The data set knowledge graph: Creating a linked open data source for data sets

Färber, Michael; Lamprecht, David

doi:10.1162/qss_a_00161

Cited by 22 publications

(10 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Evaluation Dataset. As a database for datasets, we use the Data Set Knowledge Graph (DSKG) [12]. This up-to-date collection published in 2021 is based on dataset entries in Wikidata and OpenAIRE.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Recommending Datasets for Scientific Problem Descriptions

Färber

Leisinger

2021

Proceedings of the 30th ACM International Conference on Information &Amp; Knowledge Management

Self Cite

View full text Add to dashboard Cite

The steadily rising number of datasets is making it increasingly difficult for researchers and practitioners to be aware of all datasets, particularly of the most relevant datasets for a given research problem. To this end, dataset search engines have been proposed. However, they are based on user's keywords and, thus, have difficulty determining precisely fitting datasets for complex research problems. In this paper, we propose a system that recommends suitable datasets based on a given research problem description. The recommendation task is designed as a domain-specific text classification task. As shown in a comprehensive offline evaluation using various stateof-the-art models, as well as 88,000 paper abstracts and 265,000 citation contexts as research problem descriptions, we obtain an F1-score of 0.75. In an additional user study, we show that users in real-world settings are 88% satisfied in all test cases. We therefore see promising future directions for dataset recommendation.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Google Dataset Search [2] relies on metadata, too. Datasets' metadata freely available on the web is often of poor quality [12]. Thus, in contrast to Google Dataset Search, in our approach we use a much cleaner data basis and links to papers.…”

Section: Brown Corpusmentioning

confidence: 99%

Recommending Datasets for Scientific Problem Descriptions

Färber

Leisinger

2021

Proceedings of the 30th ACM International Conference on Information &Amp; Knowledge Management

Self Cite

View full text Add to dashboard Cite

show abstract

“…Metamap 16 performs named entity recognition, returning the named entities and a confidence factor. SemRep 17 performs relation extraction while relying on MetaMap. The result of this NLP process is an open data graph.…”

Section: Scholarly Data Ecosystemmentioning

confidence: 99%

Knowledge graphs for enhancing transparency in health data ecosystems1

Aisopos

Jozashoori

Emetis

et al. 2023

View full text Add to dashboard Cite

Tailoring personalized treatments demands the analysis of a patient’s characteristics, which may be scattered over a wide variety of sources. These features include family history, life habits, comorbidities, and potential treatment side effects. Moreover, the analysis of the services visited the most by a patient before a new diagnosis, as well as the type of requested tests, may uncover patterns that contribute to earlier disease detection and treatment effectiveness. Built on knowledge-driven ecosystems, we devise DE4LungCancer, a health data ecosystem of data sources for lung cancer. In this data ecosystem, knowledge extracted from heterogeneous sources, e.g., clinical records, scientific publications, and pharmacological data, is integrated into knowledge graphs. Ontologies describe the meaning of the combined data, and mapping rules enable the declarative definition of the transformation and integration processes. DE4LungCancer is assessed regarding the methods followed for data quality assessment and curation. Lastly, the role of controlled vocabularies and ontologies in health data management is discussed, as well as their impact on transparent knowledge extraction and analytics. This paper presents the lessons learned in the DE4LungCancer development. It demonstrates the transparency level supported by the proposed knowledge-driven ecosystem, in the context of the lung cancer pilots of the EU H2020-funded project BigMedilytic, the ERA PerMed funded project P4-LUCAT, and the EU H2020 projects CLARIFY and iASiS.

show abstract

“…Bogdoll et al [5] gathered metadata of over 200 datasets in the field of autonomous driving. Similarly, Färber and Lamprecht released the data set knowledge graph, which is a collection of over 2,000 datasets with added metadata [48]. D'Ulizia et al [49] analyzed the metadata of datasets for fake news detection.…”

Section: B Dataset Analysismentioning

confidence: 99%

Impact, Attention, Influence: Early Assessment of Autonomous Driving Datasets

Bogdoll¹,

Hendl²,

Schreyer³

et al. 2023

Preprint

View full text Add to dashboard Cite

Autonomous Driving (AD), the area of robotics with the greatest potential impact on society, has gained a lot of momentum in the last decade. As a result of this, the number of datasets in AD has increased rapidly. Creators and users of datasets can benefit from a better understanding of developments in the field. While scientometric analysis has been conducted in other fields, it rarely revolves around datasets. Thus, the impact, attention, and influence of datasets on autonomous driving remains a rarely investigated field. In this work, we provide a scientometric analysis for over 200 datasets in AD. We perform a rigorous evaluation of relations between available metadata and citation counts based on linear regression. Subsequently, we propose an Influence Score to assess a dataset already early on without the need for a track-record of citations, which is only available with a certain delay.

show abstract

The data set knowledge graph: Creating a linked open data source for data sets

Cited by 22 publications

References 49 publications

Recommending Datasets for Scientific Problem Descriptions

Recommending Datasets for Scientific Problem Descriptions

Knowledge graphs for enhancing transparency in health data ecosystems1

Impact, Attention, Influence: Early Assessment of Autonomous Driving Datasets

Contact Info

Product

Resources

About