Context Of Linked Data Research Articles

Improving reproducibility and validity in measures of chronic disease incidence using large-scale linked data in Australia.

Objective and ApproachOur study aimed to improve methods for identifying incident chronic kidney disease (CKD), cardiovascular disease (CVD), and diabetes in a young cohort from the Antecedents of Renal Disease in Aboriginal Children (ARDAC) study. We linked 20 state and federal administrative health datasets, including hospital records, Medicare claims, pharmaceutical data, and the National Diabetes Services Scheme (NDSS) data for the first time. This approach facilitated the development of new algorithms for disease detection. Notably, we will make the R code for these algorithms publicly available. ResultsThe analysis, involving 3,758 ARDAC study participants, created clinically robust definitions of CKD, CVD, and diabetes (by subtype) in the Australian linked data context; and revealed that all other data sources inaccurately estimated the incidence of diabetes compared to the NDSS data. ConclusionsThis key finding illustrates the discrepancy in diabetes incidence estimates and highlights the value of integrating multiple data sources for the investigation of chronic disease. ImplicationsHundreds of published studies use data linkage to investigate CVD, CKD, and diabetes in the Australian context. Our novel robust case definitions, and the identified poor estimation of diabetes incidence among young people, underscores the need to critically evaluate linked data sources used to inform health policy and research. By sharing the developed R code, we support ongoing efforts to improve transparency and replicability in health research. Our study refines chronic disease definition methods and contributes to more effective public health strategies, with particular focus on improving outcomes for Indigenous communities.

Term Circles: Using Linked Data as a Tool to Mitigate Colonial Subject Bias

This paper considers the Canadian Federation of Library Associations (CFLA) recommendation to “decolonize library access and classification” and begins by exploring the difficulty involved when addressing this recommendation working from within a colonial institution that represents a colonial worldview. It compares general characteristics of Western and Indigenous worldviews and considers the affect that these perspectives have on the organization of knowledge and information especially in relation to a controlled subject vocabulary like the Library of Congress Subject Headings (LCSH). It presents the “term circle” as a way to mitigate colonial biases by creating a hybrid subject language system using SKOS RDF/XML in a linked data context. This hybrid system facilitates Indigenous community participation in the control and development of subject metadata and suggests a new role for library metadata.

Classification on the Web: an analysis of Dewey Linked Data

With the availability of information in the World Wide Web its access and retrieval by the users is facilitated, and the Library and Information Science (LIS) field’s knowledge and techniques can be applied to this environment in order to help with the process. The present study is descriptive, qualitative and exploratory, based on bibliographical sources, in which it was explored how the Classification discipline interacts with Linked Data, focusing on the analysis of Dewey Linked Data. From four catalogs analyzed, referred to in the literature as adhering to Dewey Linked Data, only two actually has links in their records redirecting to the system. Despite this, its presence in The Linked Open Data Cloud appears as a positive factor in its dissemination, since it boosts its visibility. It is concluded that the Classification discipline allows the thematic standardization of information resources, so that there is uniformity in the Web environment and quality retrieval of information, while promoting interoperability between data in the Linked Data context. The standardization of metadata values using classifications optimizes the representation of information and its retrieval in the Web, while also providing the reuse of data. In addition, studies that align the area of Library and Information Science with the Semantic Web and its technologies can provide new perspectives for the area, as well as contemplate the users’ always changing needs, thus, fulfilling the objective of the field.

Modeling and Simulation of Nanofluid Flow Problems

Linked Data (LD) is a well-established standard for publishing and managing structured information on the Web, gathering and bridging together knowledge from different scientific and commercial domains. The development of Linked Data Visualization techniques and tools has been followed as the primary means for the analysis of this vast amount of information by data scientists, domain experts, business users, and citizens. This book covers a wide spectrum of visualization issues, providing an overview of the recent advances in this area, focusing on techniques, tools, and use cases of visualization and visual analysis of LD. It presents the basic concepts related to data visualization and the LD technologies, the techniques employed for data visualization based on the characteristics of data techniques for Big Data visualization, use tools and use cases in the LD context, and finally a thorough assessment of the usability of these tools under different scenarios. The purpose of this book is to offer a complete guide to the evolution of LD visualization for interested readers from any background and to empower them to get started with the visual analysis of such data. This book can serve as a course textbook or a primer for all those interested in LD and data visualization.

Including widespread geometry schemas into Linked Data-based BIM applied to built heritage

A reliable data exchange – often including geometry-related data – between stakeholders is crucial in construction projects. In this regard, data exchange frameworks built on Linked Data principles are very promising for combining disparate data sets. However, existing proposals to combine geometry and Linked Data either demand dedicated applications or support only a limited number of common geometry schemas. If any existing geometry schema could be used in a Linked Data context, error-prone geometry conversions are avoided and stakeholders do not need to invest in new geometry engines. In this paper, the applicability of Resource Description Framework (RDF) literals for including a wide variety of existing geometry schemas is studied and applied in a built heritage context. The uniform linking pattern and related terminology of the Ontology for Managing Geometry are used to implement this approach. Subsequently, the File Ontology for Geometry formats and Geometry Metadata Ontology are developed to ease the reuse of linked geometry descriptions. The effectiveness of the entire data structure is demonstrated in a built heritage case study project. The receiving party is able to create successfully a coordinated view – using a demo web application – on shared, but disparate, RDF data sets containing geometry descriptions.

Ontologies in the Time of Linked Data

This paper discusses some of the methodological issues one encounters when creating and using ontologies in the rapidly expanding Linked Open Data (LOD) landscape. Over the years the notion of applied ontologies has transitioned from that of a logically formalized knowledge system with varying degrees of inferencing power to that of a lightweight knowledge representation tool. This shift is reflected in the current lexicon where different actors in the LOD community use the term ontology interchangeably with more generic terms like vocabulary or even namespace or data schema. Applied ontologies have been a key area of research in the context of Semantic Web initiative since the late 1990s. The Semantic Web has recently found a new stream of development in the Linked Data initiative, which is considered its natural evolution (Allemang and Hendler, 2011). While a good deal of literature has been devoted to investigating ontology engineering for the Semantic Web, not enough attention has yet been paid to understanding the nature and role that ontologies play in the linked data context, especially from the lens of knowledge organization research. Based on our ongoing work creating Linked Open Data applications and services for digital resources in the domain of the performing arts, we describe methodological steps and lessons learned in line with the spirit of the linked data initiative, where an agile and pragmatic approach to development is combined with the practice of learning from one another.

An assessment of open relation extraction systems for the semantic web

Open relation extraction has been a growing field of research in the last few years. This paper compares some of the most prominent open relation extractors and explores their strength and weaknesses on standard datasets. In particular, we highlight the lack of formal guidelines that define a valid relation and state that open relation extractors must be situated in particular tasks and contexts. In this respect, we briefly analyze the role of open relation extractors for the semantic Web and linked data context.

Linked Open Ontology cloud: managing a system of interlinked cross-domain lightweight ontologies

Traditionally, the structure of the controlled vocabularies used for annotation can be utilised for reasoning for information retrieval. However, this can be problematic when applied in the Linked Data context. Linked Data typically comes from different organisations and domains with mutually incompatible vocabularies without explicit links between them resulting in data silos. This paper argues that to solve the problem one has to transform the annotation vocabularies into a Linked Open Ontology cloud. We present a method for transforming a set of legacy thesauri into a cloud of interlinked ontologies while ensuring the validity of the transitive subclass relations and the means for maintaining the system when component ontologies are updated. Our approach has been used and evaluated in practice building a cloud called KOKO of 16 ontologies, with a total of 47,000 concepts. KOKO has been published as an ontology service and is in use in various organisations for both data indexing and semantic search.