Dataset Summary Visualization with LODSight

Dudáš, Marek; Svátek, Vojtěch; Mynarz, Jindřich

doi:10.1007/978-3-319-25639-9_7

Cited by 22 publications

(13 citation statements)

References 7 publications

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“…LODSight [21] is an RDF dataset summary visualization tool that displays typical combinations of types and predicates. It relies solely on SPARQL queries and as such, given a SPARQL endpoint, it can theoretically summarize all accessible data, without requiring any user input.…”

Section: Statistical Summarizationmentioning

confidence: 99%

Summarizing semantic graphs: a survey

et al. 2018

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The explosion in the amount of the available RDF data has lead to the need to explore, query and understand such data sources. Due to the complex structure of RDF graphs and their heterogeneity, the exploration and understanding tasks are significantly harder than in relational databases, where the schema can serve as a first step toward understanding the structure. Summarization has been applied to RDF data to facilitate these tasks. Its purpose is to extract concise and meaningful information from RDF knowledge bases, representing their content as faithfully as possible. There is no single concept of RDF summary, and not a single but many approaches to build such summaries; each is better suited for some uses, and each presents specific challenges with respect to its construction. This survey is the first to provide a comprehensive survey of summarization method for semantic RDF graphs. We propose a taxonomy of existing works in this area, including also some closely related works developed prior to the adoption of RDF in the data management community; we present the concepts at the core of each approach and outline their main technical aspects and implementation. We hope the survey will help readers understand this scientifically rich area, and identify the most pertinent summarization method for a variety of usage scenarios.

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Section: Statistical Summarizationmentioning

confidence: 99%

Summarizing semantic graphs: a survey

et al. 2018

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“…An example of inference which would be beneficial to support in an LDCP can be the SKOS vocabulary with its skos:broader and skos:narrower properties. According to the SKOS reference, these properties are inverse properties 46 . For instance, a dataset may contain triples such as ex:a skos:broader ex:b, whereas, the output is required to contain ex:b skos:narrower ex:a because of limitations of another component.…”

Section: Requirement 20 (Inference and Resource Fusion)mentioning

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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“…Given a SPARQL endpoint, LODEx [7] automatically generates a schema-centric, node-link diagram visualization of the content behind this endpoint. LODSight [19] and ExpLOD [27] follow conceptually similar approaches, representing similar information as nodelink diagrams. The former provides more concise, but possibly less accurate summaries than LODEx as it might suggest possible relations that are not actually present in the data.…”

Section: Background and Motivationmentioning

confidence: 99%

“…The selection of a dataset is not only based on triple count, number of links to other datasets, and presence of some keywords. In their search for datasets, users will often want to get more detailed information about what is in the dataset, as suggested by services such as LODSight [19] and LODatio [28].…”

Section: Visual Summaries Of a Dataset's Contentsmentioning

confidence: 99%

Browsing Linked Data Catalogs with LODAtlas

Pietriga

Gözükan²,

Appert³

et al. 2018

Lecture Notes in Computer Science

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The Web of Data is growing fast, as exemplified by the evolution of the Linked Open Data (LOD) cloud over the last ten years. One of the consequences of this growth is that it is becoming increasingly difficult for application developers and end-users to find the datasets that would be relevant to them. Semantic Web search engines, open data catalogs, datasets and frameworks such as LODStats and LOD Laundromat, are all useful but only give partial, even if complementary, views on what datasets are available on the Web. We introduce LODAtlas, a portal that enables users to find datasets of interest. Users can make different types of queries about both the datasets' metadata and contents, aggregated from multiple sources. They can then quickly evaluate the matching datasets' relevance, thanks to LODAtlas' summary visualizations of their general metadata, connections and contents.

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Dataset Summary Visualization with LODSight

Cited by 22 publications

References 7 publications

Summarizing semantic graphs: a survey

Summarizing semantic graphs: a survey

Survey of tools for Linked Data consumption

Browsing Linked Data Catalogs with LODAtlas

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