Using SPARQL and SPIN for Data Quality Management on the Semantic Web

Fürber, Christian; Hepp, Martin

doi:10.1007/978-3-642-12814-1_4

Cited by 69 publications

(54 citation statements)

References 9 publications

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“…Other metrics of linked data quality include coherence of links to other resources or consistency with regard to implicit information [21]. Recent studies pointed out problems with linked data quality concerning non-standardized and variable representation, inconsistency, and lack of interoperability [18,25]. One reason that linked data is not used as much as it could is the high instance of data errors including syntactically erroneous and repetitious data [13].…”

Section: Improving Geospatial Linked Data Trustworthiness and Qualitymentioning

confidence: 99%

Enriching and improving the quality of linked data with GIS

et al. 2016

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Standardization of methods for data exchange in GIS has a long history predating the creation of World Wide Web. The advent of World Wide Web brought the emergence of new solutions for data exchange and sharing including; more recently, standards proposed by the W3C for data exchange involving Semantic Web technologies and linked data. Despite the growing interest in integration, GIS and linked data are still two separate paradigms for describing and publishing spatial data on the Web. At the same time, both paradigms offer complementary ways of representing real world phenomena and means of analysis using different processing functions. The complementarity of linked data and GIS can be leveraged to synergize both paradigms resulting in richer data content and more powerful inferencing. The article presents an approach aimed at integrating linked data with GIS. The approach relies on the use of GIS tools for integration, verification and enrichment of linked data. The GIS tools are employed to enrich linked data by furnishing access to collection of data resources, defining relationship between data resources, and subsequently facilitating GIS data integration with linked data. The proposed approach is demonstrated with examples using data from DBpedia, OSM, and tools developed by the authors for standard GIS software.

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Section: Improving Geospatial Linked Data Trustworthiness and Qualitymentioning

confidence: 99%

Enriching and improving the quality of linked data with GIS

et al. 2016

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“…Because we aim at automated tools for data quality management and because the context of data consumption is often not immediately available on the Web of Data, we adopt the technical perspective on data quality to develop algorithms for the identification of data quality problems in knowledge bases. For a summary of instance-related data quality problems found in single-source scenarios, we refer to our previous work published in [11].…”

Section: Data Quality Management On the Semantic Webmentioning

confidence: 99%

“…In [11], we defined a query that identifies datatype properties that have missing literal values for a single, known datatype property. In this paper, we (1) extend this query by enabling the additional detection of missing (but required) datatype properties attached to instances of particular classes, and (2) define a query for the identification of literals that are missing on the basis of a functional dependency, e.g.…”

Section: Identification Of Missing Literalsmentioning

confidence: 99%

“…In [11], we proposed an algorithm for the identification of functional dependency violations, which required the manual definition of legal combinations of literal values of two dependent datatype properties. Similar to false values it is also possible to check functional dependent literal values against a trusted knowledge base that already contains information on these dependencies.…”

Section: Identification Of Functional Dependency Violationsmentioning

confidence: 99%

“…One possible solution approach to this problem is the utilization of data quality problem identification techniques, e.g. as presented in [11], on the trusted source itself before starting the use of Semantic Web resources as a trusted reference for quality checks on local knowledge bases.…”

Section: Limitationsmentioning

confidence: 99%

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Using Semantic Web Resources for Data Quality Management

Fürber¹,

Hepp²

2010

Knowledge Engineering and Management by the Masses

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Abstract. The quality of data is a critical factor for all kinds of decision-making and transaction processing. While there has been a lot of research on data quality in the past two decades, the topic has not yet received sufficient attention from the Semantic Web community. In this paper, we discuss (1) the data quality issues related to the growing amount of data available on the Semantic Web, (2) how data quality problems can be handled within the Semantic Web technology framework, namely using SPARQL on RDF representations, and (3) how Semantic Web reference data, e.g. from DBPedia, can be used to spot incorrect literal values and functional dependency violations. We show how this approach can be used for data quality management of public Semantic Web data and data stored in relational databases in closed settings alike. As part of our work, we developed generic SPARQL queries to identify (1) missing datatype properties or literal values, (2) illegal values, and (3) functional dependency violations. We argue that using Semantic Web datasets reduces the effort for data quality management substantially. As a use-case, we employ Geonames, a publicly available Semantic Web resource for geographical data, as a trusted reference for managing the quality of other data sources.

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Semantic data mining in the information age: A systematic review

Chanmee

Kesorn

2021

Int J Intell Syst

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Data mining is the discovery of meaningful information or unrevealed patterns in data. Traditional data‐mining approaches, using statistical calculations, machine learning, artificial intelligence, and database technology, cannot interpret data on a conceptual or semantic level and fail to reveal the meanings within the data. This results in a user not being analyzed and determines its signification and implications. Several semantic data‐mining approaches have been proposed in the past decade that overcome these limitations by using a domain ontology as background knowledge to enable and enhance data‐mining performance. The main contributions of this literature survey include organizing the surveyed articles in a new way that provides ease of understanding for interested researchers, and the provision of a critical analysis and summary of the surveyed articles, identifying the contribution of these papers to the field, and the limitations of the analysis methods and approaches discussed in this corpus, with the intention of informing researchers in this growing field in their innovative approaches to new research. Finally, we identify the future trends and challenges in this study track that will be of concern to future researchers, such as dynamic knowledge‐based methods or big‐data tool collaboration. This survey article provides a comprehensive overview of the literature on domain ontologies as used in the various semantic data‐mining tasks, such as preprocessing, modeling, and postprocessing. We investigated the role of semantic data mining in the field of data science and the processes and methods of applying semantic data mining to a data resource description framework.

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Using SPARQL and SPIN for Data Quality Management on the Semantic Web

Cited by 69 publications

References 9 publications

Enriching and improving the quality of linked data with GIS

Enriching and improving the quality of linked data with GIS

Using Semantic Web Resources for Data Quality Management

Semantic data mining in the information age: A systematic review

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