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Introducing Wikidata to the Linked Data Web
"... Abstract. Wikidata is the central data management platform of Wikipedia. By the efforts of thousands of volunteers, the project has produced a large, open knowledge base with many interesting applications. The data is highly interlinked and connected to many other datasets, but it is also very rich, ..."
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Abstract. Wikidata is the central data management platform of Wikipedia. By the efforts of thousands of volunteers, the project has produced a large, open knowledge base with many interesting applications. The data is highly interlinked and connected to many other datasets, but it is also very rich, complex, and not available in RDF. To address this issue, we introduce new RDF exports that connect Wikidata to the Linked Data Web. We explain the data model of Wikidata and discuss its encoding in RDF. Moreover, we introduce several partial exports that provide more selective or simplified views on the data. This includes a class hierarchy and several other types of ontological axioms that we extract from the site. All datasets we discuss here are freely available online and updated regularly. 1
Continuously Updating Query Results over Real-Time Linked Data
"... Abstract. Existing solutions to query dynamic Linked Data sources extend the language, and require continuous server processing for each query. Traditional endpoints accept highly expressive queries, contributing to high server cost. Extending these endpoints for time-sensitive queries increases th ..."
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Abstract. Existing solutions to query dynamic Linked Data sources extend the language, and require continuous server processing for each query. Traditional endpoints accept highly expressive queries, contributing to high server cost. Extending these endpoints for time-sensitive queries increases the server cost even further. To make continuous querying over real-time Linked Data more affordable, we extend the low-cost Triple Pattern Fragments ( ) interface with support for time-sensitive queries. In this paper, we discuss a framework on top of that allows clients to execute queries with continuously updating results. Our experiments indicate that this extension significantly lowers the server complexity. The trade-off is an increase in the execution time per query. We prove that by moving the complexity of continuously evaluating real-time queries over Linked Data to the clients and thus increasing the bandwidth usage, the cost of server-side interfaces is significantly reduced. Our results show that this solution makes real-time querying more scalable in terms of usage for a large amount of concurrent clients when compared to the alternatives.
Reifying RDF: What Works Well With Wikidata?
"... Abstract. In this paper, we compare various options for reifying RDF triples. We are motivated by the goal of representing Wikidata as RDF, which would allow legacy Semantic Web languages, techniques and tools – for example, SPARQL engines – to be used for Wikidata. However, Wikidata annotates state ..."
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Abstract. In this paper, we compare various options for reifying RDF triples. We are motivated by the goal of representing Wikidata as RDF, which would allow legacy Semantic Web languages, techniques and tools – for example, SPARQL engines – to be used for Wikidata. However, Wikidata annotates statements with qualifiers and references, which re-quire some notion of reification to model in RDF. We thus investigate four such options: (1) standard reification, (2) n-ary relations, (3) single-ton properties, and (4) named graphs. Taking a recent dump of Wikidata, we generate the four RDF datasets pertaining to each model and discuss high-level aspects relating to data sizes, etc. To empirically compare the effect of the different models on query times, we collect a set of bench-mark queries with four model-specific versions of each query. We present the results of running these queries against five popular SPARQL imple-mentations: 4store, BlazeGraph, GraphDB, Jena TDB and Virtuoso. 1
and validate the ontology of an RDF resource
"... Background: Semantic web technologies have a tremendous potential for the integration of heterogeneous data sets. Therefore, an increasing number of widely used biological resources are becoming available in the RDF data model. There are however, no tools available that provide structural overviews ..."
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Background: Semantic web technologies have a tremendous potential for the integration of heterogeneous data sets. Therefore, an increasing number of widely used biological resources are becoming available in the RDF data model. There are however, no tools available that provide structural overviews of these resources. Such structural overviews are essential to efficiently query these resources and to assess their structural integrity and design, thereby strengthening their use and potential. Results: Here we present RDF2Graph, a tool that automatically recovers the structure of an RDF resource. The generated overview allows to create complex queries on these resources and to structurally validate newly created resources. Conclusion: RDF2Graph facilitates the creation of complex queries thereby enabling access to knowledge stored across multiple RDF resources. RDF2Graph facilitates creation of high quality resources and resource descriptions, which in turn increases usability of the semantic web technologies.
