Integrating web services into data intensive web sites

Designing web sites is a complex task. Ad-hoc rapid prototyping easily leads to unsatisfactory results, e.g. poor maintainability and extensibility. However, existing web design frameworks focus exclusively on data presentation: the development of specific functionalities is still achieved through low-level programming. In this paper we address this issue by describing our work on the integration of (semantic) web services into a web design framework, OntoWeaver. The resulting architecture, OntoWeaver-S, supports rapid prototyping of service centred data-intensive web sites, which allow access to remote web services. In particular, OntoWeaver-S is integrated with a comprehensive web service platform, IRS-II, for the specification, discovery, and execution of web services. Moreover, it employs a set of comprehensive site ontologies to model and represent all aspects of service-centred data-intensive web sites, and thus is able to offer high level support for the design and development process

Semantic Query Optimisation with Ontology Simulation

Semantic Web is, without a doubt, gaining momentum in both industry and academia. The word "Semantic" refers to "meaning" - a semantic web is a web of meaning. In this fast changing and result oriented practical world, gone are the days where an individual had to struggle for finding information on the Internet where knowledge management was the major issue. The semantic web has a vision of linking, integrating and analysing data from various data sources and forming a new information stream, hence a web of databases connected with each other and machines interacting with other machines to yield results which are user oriented and accurate. With the emergence of Semantic Web framework the na\"ive approach of searching information on the syntactic web is clich\'e. This paper proposes an optimised semantic searching of keywords exemplified by simulation an ontology of Indian universities with a proposed algorithm which ramifies the effective semantic retrieval of information which is easy to access and time saving

UK utility data integration: overcoming schematic heterogeneity

In this paper we discuss syntactic, semantic and schematic issues which inhibit the integration of utility data in the UK. We then focus on the techniques employed within the VISTA project to overcome schematic heterogeneity. A Global Schema based architecture is employed. Although automated approaches to Global Schema definition were attempted the heterogeneities of the sector were too great. A manual approach to Global Schema definition was employed. The techniques used to define and subsequently map source utility data models to this schema are discussed in detail. In order to ensure a coherent integrated model, sub and cross domain validation issues are then highlighted. Finally the proposed framework and data flow for schematic integration is introduced

Using background knowledge for ontology evolution

One of the current bottlenecks for automating ontology evolution is resolving the right links between newly arising information and the existing knowledge in the ontology. Most of existing approaches mainly rely on the user when it comes to capturing and representing new knowledge. Our ontology evolution framework intends to reduce or even eliminate user input through the use of background knowledge. In this paper, we show how various sources of background knowledge could be exploited for relation discovery. We perform a relation discovery experiment focusing on the use of WordNet and Semantic Web ontologies as sources of background knowledge. We back our experiment with a thorough analysis that highlights various issues on how to improve and validate relation discovery in the future, which will directly improve the task of automatically performing ontology changes during evolution

BIM semantic-enrichment for built heritage representation

In the built heritage context, BIM has shown difficulties in representing and managing the large and complex knowledge related to non-geometrical aspects of the heritage. Within this scope, this paper focuses on a domain-specific semantic-enrichment of BIM methodology, aimed at fulfilling semantic representation requirements of built heritage through Semantic Web technologies. To develop this semantic-enriched BIM approach, this research relies on the integration of a BIM environment with a knowledge base created through information ontologies. The result is knowledge base system - and a prototypal platform - that enhances semantic representation capabilities of BIM application to architectural heritage processes. It solves the issue of knowledge formalization in cultural heritage informative models, favouring a deeper comprehension and interpretation of all the building aspects. Its open structure allows future research to customize, scale and adapt the knowledge base different typologies of artefacts and heritage activities

A Query Integrator and Manager for the Query Web

We introduce two concepts: the Query Web as a layer of interconnected queries over the document web and the semantic web, and a Query Web Integrator and Manager (QI) that enables the Query Web to evolve. QI permits users to write, save and reuse queries over any web accessible source, including other queries saved in other installations of QI. The saved queries may be in any language (e.g. SPARQL, XQuery); the only condition for interconnection is that the queries return their results in some form of XML. This condition allows queries to chain off each other, and to be written in whatever language is appropriate for the task. We illustrate the potential use of QI for several biomedical use cases, including ontology view generation using a combination of graph-based and logical approaches, value set generation for clinical data management, image annotation using terminology obtained from an ontology web service, ontology-driven brain imaging data integration, small-scale clinical data integration, and wider-scale clinical data integration. Such use cases illustrate the current range of applications of QI and lead us to speculate about the potential evolution from smaller groups of interconnected queries into a larger query network that layers over the document and semantic web. The resulting Query Web could greatly aid researchers and others who now have to manually navigate through multiple information sources in order to answer specific questions