Dataset search: a survey

Chapman, Adriane; Simperl, Elena; Koesten, Laura; Konstantinidis, George; Ibáñez, Luis‐Daniel; Kacprzak, Emilia; Groth, Paul

doi:10.1007/s00778-019-00564-x

Cited by 128 publications

(114 citation statements)

References 163 publications

Supporting

Mentioning

110

Contrasting

Unclassified

Order By: Relevance

“…Dataset search has become a new research field with new challenges. Chapman et al [3] classify dataset search into basic and constructive dataset search. Basic dataset search returns a list of existing datasets based on a user's query, while constructive dataset search [5] generates datasets on-the-fly based on a user's needs and query.…”

Section: Related Workmentioning

confidence: 99%

“…Dataset retrieval is receiving more attention as people from different fields and domains start to rely on datasets for their work. There are many data portals with the purpose of effective and efficient data management and data sharing, such as data.gov 1 , datahub 2 and data.world 3 . Most of those data portals use CKAN 4 as their backend.…”

Section: Introductionmentioning

confidence: 99%

“…Most of those data portals use CKAN 4 as their backend. However, there are two problems of dataset search engines using such infrastructure: First, ranking performance relies on the quality of metadata of datasets, while many datasets lack high quality metadata; second, the information in the metadata may not satisfy the user's information need or help them solve their task [3]. A user may not know the organization of a potentially relevant dataset, or the tags data publishers provide with a dataset.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Leveraging Schema Labels to Enhance Dataset Search

Chen

Jia

Heflin

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

A search engine's ability to retrieve desirable datasets is important for data sharing and reuse. Existing dataset search engines typically rely on matching queries to dataset descriptions. However, a user may not have enough prior knowledge to write a query using terms that match with description text. We propose a novel schema label generation model which generates possible schema labels based on dataset table content. We incorporate the generated schema labels into a mixed ranking model which not only considers the relevance between the query and dataset metadata but also the similarity between the query and generated schema labels. To evaluate our method on real-world datasets, we create a new benchmark specifically for the dataset retrieval task. Experiments show that our approach can effectively improve the precision and NDCG scores of the dataset retrieval task compared with baseline methods. We also test on a collection of Wikipedia tables to show that the features generated from schema labels can improve the unsupervised and supervised web table retrieval task as well.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Leveraging Schema Labels to Enhance Dataset Search

Chen

Jia

Heflin

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Our system, Visus, aims to fill this gap in the current HGML systems. In the next session, we describe how we integrated data search and automatic joins in our framework [2], as well as model interpretation based on rule visualization [15]. Figure 1 shows the main components of our visual analytics framework.…”

Section: Related Workmentioning

confidence: 99%

Visus

Santos

Castelo

Felix

et al. 2019

Proceedings of the Workshop on Human-in-the-Loop Data Analytics

View full text Add to dashboard Cite

While the demand for machine learning (ML) applications is booming, there is a scarcity of data scientists capable of building such models. Automatic machine learning (AutoML) approaches have been proposed that help with this problem by synthesizing end-toend ML data processing pipelines. However, these follow a besteffort approach and a user in the loop is necessary to curate and refine the derived pipelines. Since domain experts often have little or no expertise in machine learning, easy-to-use interactive interfaces that guide them throughout the model building process are necessary. In this paper, we present Visus, a system designed to support the model building process and curation of ML data processing pipelines generated by AutoML systems. We describe the framework used to ground our design choices and a usage scenario enabled by Visus. Finally, we discuss the feedback received in user testing sessions with domain experts.

show abstract

“…However, the returned results highly depend on the specified query words. It is often necessary to go several rounds by verifying the search results and revising query words [1]. Motivated by the difficulties of finding usable and relevant datasets, we aim at developing a dataset recommendation model, which assists users to efficiently find more truly relevant and usable datasets than current time demanding ways.…”

Section: Introductionmentioning

confidence: 99%

Dataset Recommendation via Variational Graph Autoencoder

Altaf

Akujuobi

et al. 2019

2019 IEEE International Conference on Data Mining (ICDM)

View full text Add to dashboard Cite

This paper targets on designing a query-based dataset recommendation system, which accepts a query denoting a user's research interest as a set of research papers and returns a list of recommended datasets that are ranked by the potential usefulness for the user's research need. The motivation of building such a system is to save users from spending time on heavy literature review work to find usable datasets. We start by constructing a two-layer network: one layer of citation network, and the other layer of datasets, connected to the firstlayer papers in which they were used. A query highlights a set of papers in the citation layer. However, answering the query as a naive retrieval of datasets linked with these highlighted papers excludes other semantically relevant datasets, which widely exist several hops away from the queried papers. We propose to learn representations of research papers and datasets in the two-layer network using heterogeneous variational graph autoencoder, and then compute the relevance of the query to the dataset candidates based on the learned representations. Our ranked datasets shown in extensive evaluation results are validated to be more truly relevant than those obtained by naive retrieval methods and adoptions of existing related solutions.

show abstract

Dataset search: a survey

Cited by 128 publications

References 163 publications

Leveraging Schema Labels to Enhance Dataset Search

Leveraging Schema Labels to Enhance Dataset Search

Visus

Dataset Recommendation via Variational Graph Autoencoder

Contact Info

Product

Resources

About