1,077 research outputs found

    An open standard for the exchange of information in the Australian timber sector

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    The purpose of this paper is to describe business-to-business (B2B) communication and the characteristics of an open standard for electronic communication within the Australian timber and wood products industry. Current issues, future goals and strategies for using business-to-business communication will be considered. From the perspective of the Timber industry sector, this study is important because supply chain efficiency is a key component in an organisation's strategy to gain a competitive advantage in the marketplace. Strong improvement in supply chain performance is possible with improved business-to-business communication which is used both for building trust and providing real time marketing data. Traditional methods such as electronic data interchange (EDI) used to facilitate B2B communication have a number of disadvantages, such as high implementation and running costs and a rigid and inflexible messaging standard. Information and communications technologies (ICT) have supported the emergence of web-based EDI which maintains the advantages of the traditional paradigm while negating the disadvantages. This has been further extended by the advent of the Semantic web which rests on the fundamental idea that web resources should be annotated with semantic markup that captures information about their meaning and facilitates meaningful machine-to-machine communication. This paper provides an ontology using OWL (Web Ontology Language) for the Australian Timber sector that can be used in conjunction with semantic web services to provide effective and cheap B2B communications

    The state of semantic technology today - overview of the first SEALS evaluation campaigns

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    This paper describes the first five SEALS Evaluation Campaigns over the semantic technologies covered by the SEALS project (ontology engineering tools, ontology reasoning tools, ontology matching tools, semantic search tools, and semantic web service tools). It presents the evaluations and test data used in these campaigns and the tools that participated in them along with a comparative analysis of their results. It also presents some lessons learnt after the execution of the evaluation campaigns and draws some final conclusions

    Towards an Infrastructure for the Evaluation of Semantic Technologies

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    This paper presents and discusses the current development status of the SEALS Platform, a lasting reference infrastructure for semantic technology evaluation. It describes the different entities managed by the platform and the ontology-based model that has been defined to represent them; it also provides an overview of the platform architecture. In addition, it presents the different challenges faced during the development of the SEALS Platform and a use scenario of the platform that supports the execution of evaluation campaigns over semantic technologies

    Characterizing the Landscape of Musical Data on the Web: State of the Art and Challenges

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    Musical data can be analysed, combined, transformed and exploited for diverse purposes. However, despite the proliferation of digital libraries and repositories for music, infrastructures and tools, such uses of musical data remain scarce. As an initial step to help fill this gap, we present a survey of the landscape of musical data on the Web, available as a Linked Open Dataset: the musoW dataset of catalogued musical resources. We present the dataset and the methodology and criteria for its creation and assessment. We map the identified dimensions and parameters to existing Linked Data vocabularies, present insights gained from SPARQL queries, and identify significant relations between resource features. We present a thematic analysis of the original research questions associated with surveyed resources and identify the extent to which the collected resources are Linked Data-ready

    Semantically-aware data discovery and placement in collaborative computing environments

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    As the size of scientific datasets and the demand for interdisciplinary collaboration grow in modern science, it becomes imperative that better ways of discovering and placing datasets generated across multiple disciplines be developed to facilitate interdisciplinary scientific research. For discovering relevant data out of large-scale interdisciplinary datasets. The development and integration of cross-domain metadata is critical as metadata serves as the key guideline for organizing data. To develop and integrate cross-domain metadata management systems in interdisciplinary collaborative computing environment, three key issues need to be addressed: the development of a cross-domain metadata schema; the implementation of a metadata management system based on this schema; the integration of the metadata system into existing distributed computing infrastructure. Current research in metadata management in distributed computing environment largely focuses on relatively simple schema that lacks the underlying descriptive power to adequately address semantic heterogeneity often found in interdisciplinary science. And current work does not take adequate consideration the issue of scalability in large-scale data management. Another key issue in data management is data placement, due to the increasing size of scientific datasets, the overhead incurred as a result of transferring data among different nodes also grow into a significant inhibiting factor affecting overall performance. Currently, few data placement strategies take into consideration semantic information concerning data content. In this dissertation, we propose a cross-domain metadata system in a collaborative distributed computing environment and identify and evaluate key factors and processes involved in a successful cross-domain metadata system with the goal of facilitating data discovery in collaborative environments. This will allow researchers/users to conduct interdisciplinary science in the context of large-scale datasets that will make it easier to access interdisciplinary datasets, reduce barrier to collaboration, reduce cost of future development of similar systems. We also investigate data placement strategies that involve semantic information about the hardware and network environment as well as domain information in the form of semantic metadata so that semantic locality could be utilized in data placement, that could potentially reduce overhead for accessing large-scale interdisciplinary datasets

    OPTION: OPTImization Algorithm Benchmarking ONtology

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    Many optimization algorithm benchmarking platforms allow users to share their experimental data to promote reproducible and reusable research. However, different platforms use different data models and formats, which drastically complicates the identification of relevant datasets, their interpretation, and their interoperability. Therefore, a semantically rich, ontology-based, machine-readable data model that can be used by different platforms is highly desirable. In this paper, we report on the development of such an ontology, which we call OPTION (OPTImization algorithm benchmarking ONtology). Our ontology provides the vocabulary needed for semantic annotation of the core entities involved in the benchmarking process, such as algorithms, problems, and evaluation measures. It also provides means for automatic data integration, improved interoperability, and powerful querying capabilities, thereby increasing the value of the benchmarking data. We demonstrate the utility of OPTION, by annotating and querying a corpus of benchmark performance data from the BBOB collection of the COCO framework and from the Yet Another Black-Box Optimization Benchmark (YABBOB) family of the Nevergrad environment. In addition, we integrate features of the BBOB functional performance landscape into the OPTION knowledge base using publicly available datasets with exploratory landscape analysis. Finally, we integrate the OPTION knowledge base into the IOHprofiler environment and provide users with the ability to perform meta-analysis of performance data

    Supporting Experimentation via an Evaluation Infrastructure for Semantic Technologies

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    One of the challenges of the Future Internet is to manage and combine information about dierent digital and real-world entities and the characteristics of these entities, covering related issues such as the trust or provenance of this information. One way to allow an eective representation and integration of this information is to use semantic technologies to correctly manage not just these heterogeneous content and data but also their associated metadata

    Benchmarking Data Exchange among Semantic-Web Ontologies

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    The increasing popularity of the Web of Data is motivating the need to integrate semantic-web ontologies. Data exchange is one integration approach that aims to populate a target ontology using data that come from one or more source ontologies. Currently, there exist a variety of systems that are suitable to perform data exchange among these ontologies; unfortunately, they have uneven performance, which makes it appealing assessing and ranking them from an empirical point of view. In the bibliography, there exist a number of benchmarks, but they cannot be applied to this context because they are not suitable for testing semantic-web ontologies or they do not focus on data exchange problems. In this paper, we present MostoBM, a benchmark for testing data exchange systems in the context of such ontologies. It provides a catalogue of three real-world and seven synthetic data exchange patterns, which can be instantiated into a variety of scenarios using some parameters. These scenarios help to analyze how the performance of data exchange systems evolves as the exchanging ontologies are scaled in structured and/or data. Finally, we provide an evaluation methodology to compare data exchange systems side by side and to make informed and statistically sound decisions regarding: 1) which data exchange system performs better; and 2) how the performance of a system is influenced by the parameters of our benchmark.Ministerio de Educación y Ciencia TIN2007-64119Junta de Andalucía P07-TIC-2602Junta de Andalucía P08- TIC-4100Ministerio de Ciencia e Innovación TIN2008-04718-EMinisterio de Ciencia e Innovación TIN2010-21744Ministerio de Ciencia e Innovación TIN2010-09809-EMinisterio de Ciencia e Innovación TIN2010-10811-EMinisterio de Ciencia e Innovación TIN2010-09988-EMinisterio de Economía y Competitividad TIN2011-15497-
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