Assessing Trust with PageRank in the Web of Data

Giménez-García, José M.; Thakkar, Harsh; Zimmermann, Antoine

doi:10.1007/978-3-319-47602-5_45

Cited by 11 publications

(16 citation statements)

References 24 publications

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“…In [24], Giménez-García et al focus on dataset reuse to assess the trust of Linked Datasets. The authors use LOD Laundromat data dumps in order to compute PageRank values on datasets and rank these datasets based on their trustworthiness value.…”

Section: Studying the Quality Of The Data On The Webmentioning

confidence: 99%

“…Zaveri et al [60] categorised a number of metrics in this category within the four dimensions Representational Conciseness, Interoperability, Interpretability and Versatility. In Table 3 we list the metrics that are assessed in this category, together with a summary of assessment results 24 showing the mean value (µ), median value (Q 2 ), and standard deviation (σ s ).…”

Section: Representational Categorymentioning

confidence: 99%

“…This metric focuses on the simplicity aspect of this definition, where by simplicity the editors 24 Table 3 List of metrics assessed in the Representational Category together with the assessed mean value (µ), median value (Q 2 ), and standard deviation (σs) of the same document mean that having short and mnemonic URIs are easier for humans to remember (e.g. http://danbri.org/foaf vs. https://w3id.org/lodquator/ resource/826514e9-e34a-40a2-bc8d-9e6b8bd54770), whilst serving the purpose of being machine processable.…”

Section: (Rc1) Keeping Uris Shortmentioning

confidence: 99%

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Evaluating the quality of the LOD cloud: An empirical investigation

Debattista

Lange

Auer

et al. 2018

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The increasing adoption of the Linked Data principles brought with it an unprecedented dimension to the Web, transforming the traditional Web of Documents to a vibrant information ecosystem, also known as the Web of Data. This transformation, however, does not come without any pain points. Similar to the Web of Documents, the Web of Data is heterogenous in terms of the various domains it covers. The diversity of the Web of Data is also reflected in its quality. Data quality impacts the fitness for use of the data for the application at hand, and choosing the right dataset is often a challenge for data consumers. In this quantitative empirical survey, we analyse 130 datasets (≈ 3.7 billion quads), extracted from the latest Linked Open Data Cloud using 27 Linked Data quality metrics, and provide insights into the current quality conformance. Furthermore, we publish the quality metadata for each assessed dataset as Linked Data, using the Dataset Quality Vocabulary (daQ). This metadata is then used by data consumers to search and filter possible datasets based on different quality criteria. Thereafter, based on our empirical study, we present an aggregated view of the Linked Data quality in general. Finally, using the results obtained from the quality assessment empirical study, we use the Principal Component Analysis (PCA) test in order to identify the key quality indicators that can give us sufficient information about a dataset's quality.

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Section: Studying the Quality Of The Data On The Webmentioning

confidence: 99%

Section: Representational Categorymentioning

confidence: 99%

Section: (Rc1) Keeping Uris Shortmentioning

confidence: 99%

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Evaluating the quality of the LOD cloud: An empirical investigation

Debattista

Lange

Auer

et al. 2018

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“…For instance, the tuple (ex:Aristotle, E1) and all the tuples having as subject ex:Aristotle, e.g., (ex:Aristotle, {P2,384 bc,D 1 }), will be sent to the same reducer, therefore we can replace ex:Aristotle with E1, e.g., (E1,P2,384 bc,D 1 ). After replacing the URIs of the subjects with their corresponding identifier, for the triples containing literals or classes as objects, we can store their corresponding real world triple (and its provenance), i.e., E1, P1, 384 bc , D 1 , since we have finished with all the conversions (see lines [15][16][17]. On the contrary, for the triples containing objects that belong to entities, we should also replace these URIs with their class of equivalence (see lines [18][19].…”

Section: Creation Of Semantics-aware Rdf Triplesmentioning

confidence: 99%

“…LODStats [16] offers several metadata and statistics for a big number of datasets, such as links between pairs of datasets, the number of triples of each dataset and so forth. In another approach [17], the authors computed the PageRank for 319 datasets for showing the popularity of specific datasets and the degree up to which other datasets trust a specific dataset. In [18], the authors selected 27 quality metrics for assessing the quality of 130 datasets, by using Luzzu framework [19].…”

Section: Introductionmentioning

confidence: 99%

High Performance Methods for Linked Open Data Connectivity Analytics

Mountantonakis

Tzitzikas

2018

Information

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The main objective of Linked Data is linking and integration, and a major step for evaluating whether this target has been reached, is to find all the connections among the Linked Open Data (LOD) Cloud datasets. Connectivity among two or more datasets can be achieved through common Entities, Triples, Literals, and Schema Elements, while more connections can occur due to equivalence relationships between URIs, such as owl:sameAs, owl:equivalentProperty and owl:equivalentClass, since many publishers use such equivalence relationships, for declaring that their URIs are equivalent with URIs of other datasets. However, there are not available connectivity measurements (and indexes) involving more than two datasets, that cover the whole content (e.g., entities, schema, triples) or "slices" (e.g., triples for a specific entity) of datasets, although they can be of primary importance for several real world tasks, such as Information Enrichment, Dataset Discovery and others. Generally, it is not an easy task to find the connections among the datasets, since there exists a big number of LOD datasets and the transitive and symmetric closure of equivalence relationships should be computed for not missing connections. For this reason, we introduce scalable methods and algorithms, (a) for performing the computation of transitive and symmetric closure for equivalence relationships (since they can produce more connections between the datasets); (b) for constructing dedicated global semantics-aware indexes that cover the whole content of datasets; and (c) for measuring the connectivity among two or more datasets. Finally, we evaluate the speedup of the proposed approach, while we report comparative results for over two billion triples.

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Scalable Methods for Measuring the Connectivity and Quality of Large Numbers of Linked Datasets

Mountantonakis

Tzitzikas

2017

J. Data and Information Quality

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Although the ultimate objective of Linked Data is linking and integration, it is not currently evident how connected the current Linked Open Data (LOD) cloud is. In this article, we focus on methods, supported by special indexes and algorithms, for performing measurements related to the connectivity of more than two datasets that are useful in various tasks including (a) Dataset Discovery and Selection ; (b) Object Coreference , i.e., for obtaining complete information about a set of entities, including provenance information; (c) Data Quality Assessment and Improvement , i.e., for assessing the connectivity between any set of datasets and monitoring their evolution over time, as well as for estimating data veracity; (d) Dataset Visualizations ; and various other tasks. Since it would be prohibitively expensive to perform all these measurements in a naïve way, in this article, we introduce indexes (and their construction algorithms) that can speed up such tasks. In brief, we introduce (i) a namespace-based prefix index, (ii) a sameAs catalog for computing the symmetric and transitive closure of the owl:sameAs relationships encountered in the datasets, (iii) a semantics-aware element index (that exploits the aforementioned indexes), and, finally, (iv) two lattice-based incremental algorithms for speeding up the computation of the intersection of URIs of any set of datasets. For enhancing scalability, we propose parallel index construction algorithms and parallel lattice-based incremental algorithms, we evaluate the achieved speedup using either a single machine or a cluster of machines, and we provide insights regarding the factors that affect efficiency. Finally, we report measurements about the connectivity of the (billion triples-sized) LOD cloud that have never been carried out so far.

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Assessing Trust with PageRank in the Web of Data

Cited by 11 publications

References 24 publications

Evaluating the quality of the LOD cloud: An empirical investigation

Evaluating the quality of the LOD cloud: An empirical investigation

High Performance Methods for Linked Open Data Connectivity Analytics

Scalable Methods for Measuring the Connectivity and Quality of Large Numbers of Linked Datasets

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