Recommending Datasets for Scientific Problem Descriptions

Färber, Michael; Leisinger, Ann-Kathrin

doi:10.1145/3459637.3482166

Cited by 7 publications

(6 citation statements)

References 24 publications

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“…Dataset recommendation is also a popular research trend in recent years. Farber and Leisinger recommended suitable dataset for given research problem description [14]. Patra et.…”

Section: Related Workmentioning

confidence: 99%

Scientific Item Recommendation Using a Citation Network

Wang

Harmelen

Cochez

et al. 2022

Knowledge Science, Engineering and Management

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“…Dataset recommendation is also a popular research trend in recent years. Farber and Leisinger recommended suitable dataset for given research problem description [14]. Patra et.…”

Section: Related Workmentioning

confidence: 99%

Scientific Item Recommendation Using a Citation Network

Wang

Harmelen

Cochez

et al. 2022

Knowledge Science, Engineering and Management

View full text Add to dashboard Cite

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“…There are several other interesting works on dataset recommendation. Michael et al [15] propose a system that recommends suitable datasets based on a given research problem description, which achieved an F1 score of 0.75 and a user satisfaction score of 0.88 on real world data. Chen et al [8] study the problem of recommending the appropriate datasets for authors, by using a multi-layer network learning model on the information from a three-layered network composed by authors, papers, and datasets, and achieved an F1 score at 3 of 0.54, dropping to 0.28 for F1 at 10.…”

Section: Introductionmentioning

confidence: 99%

Recommending scientific datasets using author networks in ensemble methods

Wang

Harmelen

Heringa

2022

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Open access to datasets is increasingly driving modern science. Consequently, discovering such datasets is becoming an important functionality for scientists in many different fields. We investigate methods for dataset recommendation: the task of recommending relevant datasets given a dataset that is already known to be relevant. Previous work has used meta-data descriptions of datasets and interest profiles of authors to support dataset recommendation. In this work, we are the first to investigate the use of co-author networks to drive the recommendation of relevant datasets. We also investigate the combination of such co-author networks with existing methods, resulting in three different algorithms for dataset recommendation. We obtain experimental results on a realistic corpus which show that only the ensemble combination of all three algorithms achieves sufficiently high precision for the dataset recommendation task.

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“…With the help of information retrieval methods, Braja et al make practical dataset recommendations in the biological domain by focusing on the datasets content, and considering researchers themselves as starting point of recommendation [75]. Färber and Leisinger provided a new dataset recommendation system using a research question as the description given to a dataset search engine, to solve the limitation of keyword-queries [30]. Altaf et al provided a dataset recommendation system based on user's interests, where user's interests is represented by a collection of papers, and returns a ranked list of datasets according to the user's research need [2].…”

Section: Topic Of This Thesis: Dataset Recommendationmentioning

confidence: 99%

“…There are several other interesting works on dataset recommendation. Michael et al [30] propose a system that recommends suitable datasets based on a given research problem description. Chen et al [15] study the problem of recommending the appropriate datasets for authors, by using a multi-layer network learning model on the information from a three-layered network composed by authors, papers, and datasets.…”

Section: Related Work and Motivationmentioning

confidence: 99%

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Scientific dataset recommendation with semantic techniques

Wang¹

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Scientific datasets are important in scientific research. Researchers always want to find a way to improve their research. Data discovery and reuse is a recent and popular way of finding more scientific datasets to help researchers. There is much current research work for data discovery and reuse. Nevertheless, most of these works are limited because researchers only trust and reuse data through social networks (such as colleagues or collaborators). Also, the scientific data from social networks bring limitations. For instance, they will only recommend data that they consider helpful. However, one scientist's useful data sometimes might be another's noisy data. A dataset search engine is a solution to help researchers find data outside their social networks by exploring scientific datasets in open data repositories or hubs. Such a dataset search engine typically returns related or relevant datasets based on a keyword query from researchers. Several existing dataset search engines already provide millions of open datasets from open-access sources. However, the performance of a dataset search engine is highly related to the quality of the keyword queries submitted by researchers. For instance, two different queries with the same meaning would get different returns from the same dataset search engine. The main question of this thesis is how to find more relevant scientific datasets from a query on a dataset search engine. A new idea is provided as the solution with dataset recommendation, which is adding one step, "if you like this dataset, do you also like these other datasets," after researchers or users submitted a query to a dataset search engine. Users submit keywords for query to a dataset search engine and then get returned list of datasets. Users can then tell the dataset search engine which returned dataset they like, and the dataset search engine will say, "If you like this dataset, you would also like those other datasets," to recommend more datasets. This thesis aims to help dataset search engines with such scientific dataset recommendations using Semantic Web technologies. As mentioned, the main idea of this thesis is "if you like this dataset, you would also like that other dataset". This thesis states the challenges and possibilities of using Semantic Web technologies on scientific dataset recommendation, as well as explores 1) how ontologies can be used for dataset recommendation, and 2) how knowledge graphs can be used for dataset recommendation? These two questions are addressed in six chapters. A scientific item (dataset or paper) recommendation benchmark was provided during the exploring work of this thesis to provide a standard for evaluating scientific item recommendation methods. This benchmark contains a large-scale corpus with millions of datasets and papers as well as the trusted links between them. Also, a collection of entity embeddings was created in the work of this thesis. These embeddings are scientific item embeddings by using knowledge graph embedding on the citation network from Microsoft Academic knowledge graph.

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Recommending Datasets for Scientific Problem Descriptions

Cited by 7 publications

References 24 publications

Scientific Item Recommendation Using a Citation Network

Scientific Item Recommendation Using a Citation Network

Recommending scientific datasets using author networks in ensemble methods

Scientific dataset recommendation with semantic techniques

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