LOD-a-lot

Fernández, Javier D.; Beek, Wouter; Martínez‐Prieto, Miguel A.; Arias, Mario Lobo

doi:10.1007/978-3-319-68204-4_7

Cited by 35 publications

(32 citation statements)

References 19 publications

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“…We have tested our approach on the LOD-a-lot dataset [11] 2 , a compressed data file that contains 28 billion unique triples from the 2015 LOD Laundromat Linked Data crawl [3]. This large subset of the LOD Cloud represents our data graph (Definition 1).…”

Section: Datasetmentioning

confidence: 99%

Detecting Erroneous Identity Links on the Web Using Network Metrics

Raad

Beek

Harmelen

et al. 2018

Lecture Notes in Computer Science

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In the absence of a central naming authority on the Semantic Web, it is common for different datasets to refer to the same thing by different IRIs. Whenever multiple names are used to denote the same thing, owl:sameAs statements are needed in order to link the data and foster reuse. Studies that date back as far as 2009, have observed that the owl:sameAs property is sometimes used incorrectly. In this paper, we show how network metrics such as the community structure of the owl:sameAs graph can be used in order to detect such possibly erroneous statements. One benefit of the here presented approach is that it can be applied to the network of owl:sameAs links itself, and does not rely on any additional knowledge. In order to illustrate its ability to scale, the approach is evaluated on the largest collection of identity links to date, containing over 558M owl:sameAs links scraped from the LOD Cloud.

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Section: Datasetmentioning

confidence: 99%

Detecting Erroneous Identity Links on the Web Using Network Metrics

Raad

Beek

Harmelen

et al. 2018

Lecture Notes in Computer Science

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“…The explicit identity relation (∼ e ) is obtained from the LOD-a-lot dataset 4 [7], a compressed data file that contains the unique triples from the 2015 LOD Laundromat corpus [2]. We use the HDT C++ library 5 to stream the result set of the following SPARQL query to a file, which takes ∼27 min: The results of this query are unique (keyword distinct) and the projection (?s ?p ?o) returns triples instead of pairs, so that regular RDF tools for storage and querying can be used.…”

Section: Explicit Identity Relationmentioning

confidence: 99%

“…6 The composition key(val(x)) gives us the identity set of x. We built an efficient implementation of this key-value scheme using the RocksDB persistent key-value store through a SWI Prolog API that we designed for this purpose 7 .…”

Section: Implicit Identity Relation: Closurementioning

confidence: 99%

sameAs.cc: The Closure of 500M owl:sameAs Statements

et al. 2018

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The owl:sameAs predicate is an essential ingredient of the Semantic Web architecture. It allows parties to independently mint names, while at the same time ensuring that these parties are able to understand each other's data. An online resource that collects all owl:sameAs statements on the Linked Open Data Cloud has therefore both practical impact (it helps data users and providers to find different names for the same entity) as well as analytical value (it reveals important aspects of the connectivity of the LOD Cloud). This paper presents sameAs.cc: the largest dataset of identity statements that has been gathered from the LOD Cloud to date. We describe an efficient approach for calculating and storing the full equivalence closure over this dataset. The dataset is published online, as well as a web service from which the data and its equivalence closure can be queried.

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“…One can exploit the offered services in order to find all the documents of a given URI or namespace. LOD-a-LOT [13] is a service that collects all the documents of LODLaundromat in a single file, i.e., it enables query answering at large scale. LODStats [2] collects metadata for approximately 10,000 RDF datasets and offers statistics and metadata for them, while LOV (Linked Open Vocabularies) [14] has collected hundreds of ontologies (including CIDOC CRM), and offers a keyword search for finding the most relevant schema elements for a given keyword, e.g., by typing Birth, it can return as relevant class the "E67_Birth" of CIDOC CRM.…”

Section: Related Workmentioning

confidence: 99%

LODsyndesis: Global Scale Knowledge Services

Mountantonakis

Tzitzikas

2018

Heritage

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In this paper, we present LODsyndesis, a suite of services over the datasets of the entire Linked Open Data Cloud, which offers fast, content-based dataset discovery and object co-reference. Emphasis is given on supporting scalable cross-dataset reasoning for finding all information about any entity and its provenance. Other tasks that can be benefited from these services are those related to the quality and veracity of data since the collection of all information about an entity, and the cross-dataset inference that is feasible, allows spotting the contradictions that exist, and also provides information for data cleaning or for estimating and suggesting which data are probably correct or more accurate. In addition, we will show how these services can assist the enrichment of existing datasets with more features for obtaining better predictions in machine learning tasks. Finally, we report measurements that reveal the sparsity of the current datasets, as regards their connectivity, which in turn justifies the need for advancing the current methods for data integration. Measurements focusing on the cultural domain are also included, specifically measurements over datasets using CIDOC CRM (Conceptual Reference Model), and connectivity measurements of British Museum data. The services of LODsyndesis are based on special indexes and algorithms and allow the indexing of 2 billion triples in around 80 min using a cluster of 96 computers.

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LOD-a-lot

Cited by 35 publications

References 19 publications

Detecting Erroneous Identity Links on the Web Using Network Metrics

Detecting Erroneous Identity Links on the Web Using Network Metrics

sameAs.cc: The Closure of 500M owl:sameAs Statements

LODsyndesis: Global Scale Knowledge Services

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