Is the LOD cloud at risk of becoming a museum for datasets? Looking ahead towards a fully collaborative and sustainable LOD cloud

Debattista, Jeremy; Attard, Judie; Brennan, Rob; O’Sullivan, Declan

doi:10.1145/3308560.3317075

Cited by 4 publications

(7 citation statements)

References 11 publications

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“…The analysis of measure efficiency involved 280 datasets out of 1,163 (end of 2017). While this number seems low regarding the theoretically available number of datasets, compared to other qualitative studies on datasets from the LOD Cloud, for instance [11,12,21], it sounds reasonable and of sufficient representativeness. Unfortunately, this is the current situation and, without additionally querying SPARQLendpoints, the most that one can get from crawling the LOD Cloud.…”

Section: Size Of the Samplementioning

confidence: 75%

“…The reasons were: (1) corrupt downloads, (2) wrong file media type statements, and (3) syntax errors or other formats than these what were expected during the transformation process. This number seems low compared to the total number of available datasets in the LOD Cloud, though it sounds reasonable compared to recent studies on the LOD Cloud [11,12,21]. Table 2 gives some descriptive statistics about the analyzed datasets.…”

Section: Datasetsmentioning

confidence: 79%

“…The quality aspect of Linked Open Data has been subject to some recent studies. Debattista et al assessed the quality of metadata and dataset availability, investigating datasets from the LOD Cloud 2014 [12] and early 2019 [11]. Haller et al [21] investigated different types of links, i.e., contained in the ABox and TBox, exposed by 430 datasets in the LOD Cloud.…”

Section: Rdf-specific Analysesmentioning

confidence: 99%

“…As graph library we used graph-tool, 11 an efficient library for statistical analysis of graphs. In graph-tool, core data structures and algorithms are implemented in C/C++, while the library itself can be used with Python.…”

Section: Datasetsmentioning

confidence: 99%

“…Example: https://old.datahub.io/dataset/<dataset-name>/ datapackage.json 10. Other media type statements like html_json_ld_ttl_ rdf_xml or rdf_xml_turtle_html were ignored, since they are ambiguous 11. graph-tool, https://graph-tool.skewed.de/.…”

mentioning

confidence: 99%

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Charaterizing RDF graphs through graph-based measures – framework and assessment

Zloch

Acosta

Hienert

et al. 2021

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The topological structure of RDF graphs inherently differs from other types of graphs, like social graphs, due to the pervasive existence of hierarchical relations (TBox), which complement transversal relations (ABox). Graph measures capture such particularities through descriptive statistics. Besides the classical set of measures established in the field of network analysis, such as size and volume of the graph or the type of degree distribution of its vertices, there has been some effort to define measures that capture some of the aforementioned particularities RDF graphs adhere to. However, some of them are redundant, computationally expensive, and not meaningful enough to describe RDF graphs. In particular, it is not clear which of them are efficient metrics to capture specific distinguishing characteristics of datasets in different knowledge domains (e.g., Cross Domain vs. Linguistics). In this work, we address the problem of identifying a minimal set of measures that is efficient, essential (non-redundant), and meaningful. Based on 54 measures and a sample of 280 graphs of nine knowledge domains from the Linked Open Data Cloud, we identify an essential set of 13 measures, having the capacity to describe graphs concisely. These measures have the capacity to present the topological structures and differences of datasets in established knowledge domains.

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Section: Size Of the Samplementioning

confidence: 75%

Section: Datasetsmentioning

confidence: 79%

Section: Rdf-specific Analysesmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

mentioning

confidence: 99%

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Charaterizing RDF graphs through graph-based measures – framework and assessment

Zloch

Acosta

Hienert

et al. 2021

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LODChain: Strengthen the Connectivity of Your RDF Dataset to the Rest LOD Cloud

Mountantonakis

Tzitzikas

2022

Lecture Notes in Computer Science

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There is a recent trend for using the novel Artificial Intelligence Chat-GPT chatbox, which provides detailed responses and articulate answers across many domains of knowledge. However, in many cases it returns plausible-sounding but incorrect or inaccurate responses, whereas it does not provide evidence. Therefore, any user has to further search for checking the accuracy of the answer or/and for finding more information about the entities of the response. At the same time there is a high proliferation of RDF Knowledge Graphs (KGs) over any real domain, that offer high quality structured data. For enabling the combination of ChatGPT and RDF KGs, we present a research prototype, called GPT•LODS, which is able to enrich any ChatGPT response with more information from hundreds of RDF KGs. In particular, it identifies and annotates each entity of the response with statistics and hyperlinks to LODsyndesis KG (which contains integrated data from 400 RDF KGs and over 412 million entities). In this way, it is feasible to enrich the content of entities and to perform fact checking and validation for the facts of the response at real time.Demo Video: https://youtu.be/H30bSv9NfUw. Demo URL: https://demos.isl.ics.forth.gr/GPToLODS.

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Linked open data for agricultural land evaluation

Fibriani,

Mardhani,

Ashari

2024

E3S Web Conf.

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The government has programs that related to food independence which can be achieved by optimizing agricultural data analysis as decision support. The analysis requires a combination of several data but these data are in different sources and formats. Linked Open Data (LOD) is the answer, it makes the interconnectedness of all data that be needed, the interconnected data can be used for the next process in land evaluation. Land evaluation has 2 activities: land characteristics, and land quality. This research focus on designing a prototype of a land evaluation system model based on linked open data. There are 12 data sources that will be used for this land evaluation. The datas are spatial data which have longitude and latitude. In addition, any 2 types of data that be used text and geojson. The linked between these attributes from many sources and was using LOD’s consept creates a relationship terminology.

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Is the LOD cloud at risk of becoming a museum for datasets? Looking ahead towards a fully collaborative and sustainable LOD cloud

Cited by 4 publications

References 11 publications

Charaterizing RDF graphs through graph-based measures – framework and assessment

Charaterizing RDF graphs through graph-based measures – framework and assessment

LODChain: Strengthen the Connectivity of Your RDF Dataset to the Rest LOD Cloud

Linked open data for agricultural land evaluation

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