10,652 research outputs found
The Semantic Automated Discovery and Integration (SADI) Web service Design-Pattern, API and Reference Implementation
Background. 
The complexity and inter-related nature of biological data poses a difficult challenge for data and tool integration. There has been a proliferation of interoperability standards and projects over the past decade, none of which has been widely adopted by the bioinformatics community. Recent attempts have focused on the use of semantics to assist integration, and Semantic Web technologies are being welcomed by this community.

Description. 
SADI – Semantic Automated Discovery and Integration – is a lightweight set of fully standards-compliant Semantic Web service design patterns that simplify the publication of services of the type commonly found in bioinformatics and other scientific domains. Using Semantic Web technologies at every level of the Web services “stack”, SADI services consume and produce instances of OWL Classes following a small number of very straightforward best-practices. In addition, we provide codebases that support these best-practices, and plug-in tools to popular developer and client software that dramatically simplify deployment of services by providers, and the discovery and utilization of those services by their consumers.

Conclusions.
SADI Services are fully compliant with, and utilize only foundational Web standards; are simple to create and maintain for service providers; and can be discovered and utilized in a very intuitive way by biologist end-users. In addition, the SADI design patterns significantly improve the ability of software to automatically discover appropriate services based on user-needs, and automatically chain these into complex analytical workflows. We show that, when resources are exposed through SADI, data compliant with a given ontological model can be automatically gathered, or generated, from these distributed, non-coordinating resources - a behavior we have not observed in any other Semantic system. Finally, we show that, using SADI, data dynamically generated from Web services can be explored in a manner very similar to data housed in static triple-stores, thus facilitating the intersection of Web services and Semantic Web technologies
The Knowledge of the Grid: A Grid Ontology
This paper presents a knowledge architecture and set of ontologies that can be used as the foundation to facilitate the matching of abstract resource requests to services and resources, to determine the functional equivalence of Grid middle wares and deployments and to allow the description of ‘hybrid’ compound Grids composed of individual heterogeneous Grids. This is necessary as in all these cases what is required is mediation between different views or descriptions of Grids, which requires a formal reference vocabulary. We present a framework and ontologies for achieving this
Integration of environmental data in BIM tool & linked building data
Environmental assessment is a critical need to ensure building sustainability. In order to enhance the sustainability of building, involved actors should be able to access and share not only information about the building but also data about products and especially their environmental assessment. Among several approaches that have been proposed to achieve that, semantic web technologies stand out from the crowd by their capabilities to share data and enhance interoperability in between the most heterogeneous systems. This paper presents the implementation of a method in which semantic web technologies and particularly Linked Data have been combined with Building Information Modelling (BIM) tools to foster building sustainability by introducing products with their environmental assessment in building data during the modelling phase. Based on Linked Building Data (LBD) vocabularies and environmental data, several ontologies have been generated in order to make both of them available as Resource Description Framework (RDF) graphs. A database access plugin has been developed and installed in a BIM tool. In that way, the LBD generated from the BIM tool contains, for each product a reference to its environmental assessment which is contained in a triplestore
S-OGSA as a Reference Architecture for OntoGrid and for the Semantic Grid
The Grid aims to support secure, flexible and coordinated resource sharing through providing a middleware platform for advanced distributing computing. Consequently, the Grid’s infrastructural machinery aims to allow collections of any kind of resources—computing, storage, data sets, digital libraries, scientific instruments, people, etc—to easily form Virtual Organisations (VOs) that cross organisational boundaries in order to work together to solve a problem. A Grid depends on understanding the available resources, their capabilities, how to assemble them and how to best exploit them. Thus Grid middleware and the Grid applications they support thrive on the metadata that describes resources in all their forms, the VOs, the policies that drive then and so on, together with the knowledge to apply that metadata intelligently
An ontology enhanced parallel SVM for scalable spam filter training
This is the post-print version of the final paper published in Neurocomputing. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Spam, under a variety of shapes and forms, continues to inflict increased damage. Varying approaches including Support Vector Machine (SVM) techniques have been proposed for spam filter training and classification. However, SVM training is a computationally intensive process. This paper presents a MapReduce based parallel SVM algorithm for scalable spam filter training. By distributing, processing and optimizing the subsets of the training data across multiple participating computer nodes, the parallel SVM reduces the training time significantly. Ontology semantics are employed to minimize the impact of accuracy degradation when distributing the training data among a number of SVM classifiers. Experimental results show that ontology based augmentation improves the accuracy level of the parallel SVM beyond the original sequential counterpart
Ontology (Science)
Increasingly, in data-intensive areas of the life sciences, experimental results are being described in algorithmically useful ways with the help of ontologies. Such ontologies are authored and maintained by scientists to support the retrieval, integration and analysis of their data. The proposition to be defended here is that ontologies of this type – the Gene Ontology (GO) being the most conspicuous example – are a _part of science_. Initial evidence for the truth of this proposition (which some will find self-evident) is the increasing recognition of the importance of empirically-based methods of evaluation to the ontology develop¬ment work being undertaken in support of scientific research. Ontologies created by scientists must, of course, be associated with implementations satisfying the requirements of software engineering. But the ontologies are not themselves engineering artifacts, and to conceive them as such brings grievous consequences. Rather, ontologies such as the GO are in different respects comparable to scientific theories, to scientific databases, and to scientific journal publications. Such a view implies a new conception of what is involved in the author¬ing, maintenance and application of ontologies in scientific contexts, and therewith also a new approach to the evaluation of ontologies and to the training of ontologists
NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings
Current approaches for service composition (assemblies of atomic services)
require developers to use: (a) domain-specific semantics to formalize services
that restrict the vocabulary for their descriptions, and (b) translation
mechanisms for service retrieval to convert unstructured user requests to
strongly-typed semantic representations. In our work, we argue that effort to
developing service descriptions, request translations, and matching mechanisms
could be reduced using unrestricted natural language; allowing both: (1)
end-users to intuitively express their needs using natural language, and (2)
service developers to develop services without relying on syntactic/semantic
description languages. Although there are some natural language-based service
composition approaches, they restrict service retrieval to syntactic/semantic
matching. With recent developments in Machine learning and Natural Language
Processing, we motivate the use of Sentence Embeddings by leveraging richer
semantic representations of sentences for service description, matching and
retrieval. Experimental results show that service composition development
effort may be reduced by more than 44\% while keeping a high precision/recall
when matching high-level user requests with low-level service method
invocations.Comment: This paper will appear on SCC'19 (IEEE International Conference on
Services Computing) on July 1
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