LinkLion: A Link Repository for the Web of Data

Nentwig, Markus; Soru, Tommaso; Ngomo, Axel-Cyrille Ngonga; Rahm, Erhard

doi:10.1007/978-3-319-11955-7_63

Cited by 20 publications

(23 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, our measurements concern connectivity, while [20] focuses on other aspects (like validity of documents) and they do not compute common URIs between three or more datasets. The authors of [15] created a portal for link discovery which contains mappings between pairs of 462 datasets. In comparison to our approach, they take into account only pairs of datasets and they do not index the URIs.…”

Section: Related Workmentioning

confidence: 99%

On measuring the lattice of commonalities among several linked datasets

Mountantonakis

Tzitzikas

2016

Proc. VLDB Endow.

View full text Add to dashboard Cite

A big number of datasets has been published according to the principles of Linked Data and this number keeps increasing. Although the ultimate objective is linking and integration, it is not currently evident how connected the current LOD cloud is. Measurements (and indexes) that involve more than two datasets are not available although they are important: (a) for obtaining complete information about one particular URI (or set of URIs) with provenance (b) for aiding dataset discovery and selection, (c) for assessing the connectivity between any set of datasets for quality checking and for monitoring their evolution over time, (d) for constructing visualizations that provide more informative overviews. Since it would be prohibitively expensive to perform all these measurements in a naïve way, in this paper we introduce indexes (and their construction algorithms) that can speedup 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. We discuss the speedup obtained by the introduced indexes and algorithms through comparative results and finally we report measurements about connectivity of the LOD cloud that have never been carried out so far.

show abstract

Section: Related Workmentioning

confidence: 99%

On measuring the lattice of commonalities among several linked datasets

Mountantonakis

Tzitzikas

2016

Proc. VLDB Endow.

View full text Add to dashboard Cite

show abstract

“…Config 1 applies a minimal similarity threshold of 0.9 for labels while configs 2 and 3 apply threshold 0.85 and 0.9 for the average label and geographic similarity, respectively. Links for DS2 were extracted from the LinkLion [17] repository, and have been computed by different LD tools from the community.…”

Section: Setup and Configurationsmentioning

confidence: 99%

“…As a consequence, there has been much research effort to develop link discovery (LD) frameworks [16] for automatic link generation. Platforms like datahub.io and sameas.org or repositories such as LinkLion [17] and BioPortal [21] collect and provide large sets of links between numerous different knowledge sources. They are valuable resources to improve the availability and re-usability of links in applications and avoid an expensive re-determination of the links.…”

Section: Introductionmentioning

confidence: 99%

Distributed Holistic Clustering on Linked Data

Nentwig

Groß

Möller

et al. 2017

On the Move to Meaningful Internet Systems. OTM 2017 Conferences

Self Cite

View full text Add to dashboard Cite

Abstract. Link discovery is an active field of research to support data integration in the Web of Data. Due to the huge size and number of available data sources, efficient and effective link discovery is a very challenging task. Common pairwise link discovery approaches do not scale to many sources with very large entity sets. We here propose a distributed holistic approach to link many data sources based on a clustering of entities that represent the same real-world object. Our clustering approach provides a compact and fused representation of entities, and can identify errors in existing links as well as many new links. We support a distributed execution of the clustering approach to achieve faster execution times and scalability for large real-world data sets. We provide a novel gold standard for multi-source clustering, and evaluate our methods with respect to effectiveness and efficiency for large data sets from the geographic and music domains.

show abstract

“…LinkLion [13] is an online repository for the storage, analysis and retrieval of interlinks between datasets. Users can submit interlinks to the repository, where they are represented according to the LinkLion vocabulary.…”

Section: Existing Approachesmentioning

confidence: 99%

“…The SCM was originally designed to represent vocabulary mappings only but has now been expanded to also represent interlinks which allows for meta-data annotation in a similar manner to how BioPortal [18] and LinkLion [13] represent interlinks.  For R2, the mappings will be stored in a triple-store with SPARQL 1.1 and SPARQL 1.1 Federated Query compliance, so all mappings (local and remote) can be accessed.…”

Section: Summr Designmentioning

confidence: 99%

Validating Interlinks Between Linked Data Datasets with the SUMMR Methodology

Meehan

Kontokostas²,

Freudenberg³

et al. 2016

On the Move to Meaningful Internet Systems: OTM 2016 Conferences

View full text Add to dashboard Cite

Abstract. Linked Data datasets use interlinks to connect semantically similar resources across datasets. As datasets evolve, a resources locator can change which can cause interlinks that contain old resource locators, to no longer dereference and become invalid. Validating interlinks, through validating the resource locators within them, when a dataset has changed is important to ensure interlinks work as intended. In this paper we introduce the SPARQL Usage for Mapping Maintenance and Reuse (SUMMR) methodology. SUMMR is an approach for Mapping Maintenance and Reuse (MMR) that provides query templates which are based on standard SPARQL queries for MMR activities. This paper describes SUMMR and two experiments: a lab-based evaluation of SUMMR's mapping maintenance query templates and a deployment of SUMMR in the DBpedia v.2015-10 release to detect invalid interlinks. The labbased evaluation involved detecting interlinks that have become invalid, due to changes in resource locators and the repair of the invalid interlinks. The results show that the SUMMR templates and approach can be used to effectively detect and repair invalid interlinks. SUMMR's query template for discovering invalid interlinks was applied to the DBpedia v.2015-10 release, which discovered 53,418 invalid interlinks in that release.

show abstract

LinkLion: A Link Repository for the Web of Data

Cited by 20 publications

References 3 publications

On measuring the lattice of commonalities among several linked datasets

On measuring the lattice of commonalities among several linked datasets

Distributed Holistic Clustering on Linked Data

Validating Interlinks Between Linked Data Datasets with the SUMMR Methodology

Contact Info

Product

Resources

About