space and hidden from users' sight, presented by Weiser in 1991 are becoming more realistic and feasible to implement.A technology supporting pervasive computing and Ambient Intelligence also needs to follow miniaturization. The Ambient Intelligence domain was mainly focused on supercomputers with large computation power and it is now moving towards smaller devices, with limited computation power, and takes inspiration from distributed systems, ad-hoc networks and emergent computing. The ability to process knowledge, understand network protocols, adapt and learn is becoming a required capability from fairly small and energy-frugal devices. This research project consists of two main parts. The first part of the project has created a context aware generic knowledgebase interpretation engine that enables autonomous devices to pervasively manage smart spaces using Communicating Sequential Processes as the underlying design methodology. In the second part a knowledgebase containing all the information that is needed for a device to cooperate, make decisions and react was designed and constructed. The interpretation engine is designed to be suitable for devices from different vendors, as it enables semantic interoperability based on the use of ontologies. The knowledge, that the engine interprets, is drawn from an ontology and the model of the chosen ontology is fixed in the engine. This project has investigated, designed and built a prototype of the knowledge base interpretation engine. Functional testing was performed using a simulation implemented in JCSP. The implementation simulates many autonomous devices running in parallel, communicating using a broadcast-based protocol, self-organizing into sub-networks and reacting to users' requests. The main goal of the project was to design and investigate the knowledge interpretation engine, determine the number of functions that the engine performs, to enable hardware realisation, and investigate the knowledgebase represented with use of RDF triples and chosen ontology model. This project was undertaken in collaboration with NXP Semiconductor Research Eindhoven, The Netherlands

Resolving semantic conflicts through ontological layering

We examine the problem of semantic interoperability in modern software systems, which exhibit pervasiveness, a range of heterogeneities and in particular, semantic heterogeneity of data models which are built upon ubiquitous data repositories. We investigate whether we can build ontologies upon heterogeneous data repositories in order to resolve semantic conflicts in them, and achieve their semantic interoperability. We propose a layered software architecture, which accommodates in its core, ontological layering, resulting in a Generic ontology for Context aware, Interoperable and Data sharing (Go-CID) software applications. The software architecture supports retrievals from various data repositories and resolves semantic conflicts which arise from heterogeneities inherent in them. It allows extendibility of heterogeneous data repositories through ontological layering, whilst preserving the autonomy of their individual elements. Our specific ontological layering for interoperable data repositories is based on clearly defined reasoning mechanisms in order to perform ontology mappings. The reasoning mechanisms depend on the user‟s involvments in retrievals of and types of semantic conflicts, which we have to resolve after identifying semantically related data. Ontologies are described in terms of ontological concepts and their semantic roles that make the types of semantic conflicts explicit. We contextualise semantically related data through our own categorisation of semantic conflicts and their degrees of similarities. Our software architecture has been tested through a case study of retrievals of semantically related data across repositories in pervasive healthcare and deployed with Semantic Web technology. The extensions to the research results include the applicability of our ontological layering and reasoning mechanisms in various problem domains and in environments where we need to (i) establish if and when we have overlapping “semantics”, and (ii) infer/assert a correct set of “semantics” which can support any decision making in such domains