Dataset Retrieval

Kunze, Sven R.; Auer, Sören

doi:10.1109/icsc.2013.12

Cited by 22 publications

(25 citation statements)

References 10 publications

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“…One solution proposed to tackle the metadata quality problem include cross-validating metadata by merging feeds from identified entities [65]. Using self-categorized information [85] as facets is another. Attempts to better represent the underlying data [22] do have an affect on search.…”

Section: Data Handlingmentioning

confidence: 99%

Dataset search: a survey

et al. 2019

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Generating value from data requires the ability to find, access and make sense of datasets. There are many efforts underway to encourage data sharing and reuse, from scientific publishers asking authors to submit data alongside manuscripts to data marketplaces, open data portals and data communities. Google recently beta released a search service for datasets, which allows users to discover data stored in various online repositories via keyword queries. These developments foreshadow an emerging research field around dataset search or retrieval that broadly encompasses frameworks, methods and tools that help match a user data need against a collection of datasets. Here, we survey the state of the art of research and commercial systems in dataset retrieval. We identify what makes dataset search a research field in its own right, with unique challenges and methods and highlight open problems. We look at approaches and implementations from related areas dataset search is drawing upon, including information retrieval, databases, entity-centric and tabular search in order to identify possible paths to resolve these open problems as well as immediate next steps that will take the field forward.

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Section: Data Handlingmentioning

confidence: 99%

Dataset search: a survey

et al. 2019

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“…Kunze and Auer [9] proposed a search engine for RDF datasets that is mainly based on filters that work similar to a faceted search. For ranking, the authors use a similarity function that comprises different aspects.…”

Section: Related Workmentioning

confidence: 99%

“…The weighting function w(v) is inspired by the idf term of the tf-idf function. Thus, the more datasets are using the vocabulary, the less important it is for the topical similarity and the lower its weighting [9]. The last baseline is using Apache Lucene 7 .…”

Section: Baselinesmentioning

confidence: 99%

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Detecting Similar Linked Datasets Using Topic Modelling

Röder

Ngomo

Ermilov

et al. 2016

The Semantic Web. Latest Advances and New Domains

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Abstract. The Web of data is growing continuously with respect to both the size and number of the datasets published. Porting a dataset to five-star Linked Data however requires the publisher of this dataset to link it with the already available linked datasets. Given the size and growth of the Linked Data Cloud, the current mostly manual approach used for detecting relevant datasets for linking is obsolete. We study the use of topic modelling for dataset search experimentally and present Tapioca, a linked dataset search engine that provides data publishers with similar existing datasets automatically. Our search engine uses a novel approach for determining the topical similarity of datasets. This approach relies on probabilistic topic modelling to determine related datasets by relying solely on the metadata of datasets. We evaluate our approach on a manually created gold standard and with a user study. Our evaluation shows that our algorithm outperforms a set of comparable baseline algorithms including standard search engines significantly by 6 % F1-score. Moreover, we show that it can be used on a large real world dataset with a comparable performance.

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“…With respect to publishing and providing scientific data as Linked Open Data and RDF datasets, Kunze and Auer (2013) have sketched a way to query datasets on the basis of their VOID descriptions similar to metadata records describing literature content. Moreover, they suggested referring to single data by means of its URI patterns, hence a dataset containing such a pattern to be considered as 'relevant'.…”

Section: Related Workmentioning

confidence: 99%

Patterns for searching data on the web across different research communities

Borst

Limani

2020

LIBER Quarterly

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Being a concept quite familiar in the domain of information retrieval, data search in a web based environment has recently gained attention. With researchers and academic institutions increasingly publishing their data on the public web, traditional research workflows with respect to data search are subject to empirical analysis, user studies, re-engineering and service development. We investigate these workflows more in detail and introduce three patterns of web-based data search intended to serve both as a general reference and as a starting point for discipline specific adoptions. We give some real-world examples in terms of existing web applications and GUI components, thereby suggesting a combination of both generic and community specific approaches towards solutions for data search. We further analyze these patterns by means of empirical evidences we found in some research communities, before giving a summary and outlook on future work.

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Dataset Retrieval

Cited by 22 publications

References 10 publications

Dataset search: a survey

Dataset search: a survey

Detecting Similar Linked Datasets Using Topic Modelling

Patterns for searching data on the web across different research communities

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