Harvesting Knowledge from Cultural Images with Assorted Technologies: The Example of the ChIA Project

Dorn, Amelie; Abgaz, Yalemisew M.; Koch, Gerda; Díaz, José Luis Preza

doi:10.5771/9783956507762-470

Cited by 2 publications

(2 citation statements)

References 3 publications

(3 reference statements)

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“…The second step introduces tools and strategies for analyzing and annotating information, as well as training and assessing CV models, while the third step focuses on active learning component deployment, exploration, and integration. The proposed methodology was created as part of the ChIA project (ChIA-accessing and analyzing cultural images with new technologies) [139], which uses AI to solve problems in the digital humanities. Some artificial intelligence-based methods for the promotion, curation, and protection of cultural heritage were described in the current studies.…”

Section: B Automatic Information Extractionmentioning

confidence: 99%

Cultural Heritage Information Retrieval: Past, Present, and Future Trends

Ranjgar,

Sadeghi-Niaraki,

Shakeri

et al. 2024

IEEE Access

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Knowledge organization and development of better information retrieval techniques were of great importance from a very early time period in human history. The need has grown high for such systems with the advent of digitization and the web era. Computer systems and web have offered easier retrieval of information in almost no time. However, as the amount of data increased, these systems were not able to work well in terms of accuracy and precision of retrieval. Semantic Web concept was introduced to overcome the issue by converting the web of documents to a web of data. Semantic Web technologies makes data machine-understandable so that information retrieval can be more precise and accurate. The Cultural Heritage (CH) community, with the goal of preserving and dissemination of the historical information to people and society, is one of the first domains to adopt Semantic Web recommendations and technologies, which can provide interoperability between various organizations by creating a shared understanding in the community. The data in the CH domain differs widely with types and formats. Also, a lot of organizations and experts from various fields interact through different processes within this community. Due to the mentioned needs, the CH community employed Semantic Web technologies step by step along its evolution process for better knowledge management and a uniform understanding among the community. In this study, we present a comprehensive conceptual framework that spans cultural heritage, information modeling, and information retrieval. Our model addresses early solutions in knowledge organization systems, highlighting the evolution from classification systems and controlled vocabularies to the significance of metadata schemas. We delve into the limitations of traditional knowledge organization systems and the necessity of formal ontologies, particularly in the cultural heritage domain. The comparative analysis of CRM vs. EDM, ontology-based metadata interoperability, and ontology technologies elucidate our contributions to the field. This paper outlines the process from the initial steps of adopting Semantic Web technologies in the CH domain to the latest developments in CH information retrieval. In this paper, we also reviewed intelligent applications and services developed in the CH domain after establishing semantic data models and Knowledge Organization Systems. Finally, challenges and possible future research directions are discussed. The findings revealed that GLAMs (Galleries, Libraries, Archives, and Museums) are excellent and comprehensive sources of CH information. The CH community has put in a lot of time and effort to develop data models and knowledge organization tools; now it's time to use this valuable resource to construct smart applications that are still in their early phases. This could benefit the CH industry even more.

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Section: B Automatic Information Extractionmentioning

confidence: 99%

Cultural Heritage Information Retrieval: Past, Present, and Future Trends

Ranjgar,

Sadeghi-Niaraki,

Shakeri

et al. 2024

IEEE Access

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show abstract

“…Our methodology follows a continuous and iterative development within and across the phases. The proposed methodology is developed in the framework of the ChIA project (ChIA—accessing and analysing cultural images with new technologies) [ 18 ] where AI solutions are applied to solve problems in the digital humanities domain. The contribution of the paper includes: An end-to-end methodology and case study for semantic enrichment of cultural images.…”

Section: Introductionmentioning

confidence: 99%

A Methodology for Semantic Enrichment of Cultural Heritage Images Using Artificial Intelligence Technologies

et al. 2021

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Cultural heritage images are among the primary media for communicating and preserving the cultural values of a society. The images represent concrete and abstract content and symbolise the social, economic, political, and cultural values of the society. However, an enormous amount of such values embedded in the images is left unexploited partly due to the absence of methodological and technical solutions to capture, represent, and exploit the latent information. With the emergence of new technologies and availability of cultural heritage images in digital formats, the methodology followed to semantically enrich and utilise such resources become a vital factor in supporting users need. This paper presents a methodology proposed to unearth the cultural information communicated via cultural digital images by applying Artificial Intelligence (AI) technologies (such as Computer Vision (CV) and semantic web technologies). To this end, the paper presents a methodology that enables efficient analysis and enrichment of a large collection of cultural images covering all the major phases and tasks. The proposed method is applied and tested using a case study on cultural image collections from the Europeana platform. The paper further presents the analysis of the case study, the challenges, the lessons learned, and promising future research areas on the topic.

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Harvesting Knowledge from Cultural Images with Assorted Technologies: The Example of the ChIA Project

Cited by 2 publications

References 3 publications

Cultural Heritage Information Retrieval: Past, Present, and Future Trends

Cultural Heritage Information Retrieval: Past, Present, and Future Trends

A Methodology for Semantic Enrichment of Cultural Heritage Images Using Artificial Intelligence Technologies

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