Investigating the Relevance of Linked Open Data Sets with SPARQL Queries

Holst, Thomas; Höfig, Edzard

doi:10.1109/compsacw.2013.31

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…Each part of the RDF triple has a unique identifier Uniform Resource Identifiers (URIs). The RDF triples can be processed using the SPARQL language (Holst and H€ ofig, 2013).…”

Section: Preliminariesmentioning

confidence: 99%

OpenRank – a novel approach to rank universities using objective and publicly verifiable data sources

Qureshi

Daud

Hayat

et al. 2021

LHT

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PurposeAcademic rankings are facing various issues, including the use of data sources that are not publicly verifiable, subjective parameters, a narrow focus on research productivity and regional biases and so forth. This research work is intended to enhance creditability of the ranking process by using the objective indicators based on publicly verifiable data sources.Design/methodology/approachThe proposed ranking methodology – OpenRank – drives the objective indicators from two well-known publicly verifiable data repositories: the ArnetMiner and DBpedia.FindingsThe resultant academic ranking reflects common tendencies of the international academic rankings published by the Shanghai Ranking Consultancy (SRC), Quacquarelli Symonds (QS) and Times Higher Education (THE). Evaluation of the proposed methodology advocates its effectiveness and quick reproducibility with low cost of data collection.Research limitations/implicationsImplementation of the OpenRank methodology faced the issue of availability of the quality data. In future, accuracy of the academic rankings can be improved further by employing more relevant public data sources like the Microsoft Academic Graph, millions of graduate's profiles available in the LinkedIn repositories and the bibliographic data maintained by Association for Computing Machinery and Scopus and so forth.Practical implicationsThe suggested use of open data sources would offer new dimensions to evaluate academic performance of the higher education institutions (HEIs) and having comprehensive understanding of the catalyst factors in the higher education.Social implicationsThe research work highlighted the need of a purposely built, publicly verifiable electronic data source for performance evaluation of the global HEIs. Availability of such a global database would help in better academic planning, monitoring and analysis. Definitely, more transparent, reliable and less controversial academic rankings can be generated by employing the aspired data source.Originality/valueWe suggested a satisfying solution for improvement of the HEIs' ranking process by making the following contributions: (1) enhancing creditability of the ranking results by merely employing the objective performance indicators extracted from the publicly verifiable data sources, (2) developing an academic ranking methodology based on the objective indicators using two well-known data repositories, the DBpedia and ArnetMiner and (3) demonstrating effectiveness of the proposed ranking methodology on the real data sources.

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“…Each part of the RDF triple has a unique identifier Uniform Resource Identifiers (URIs). The RDF triples can be processed using the SPARQL language (Holst and H€ ofig, 2013).…”

Section: Preliminariesmentioning

confidence: 99%

OpenRank – a novel approach to rank universities using objective and publicly verifiable data sources

Qureshi

Daud

Hayat

et al. 2021

LHT

View full text Add to dashboard Cite

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“…Langegger & Wöß propose RDFStats (Langegger & Wöß, 2009), which uses a pipeline of SPARQL (1.0) queries to generate a histogram on a per-class basis, representing the predicates and types of values associated with its instances. Holst & Höfig (Holst & Höfig, 2013) propose the use of SPARQL 1.1 queries to discover specific aspects of an RDF dataset, but the authors do not consider VoID and only run local experiments over three datasets. Mountantonakis et al (Mountantonakis et al, 2014) propose a set of SPARQL 1.1 queries that can compute the connectivity metrics with which they extend VoID.…”

Section: Describing/summarising Rdf Datasetsmentioning

confidence: 99%

Sportal

Hasnain

Mehmood

Zainab

et al. 2016

International Journal on Semantic Web and Information Systems

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Access to hundreds of knowledge bases has been made available on the Web through public SPARQL endpoints. Unfortunately, few endpoints publish descriptions of their content (e.g., using VoID). It is thus unclear how agents can learn about the content of a given SPARQL endpoint or, relatedly, find SPARQL endpoints with content relevant to their needs. In this paper, the authors investigate the feasibility of a system that gathers information about public SPARQL endpoints by querying them directly about their own content. With the advent of SPARQL 1.1 and features such as aggregates, it is now possible to specify queries whose results would form a detailed profile of the content of the endpoint, comparable with a large subset of VoID. In theory it would thus be feasible to build a rich centralised catalogue describing the content indexed by individual endpoints by issuing them SPARQL (1.1) queries; this catalogue could then be searched and queried by agents looking for endpoints with content they are interested in. In practice, however, the coverage of the catalogue is bounded by the limitations of public endpoints themselves: some may not support SPARQL 1.1, some may return partial responses, some may throw exceptions for expensive aggregate queries, etc. The authors' goal in this paper is thus twofold: (i) using VoID as a bar, to empirically investigate the extent to which public endpoints can describe their own content, and (ii) to build and analyse the capabilities of a best-effort online catalogue of current endpoints based on the (partial) results collected.

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“…ROS algorithms do not rely on any model or statistical information, but by virtue of estimating the size of the result set it can determine the cost of connection, and then select the minimum cost as the join order. Therefore, you need to determine a method of estimating the operation result sets in the connection diagram [10] . That is the method based on the sampling.…”

Section: Query Optimizationmentioning

confidence: 99%

Research on Efficient SPARQL Query Processing for RDF Data

Zhang¹

2015

Proceedings of the 2015 2nd International Workshop on Materials Engineering and Computer Sciences

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Semantic Web can add explicit semantics to data so that machines can not only simply display these data, but also the machines can understand, process and even integrate them. In recent years, with the comprehensive development of the Linking Open Data (LOD)project and the DBpedia project, Semantic Web data sources increase significantly and a large number of graph data in form of RDF data model are published. The Web is rapidly changing from the one containing Web pages and hyper-links only (called Document Web) to a Data Web with abundant entities and rich relationships between these entities. Based on these semantic data, lots of large search engine companies like Google are building knowledge graph to improve the quality of search, which indicates that we enter into the era of semantic search.

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Investigating the Relevance of Linked Open Data Sets with SPARQL Queries

Cited by 7 publications

References 4 publications

OpenRank – a novel approach to rank universities using objective and publicly verifiable data sources

OpenRank – a novel approach to rank universities using objective and publicly verifiable data sources

Sportal

Research on Efficient SPARQL Query Processing for RDF Data

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