SPARQLGraph: a web-based platform for graphically querying biological Semantic Web databases

Schweiger, Dominik; Trajanoski, Zlatko; Pabinger, Stephan

doi:10.1186/1471-2105-15-279

Cited by 26 publications

(14 citation statements)

References 16 publications

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“…Our tool is complementary to existing tools that help create queries such as SPARQL assist [23], Visor [24] iSPARQL [25] and SPARQLGraph [26], these tools are based on local instance or class relationship browsing, or on query suggestion and completion or on a graphical representation of the SPARQL query.…”

Section: Discussionmentioning

confidence: 99%

RDF2Graph a tool to recover, understand and validate the ontology of an RDF resource

et al. 2015

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BackgroundSemantic 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.ResultsHere 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.ConclusionRDF2Graph 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-015-0038-9) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

RDF2Graph a tool to recover, understand and validate the ontology of an RDF resource

et al. 2015

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“…This could be done in a graphical way used e.g. by graphical SPARQL query builders such as SPAR-QLGraph [127], but not necessarily complicated by the full expressiveness of SPARQL.…”

Section: Motivating Examplementioning

confidence: 99%

Survey of tools for Linked Data consumption

Klímek

Škoda

Nečaský

2019

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There is lots of data published as Linked (Open) Data (LOD/LD). At the same time, there is also a multitude of tools for publication of LD. However, potential LD consumers still have difficulty discovering, accessing and exploiting LD. This is because compared to consumption of traditional data formats such as XML and CSV files, there is a distinct lack of tools for consumption of LD. The promoters of LD use the well-known 5-star Open Data deployment scheme to suggest that consumption of LD is a better experience once the consumer knows RDF and related technologies. This suggestion, however, falls short when the consumers search for an appropriate tooling support for LD consumption. In this paper we define a LD consumption process. Based on this process and current literature, we define a set of 34 requirements a hypothetical Linked Data Consumption Platform (LDCP) should ideally fulfill. We cover those requirements with a set of 94 evaluation criteria. We survey 110 tools identified as potential candidates for an LDCP, eliminating them in 3 rounds until 16 candidates for remain. We evaluate the 16 candidates using our 94 criteria. Based on this evaluation we show which parts of the LD consumption process are covered by the 16 candidates. Finally, we identify 8 tools which satisfy our requirements on being a LDCP. We also show that there are important LD consumption steps which are not sufficiently covered by existing tools. The authors of LDCP implementations may use this survey to decide about directions of future development of their tools. LD experts may use it to see the level of support of the state of the art technologies in existing tools. Non-LD experts may use it to choose a tool which supports their LD processing needs without requiring them to have expert knowledge of the technologies. The paper can also be used as an introductory text to LD consumption.

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“…There are already lots of works using visual interface with "Drag-and-Drop" graphic icons or objects to formulate general SQL queries for RDBMS, such as Ajax Query Builder [4]. But in multidimensional data store area, many efforts such as SPARQLGraph [5] can be seen as domain-specific methodology. NaLIR [6,7] now can accept a logically complex English language sentence as query input for data store, but there is still a long way to take natural language queries as a database query interface due to the difficulty of multilingual user-specified query translation into the actual schema structure in the multidimensional data store.…”

Section: Related Workmentioning

confidence: 99%

ViMDQL: An Easy-to-Use Drag-and-Drop Visual Query Composer for Multidimensional Data

2017

JWSA

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Abstract. In this study, we present ViMDQL, a useful system to tack the challenge of composing multidimensional data based analytical queries easily. Currently, fundamental query blocks such as import and export, create, retrieve, update, delete are supported, which can be used to loading and exporting data, creating, retrieving, updating, join, sampling and removing multidimensional data. Analytic functionalities such as aggregation, statistics are also supported. We demonstrated that, since ViMDQL make users can express their query intent by drag and drop to link query blocks together, which enable users can easily compose queries like the Stacker Game, it has been proved to be a productivity tool for graphically building multidimensional data based queries.

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SPARQLGraph: a web-based platform for graphically querying biological Semantic Web databases

Cited by 26 publications

References 16 publications

RDF2Graph a tool to recover, understand and validate the ontology of an RDF resource

RDF2Graph a tool to recover, understand and validate the ontology of an RDF resource

Survey of tools for Linked Data consumption

ViMDQL: An Easy-to-Use Drag-and-Drop Visual Query Composer for Multidimensional Data

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