D-h-index: a linked data quality metric

2021 ◽  
Author(s):  
Konstantinos Kotis
Keyword(s):  
Data Quality ◽  
Linked Data ◽  
H Index ◽  
Quality Metric

Web Data Quality

2014 ◽  
Vol 10 (2) ◽  
pp. 1-6 ◽  
Author(s):  
Amrapali Zaveri ◽  
Andrea Maurino ◽  
Laure-Berti Equille
Keyword(s):  
Semantic Web ◽  
Data Quality ◽  
Linked Data ◽  
Web Data ◽  
Semantic Web Technologies ◽  
Future Directions ◽  
Web Technologies ◽  
Current State ◽  
Web Of Data ◽  
The Web

The standardization and adoption of Semantic Web technologies has resulted in an unprecedented volume of data being published as Linked Data (LD). However, the “publish first, refine later” philosophy leads to various quality problems arising in the underlying data such as incompleteness, inconsistency and semantic ambiguities. In this article, we describe the current state of Data Quality in the Web of Data along with details of the three papers accepted for the International Journal on Semantic Web and Information Systems' (IJSWIS) Special Issue on Web Data Quality. Additionally, we identify new challenges that are specific to the Web of Data and provide insights into the current progress and future directions for each of those challenges.

Towards An Objective Assessment Framework for Linked Data Quality

2016 ◽  
Vol 12 (3) ◽  
pp. 111-133 ◽  
Author(s):  
Ahmad Assaf ◽  
Aline Senart ◽  
Raphaël Troncy
Keyword(s):  
Data Quality ◽  
Linked Data ◽  
Objective Assessment ◽  
Open Data ◽  
Quality Measurement ◽  
Measurement Tool ◽  
Assessment Framework ◽  
Complex Process ◽  
Structured Information

Ensuring data quality in Linked Open Data is a complex process as it consists of structured information supported by models, ontologies and vocabularies and contains queryable endpoints and links. In this paper, the authors first propose an objective assessment framework for Linked Data quality. The authors build upon previous efforts that have identified potential quality issues but focus only on objective quality indicators that can measured regardless on the underlying use case. Secondly, the authors present an extensible quality measurement tool that helps on one hand data owners to rate the quality of their datasets, and on the other hand data consumers to choose their data sources from a ranked set. The authors evaluate this tool by measuring the quality of the LOD cloud. The results demonstrate that the general state of the datasets needs attention as they mostly have low completeness, provenance, licensing and comprehensibility quality scores.

scholarly journals The Spatial-Comprehensiveness (S-COM) Index: Identifying Optimal Spatial Extents in Volunteered Geographic Information Point Datasets

2020 ◽  
Vol 9 (9) ◽  
pp. 497
Author(s):  
Haydn Lawrence ◽  
Colin Robertson ◽  
Rob Feick ◽  
Trisalyn Nelson
Keyword(s):  
Social Media ◽  
Data Quality ◽  
Spatial Scales ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Quality Metric ◽  
Fine Grained ◽  
Multi Scale ◽  
Feasible Study

Social media and other forms of volunteered geographic information (VGI) are used frequently as a source of fine-grained big data for research. While employing geographically referenced social media data for a wide array of purposes has become commonplace, the relevant scales over which these data apply to is typically unknown. For researchers to use VGI appropriately (e.g., aggregated to areal units (e.g., neighbourhoods) to elicit key trend or demographic information), general methods for assessing the quality are required, particularly, the explicit linkage of data quality and relevant spatial scales, as there are no accepted standards or sampling controls. We present a data quality metric, the Spatial-comprehensiveness Index (S-COM), which can delineate feasible study areas or spatial extents based on the quality of uneven and dynamic geographically referenced VGI. This scale-sensitive approach to analyzing VGI is demonstrated over different grains with data from two citizen science initiatives. The S-COM index can be used both to assess feasible study extents based on coverage, user-heterogeneity, and density and to find feasible sub-study areas from a larger, indefinite area. The results identified sub-study areas of VGI for focused analysis, allowing for a larger adoption of a similar methodology in multi-scale analyses of VGI.

Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO

Semantic Web ◽  
2017 ◽  
Vol 9 (1) ◽  
pp. 77-129 ◽  
Author(s):  
Michael Färber ◽  
Frederic Bartscherer ◽  
Carsten Menne ◽  
Achim Rettinger
Keyword(s):  
Data Quality ◽  
Linked Data

Detecting Linked Data quality issues via crowdsourcing: A DBpedia study

Semantic Web ◽  
2018 ◽  
Vol 9 (3) ◽  
pp. 303-335 ◽  
Author(s):  
Maribel Acosta ◽  
Amrapali Zaveri ◽  
Elena Simperl ◽  
Dimitris Kontokostas ◽  
Fabian Flöck ◽  
...  
Keyword(s):  
Data Quality ◽  
Linked Data ◽  
Quality Issues

Fall-related sub-acute and non-acute care and hospitalised rehabilitation episodes of care: what is the injury burden?

2013 ◽  
Vol 37 (3) ◽  
pp. 348 ◽  
Author(s):  
Rebecca J. Mitchell ◽  
Jacqui Close ◽  
Ian D. Cameron ◽  
Stephen Lord
Keyword(s):  
Data Quality ◽  
Acute Care ◽  
Resource Use ◽  
Linked Data ◽  
Hospital Admissions ◽  
Temporal Trends ◽  
Inpatient Rehabilitation ◽  
Rehabilitation Services ◽  
Inpatient Hospital ◽  
Epidemiological Profile

Background Falls are the leading cause of injury in older people. Rehabilitation services can assist individuals to improve mobility and function after sustaining a fall-related injury. However, the true effect of fall-related injury resulting in hospitalisation is often underestimated because of failure to consider sub-acute and non-acute care provided following the acute hospitalisation episode. Aim This study aims to describe the sub-acute and non-acute health service use of individuals hospitalised in New South Wales (NSW), Australia for a fall-related injury during 2000–01 to 2008–09, to examine the burden of fall-related inpatient rehabilitation hospital admissions from 1998–99 to 2010–11 and to estimate future demand for fall-related inpatient rehabilitation admissions in NSW to 2020. Method Retrospective review of sub-acute and non-acute records linked to hospital admission records during 2001–02 to 2008–09 in NSW. Analysis of temporal trends from 1998–99 to 2010–11 and projections to 2020 for rehabilitation-related (ICD-10-AM: Z47, Z48, Z50, Z75.1) inpatient hospital admissions. Results There were 4317 individuals with a fall-related injury admitted to hospital and subsequently admitted for sub-acute and non-acute care; 84% of these were aged 65+ years; 70.4% were female and 27.2% had femur fractures. For the rehabilitation-related admissions, total mean functional independence measure (FIM) scores improved significantly (from 78.4 to 94.6; P < 0.0001) between admission and discharge. Fall-related inpatient rehabilitation episodes increased by 9.1% each year between 1998 and 2011 for individuals aged 65 years and older and are projected to rise to 50 000 admissions annually by 2020. Conclusion This is the first study to provide an epidemiological profile of individuals using sub-acute and non-acute care in NSW using linked data. Improvements in data validity and reliability would enhance the quality of the sub-acute and non-acute care data and its ability to be used to inform resource use in this sector. The examination of temporal trends using only the inpatient hospital admissions provides a guide for resource implications for inpatient rehabilitation services. What is known about this topic? Fall-related injuries that result in inpatient hospital admissions are increasing in Australia. However, the extent of the effect of fall-related injuries in the sub-acute and non-acute sector remains unknown, due to data limitations. What does this paper add? Provides the first epidemiological profile of individuals who fall and go on to use sub-acute and non-acute care in NSW using linked data. It highlights where improvements in data quality in the sub-acute and non-acute care data could be made to improve their usefulness to inform resource use in this sector. What are the implications for clinicians? Fall injury prevention and healthy ageing strategies for older individuals remain a priority for clinicians. The current and projected future resource implications for inpatient rehabilitation and follow-up services provide an indication for clinicians of future demand in this area as the population ages. However, data quality needs to improve to provide clinicians with strongly relevant guidance to inform clinical practice.

Luzzu—A Methodology and Framework for Linked Data Quality Assessment

2016 ◽  
Vol 8 (1) ◽  
pp. 1-32 ◽  
Author(s):  
Jeremy Debattista ◽  
SÖren Auer ◽  
Christoph Lange
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Linked Data ◽  
Data Quality Assessment

A data quality metric (DQM)

2017 ◽  
Vol 10 (10) ◽  
pp. 1094-1105 ◽  
Author(s):  
Yeounoh Chung ◽  
Sanjay Krishnan ◽  
Tim Kraska
Keyword(s):  
Data Quality ◽  
Quality Metric

ODF: An Efficient OWL-Based Linked Course Data Generating Framework

2014 ◽  
Vol 886 ◽  
pp. 613-616
Author(s):  
Shan Gao
Keyword(s):  
Data Quality ◽  
Linked Data ◽  
Semantic Data ◽  
Complex Knowledge ◽  
The Impact

Although the intention of OWL is to provide an open, minimally constraining way for representing to represent rich and complex knowledge about things, there exists an increasing demands for the efficiency of course data generating. Addressing this issue, we present the ODF: a new OWL-based Linked Course Data generating framework, which makes it possible to specify semantic data directly. Generating such data directly does not only help in maintaining course data quality, but also opens up new optimization opportunities for link sources and, most importantly, makes generating process easier for users and system developers. We present OWL-based Linked Course Data generating framework and discuss the impact on Linked Data.

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