Similarity vs. Relevance: From Simple Searches to Complex Discovery

Skopal, Tomáš; Bernhauer, David; Škoda, Petr; Klímek, Jakub; Nečaský, Martin

doi:10.1007/978-3-030-89657-7_9

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…In addition, they also observed that BM25, the classical indicator, showed the best performance in some cases. Skopal et al [16] introduced data-transitive similarity using intermediator datasets between non-similar datasets. Bernhauer et al [10] evaluated this indicator and widely compared it with other similarity indicators based on text metadata.…”

Section: B Metadata-based Similarity Indicator Between Datasetsmentioning

confidence: 99%

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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: B Metadata-based Similarity Indicator Between Datasetsmentioning

confidence: 99%

“…Another text-based approach is data-transitive similarity, proposed by Skopal et al [16] It assumes that datasets x and y are transitively similar when they are similar to the same dataset i. The following Equation (5) shows the definition of data-transitive similarity DT(x, y):…”

Section: ) Metadata-based Similarity Indicatormentioning

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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“…We are aware that the development of an ultimate and universal method for dataset discovery would be an infeasible effort. This is based on our previous work – Škoda et al (2019), Skopal et al (2021) – where we already experimented with various similarity discovery methods. We have measured them on various real search scenarios, and we showed that none of the evaluated methods performs best on all the scenarios.…”

Section: Introductionmentioning

confidence: 99%

Modular framework for similarity-based dataset discovery using external knowledge

Nečaský

Škoda

Bernhauer

et al. 2022

DTA

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PurposeSemantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.Design/methodology/approachIn this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.FindingsThe study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.Originality/valueTo the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.

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