Human-centric Evaluation of Semantic Resources: A Systematic Mapping Study

Semantic resources such as ontologies and knowledge graphs enable intelligent applications in a broad range of application areas. As ensuring their quality is important, evaluation activities are an integral part of their life-cycle. Yet, several aspects of these resources, such as domain or modelling correctness, are not amenable to automatic evaluation and require assessment through human participation. Despite such human-centric evaluation of semantic resources (HESR) is essential, there is a lack of a systematic understanding of this area in terms of a theoretical framework as well as an overview of concrete HESR approaches and trends . We address this gap by, firstly, integrating existing literature into a theoretical framework for defining and characterising HESR thus developing a new perspective on this area. Second, we perform a Systematic Mapping Study of 15 years of work published in this area covering 144 papers in order to empirically ground and extend the theoretical framework and identify trends and best practices. These theoretical foundations , insights into current trends and practices as well as our openly available data and analysis scripts aim to enable new research in this area and offer practical guidelines for performing HESR to researchers and practitioners alike.

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Publication Details

Journal
ACM Computing Surveys
Published
2026-03-05
DOI
https://doi.org/10.1145/3800939
Primary Topic
Advanced Graph Neural Networks
Type
article
Field-Weighted Citation Impact
0.00
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article

Human-centric Evaluation of Semantic Resources: A Systematic Mapping Study

Stefani Tsaneva, María Poveda, Mari Carmen Suárez-Figueroa, Miriam Fernández et al.
ACM Computing Surveys
Advanced Graph Neural Networks
article

Human-centric Evaluation of Semantic Resources: A Systematic Mapping Study

Stefani Tsaneva, María Poveda, Mari Carmen Suárez-Figueroa, Miriam Fernández, Marta ; id_orcid 0000-0001-9301-8418 Sabou
article en

Abstract

Semantic resources such as ontologies and knowledge graphs enable intelligent applications in a broad range of application areas. As ensuring their quality is important, evaluation activities are an integral part of their life-cycle. Yet, several aspects of these resources, such as domain or modelling correctness, are not amenable to automatic evaluation and require assessment through human participation. Despite such human-centric evaluation of semantic resources (HESR) is essential, there is a lack of a systematic understanding of this area in terms of a theoretical framework as well as an overview of concrete HESR approaches and trends . We address this gap by, firstly, integrating existing literature into a theoretical framework for defining and characterising HESR thus developing a new perspective on this area. Second, we perform a Systematic Mapping Study of 15 years of work published in this area covering 144 papers in order to empirically ground and extend the theoretical framework and identify trends and best practices. These theoretical foundations , insights into current trends and practices as well as our openly available data and analysis scripts aim to enable new research in this area and offer practical guidelines for performing HESR to researchers and practitioners alike.

ACM Computing Surveys
Vienna University of Economics and Business (AT), The Open University (GB), Universidad Politécnica de Madrid (ES)
Openalex Percentile: Top 37%
Advanced Graph Neural Networks
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