Security-oriented data grids for microarray expression profiles

Microarray experiments are one of the key ways in which gene activity can be identified and measured thereby shedding light and understanding for example on biological processes. The BBSRC funded Grid enabled Microarray Expression Profile Search (GEMEPS) project has developed an infrastructure which allows post-genomic life science researchers to ask and answer the following questions: who has undertaken microarray experiments that are in some way similar or relevant to mine; and how similar were these relevant experiments? Given that microarray experiments are expensive to undertake and may possess crucial information for future exploitation (both academically and commercially), scientists are wary of allowing unrestricted access to their data by the wider community until fully exploited locally. A key requirement is thus to have fine grained security that is easy to establish and simple (or ideally transparent) to use across inter-institutional virtual organisations. In this paper we present an enhanced security-oriented data Grid infrastructure that supports the definition of these kinds of queries and the analysis and comparison of microarray experiment results

An Automated System for the Assessment and Ranking of Domain Ontologies

As the number of intelligent software applications and the number of semantic websites continue to expand, ontologies are needed to formalize shared terms. Often it is necessary to either find a previously used ontology for a particular purpose, or to develop a new one to meet a specific need. Because of the challenge involved in creating a new ontology from scratch, the latter option is often preferable. The ability of a user to select an appropriate, high-quality domain ontology from a set of available options would be most useful in knowledge engineering and in developing intelligent applications. Being able to assess an ontology\u27s quality and suitability is also important when an ontology is developed from the beginning. These capabilities, however, require good quality assessment mechanisms as well as automated support when there are a large number of ontologies from which to make a selection. This thesis provides an in-depth analysis of the current research in domain ontology evaluation, including the development of a taxonomy to categorize the numerous directions the research has taken. Based on the lessons learned from the literature review, an approach to the automatic assessment of domain ontologies is selected and a suite of ontology quality assessment metrics grounded in semiotic theory is presented. The metrics are implemented in a Domain Ontology Rating System (DoORS), which is made available as an open source web application. An additional framework is developed that would incorporate this rating system as part of a larger system to find ontology libraries on the web, retrieve ontologies from them, and assess them to select the best ontology for a particular task. An empirical evaluation in four phases shows the usefulness of the work, including a more stringent evaluation of the metrics that assess how well an ontology fits its domain and how well an ontology is regarded within its community of users

Common vocabularies for collective intelligence - work in progress

Web based applications and tools offer a great potential to increase the efficiency of information flow and communication among different agents during emergencies. Among the different factors, technical and non technical, that hinder the integration of an information model in emergency management sector, is a lack of a common, shared vocabulary. This paper furthers previous work in the area of ontology development, and presents a summary and overview of the goal, process and methodology to construct a shared set of metadata that can be used to map existing vocabulary. This paper is a work in progress report

Ontologies on the semantic web

As an informational technology, the World Wide Web has enjoyed spectacular success. In just ten years it has transformed the way information is produced, stored, and shared in arenas as diverse as shopping, family photo albums, and high-level academic research. The “Semantic Web” was touted by its developers as equally revolutionary but has not yet achieved anything like the Web’s exponential uptake. This 17 000 word survey article explores why this might be so, from a perspective that bridges both philosophy and IT

What are Links in Linked Open Data? A Characterization and Evaluation of Links between Knowledge Graphs on the Web

Linked Open Data promises to provide guiding principles to publish interlinked knowledge graphs on the Web in the form of findable, accessible, interoperable and reusable datasets. We argue that while as such, Linked Data may be viewed as a basis for instantiating the FAIR principles, there are still a number of open issues that cause significant data quality issues even when knowledge graphs are published as Linked Data. Firstly, in order to define boundaries of single coherent knowledge graphs within Linked Data, a principled notion of what a dataset is, or, respectively, what links within and between datasets are, has been missing. Secondly, we argue that in order to enable FAIR knowledge graphs, Linked Data misses standardised findability and accessability mechanism, via a single entry link. In order to address the first issue, we (i) propose a rigorous definition of a naming authority for a Linked Data dataset (ii) define different link types for data in Linked datasets, (iii) provide an empirical analysis of linkage among the datasets of the Linked Open Data cloud, and (iv) analyse the dereferenceability of those links. We base our analyses and link computations on a scalable mechanism implemented on top of the HDT format, which allows us to analyse quantity and quality of different link types at scale.Series: Working Papers on Information Systems, Information Business and Operation

Ontology selection for reuse: Will it ever get easier?

Ontologists and knowledge engineers tend to examine different aspects of ontologies when assessing their suitability for reuse. However, most of the evaluation metrics and frameworks introduced in the literature are based on a limited set of internal characteristics of ontologies and dismiss how the community uses and evaluates them. This paper used a survey questionnaire to explore, clarify and also confirm the importance of the set of quality related metrics previously found in the literature and an interview study. According to the 157 responses collected from ontologists and knowledge engineers, the process of ontology selection for reuse depends on different social and community related metrics and metadata. We believe that the findings of this research can contribute to facilitating the process of selecting an ontology for reuse

Exploiting a perdurantist foundational ontology and graph database for semantic data integration

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.The view of reality that is inherent to perdurantist philosophical ontologies, often termed four dimensional (4D) ontologies, has not been widely adopted within the mainstream of information system design practice. However, as the closed world of enterprise systems is opened to Internet scale Semantic Web and Open Data information sources, there is a need to better understand the semantics of both internal and external data and how they can be integrated. Philosophical foundational ontologies can help establish this understanding and there is, therefore, an emerging need to research how they can be applied to the problem of semantic data integration. Therefore, a prime objective of this research was to develop a framework through which to apply a 4D foundational ontology and a graph database to the problem of semantic data integration, and to assess the effectiveness of the approach. The research employed design science, a methodology which is applicable to undertaking research within information systems as it encompasses methods through which the research can be undertaken and the resultant artefacts evaluated. This methodology has a number of discrete stages: problem awareness; a core design-build-evaluate iterative cycle through which the research is conducted; and a conclusion stage. The design science research was conducted through the development of a number of artefacts, the prime being the 4D-Semantic Extract Load (4D-SETL) framework. The effectiveness of the framework was assessed by applying it to semantically interpret and integrate a number of large scale datasets and to instantiate a prototype graph database warehouse to persist the resultant ontology. A series of technical experiments confirmed that directly reflecting the model patterns of 4D ontology within a prototype data warehouse proved an effective means of both structuring and semantically integrating complex datasets and that the artefacts produced by 4D-SETL could function at scale. Through illustrative scenario, the effectiveness of the approach is described in relation to the ability of the framework to address a number of weaknesses in current approaches. Furthermore the major advantages of the 4D-SETL are elaborated; which include ability of the framework is to combine foundational, domain and instance level ontological models in a single coherent system that dispensed with much of the translation normally undertaken between conceptual, logical and physical data models. Additionally, adopting a perdurantist realist foundational ontology provided a clear means of establishing and maintaining the identity of physical objects as their constituent temporal and spatial parts unfold over the course of tim

Ontology Ranking: Finding the Right Ontologies on the Web

Ontology search, which is the process of finding ontologies or ontological terms for users’ defined queries from an ontology collection, is an important task to facilitate ontology reuse of ontology engineering. Ontology reuse is desired to avoid the tedious process of building an ontology from scratch and to limit the design of several competing ontologies that represent similar knowledge. Since many organisations in both the private and public sectors are publishing their data in RDF, they increasingly require to find or design ontologies for data annotation and/or integration. In general, there exist multiple ontologies representing a domain, therefore, finding the best matching ontologies or their terms is required to facilitate manual or dynamic ontology selection for both ontology design and data annotation. The ranking is a crucial component in the ontology retrieval process which aims at listing the ‘relevant0 ontologies or their terms as high as possible in the search results to reduce the human intervention. Most existing ontology ranking techniques inherit one or more information retrieval ranking parameter(s). They linearly combine the values of these parameters for each ontology to compute the relevance score against a user query and rank the results in descending order of the relevance score. A significant aspect of achieving an effective ontology ranking model is to develop novel metrics and dynamic techniques that can optimise the relevance score of the most relevant ontology for a user query. In this thesis, we present extensive research in ontology retrieval and ranking, where several research gaps in the existing literature are identified and addressed. First, we begin the thesis with a review of the literature and propose a taxonomy of Semantic Web data (i.e., ontologies and linked data) retrieval approaches. That allows us to identify potential research directions in the field. In the remainder of the thesis, we address several of the identified shortcomings in the ontology retrieval domain. We develop a framework for the empirical and comparative evaluation of different ontology ranking solutions, which has not been studied in the literature so far. Second, we propose an effective relationship-based concept retrieval framework and a concept ranking model through the use of learning to rank approach which addresses the limitation of the existing linear ranking models. Third, we propose RecOn, a framework that helps users in finding the best matching ontologies to a multi-keyword query. There the relevance score of an ontology to the query is computed by formulating and solving the ontology recommendation problem as a linear and an optimisation problem. Finally, the thesis also reports on an extensive comparative evaluation of our proposed solutions with several other state-of-the-art techniques using real-world ontologies. This thesis will be useful for researchers and practitioners interested in ontology search, for methods and performance benchmark on ranking approaches to ontology search