Scientific Item Recommendation Using a Citation Network

Wang, Xu; Harmelen, Frank van; Cochez, Michael; Huang, Zhisheng

doi:10.1007/978-3-031-10986-7_38

Cited by 2 publications

(3 citation statements)

References 28 publications

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“…In cases where the discovery target is limited to a scientific dataset attached to research papers, information recommendation approaches are predominant. In contrast to retrievalbased approaches, it does not necessarily depend on text information because it mainly uses link structures, such as citations [25]- [27] and co-author relationships [28]. As mentioned above, such types of information are only available on datasets linked to research papers.…”

Section: A Discovery Of Similar Datasetsmentioning

confidence: 99%

Metadata-Based Clustering and Selection of Metadata Items for Similar Dataset Discovery and Data Combination Tasks

Sakumoto,

Hayashi,

Sakaji

et al. 2024

IEEE Access

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Data integration, which aims to solve problems and create new services by combining datasets, has attracted considerable attention. The discovery of similar datasets that can be combined is critical. In the literature on similar dataset discovery, it is important to select an appropriate discovery method for each information need, such as the domain. However, conventional studies have evaluated discovery methods in different ways, such as domains, test datasets, and evaluation metrics. This factor prevents the appropriate method selection for each situation. Furthermore, the specific effects of the combination of different methods are not well known despite conventional studies arguing the importance of the combination. This study attempts to understand (1) the similarity indicators that should be employed for each domain and (2) the effects of a combination of different indicators on performance. We evaluated 16 inter-dataset clustering models based on different metadata-based similarity indicators, using unified evaluation metrics and datasets for 15 domains. Our results (1) suggest that similarity indicators should be used for each domain and (2) demonstrate that most of the combinations of different methods can improve clustering performance.INDEX TERMS dataset discovery, dataset similarity, clustering, data exchange platform, metadata

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Section: A Discovery Of Similar Datasetsmentioning

confidence: 99%

Metadata-Based Clustering and Selection of Metadata Items for Similar Dataset Discovery and Data Combination Tasks

Sakumoto,

Hayashi,

Sakaji

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Rather than the dataset itself, this chapter employed the descriptive metadata of the dataset (title and description) as the basis for the recommendation. Finally, in addition to the descriptive metadata, Chapter 6 and Chapter 7 proposed an approach using a co-author network and a citation network as sources for relevant datasets recommendation [109,107].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…his chapter is from the published paper in the KSEM 2022 conference [107], which is co-authored by Frank van Harmelen, Michael Cochez, and Zhisheng Huang.…”

Section: Publicationmentioning

confidence: 99%

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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Scientific Item Recommendation Using a Citation Network

Cited by 2 publications

References 28 publications

Metadata-Based Clustering and Selection of Metadata Items for Similar Dataset Discovery and Data Combination Tasks

Metadata-Based Clustering and Selection of Metadata Items for Similar Dataset Discovery and Data Combination Tasks

Scientific dataset recommendation with semantic techniques

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