Matches in ScholarlyData for { ?s ?p ?o. }
- paper-14 abstract "Research in Systems Biology involves integrating data and knowledge about the dynamic processes in biological systems in order to understand and model them. Semantic web technologies should be ideal for exploring the complex networks of genes, proteins and metabolites that interact, but much of this data is not natively available to the semantic web. Data is typically collected and stored with free-text annotations in spreadsheets, many of which do not conform to existing metadata standards and are often not publica lly released. Along with initiatives to promote more data sharing, one of the main challenges is therefore to semantically annotate and extract this data so that it is available to the research community. Data annotation and curation are expensive and undervalued tasks that have enormous benefits to the discipline as a whole, but fewer benefits to the individual data producers. By embedding semantic annotation into spreadsheets, however, and automat ically extracting this data into RDF at the time of repository submission, the process of producing standards-compliant data, that is available for semantic web querying, can be achieved without adding additional overheads to laboratory data management. This paper describes these strategies in the context of semantic data management in the SEEK. The SEEK is a web-based resource for sharing and exchanging Systems Biology data and models that is underpinned by the JERM ontology (Just Enough Results Model), which describes the relationships between data, models, protocols and experiments. The SEEK was originally developed for SysMO, a large European Systems Biology consortium studying micro-organisms, but it has since had widespread adoption across European Systems Biology.".
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- paper-14 title "Semantic Data and Models Sharing in systems Biology: The Just Enough Results Model and the SEEK Platform".
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- item-1 hasContent katherine-wolstencroft.
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- item-2 hasContent stuart-owen.
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- item-7 hasContent carole-goble.
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- paper-15 type InProceedings.
- paper-15 label "Reasoning on crowd-sourced semantic annotations to facilitate cataloguing of 3D artefacts in the cultural heritage domain".
- paper-15 sameAs paper-15.
- paper-15 abstract "The 3D Semantic Annotation (3DSA) system expedites the classification of 3D digital surrogates from the cultural heritage domain, by leveraging crowd-sourced semantic annotations. More specifically, the 3DSA system generates high-level classifications of 3D ob jects by applying rule-based reasoning across community-generated annotations and low-level shape and size attributes. This paper describes a particular use of the 3DSA system – cataloguing Greek pottery. It also describes our novel approach to rule-based reasoning that is modelled on concepts inspired from Markov logic networks. Our evaluation of this approach demonstrates its efficiency, accuracy and versatility, compared to classical rule-based reasoning.".
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- paper-15 title "Reasoning on crowd-sourced semantic annotations to facilitate cataloguing of 3D artefacts in the cultural heritage domain".
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- item-1 hasContent chih-hao-yu.
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- item-2 hasContent tudor-groza.
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- paper-16 type InProceedings.
- paper-16 label "Using Linked Data to evaluate the impact of Research and Development in Europe: a Structural Equation Model".
- paper-16 sameAs paper-16.
- paper-16 abstract "Europe has a high impact on the global biomedical literature, having contributed with a growing number of research articles and a significant citation impact. However, the impact of research and development generated by European countries on economic, educational and healthcare performance is poorly understood. The recent Linking Open Data (LOD) project has made a lot of data sources publicly available and in human-readable formats. In this paper, we demonstrate the utility of LOD in assessing the impact of Research and Development (R&D) on the economic, education and healthcare performance in Europe. We extract relevant variables from two LOD datasets, namely World Bank and Eurostat. We analyze the data for 20 out of the 27 European countries over a span of 10 years (1999 to 2009). We use a Structural Equation Modeling (SEM) approach to quantify the impact of R&D on the different measures. We perform different exploratory and confirmatory factorial analysis evaluations which gives rise to four latent variables that are included in the model: (i) Research and Development (R&D), (ii) Economic Performance (EcoP), (iii) Educational Performance (EduP), (iv) Healthcare performance (HcareP) of the European countries. Our results indicate the importance of R&D to the overall development of the European educational and healthcare performance (directly) and economic performance (indirectly). The results also shows the practical applicability of LOD to estimate this impact.".
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- paper-16 title "Using Linked Data to evaluate the impact of Research and Development in Europe: a Structural Equation Model".
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- item-1 hasContent amrapali-zaveri.
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- item-2 hasContent joao-ricardo-nickenig-vissoci.
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- paper-17 type InProceedings.
- paper-17 label "Crowdsourcing Linked Data Quality Assessment".
- paper-17 sameAs paper-17.
- paper-17 abstract "In this paper we look into the use of crowdsourcing as a means to handle Linked Data quality problems that are challenging to be solved automatically. We analyzed the most common errors encountered in Linked Data sources and classified them according to the extent to which they are likely to be amenable to a specific form of crowdsourcing. Based on this analysis, we implemented a quality assessment methodology for Linked Data that leverages the wisdom of the crowds in different ways: (i) a contest targeting an expert crowd of researchers and Linked Data enthusiasts; complemented by (ii) paid microtasks published on Amazon Mechanical Turk. We empirically evaluated how this methodology could efficiently spot quality issues in DBpedia. We also investigated how the contributions of the two types of crowds could be optimally integrated into Linked Data curation processes. The results show that the two styles of crowdsourcing are complementary and that crowdsourcing-enabled quality assessment is a promising and affordable way to enhance the quality of Linked Data.".
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- paper-17 title "Crowdsourcing Linked Data Quality Assessment".
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