Learning and Reasoning for Cultural Metadata Quality: Coupling Symbolic AI and Machine Learning over a Semantic Web Knowledge Graph to Support Museum Curators in Improving the Quality of Cultural Metadata and Information Retrieval

Bobasheva, Anna; Gandon, Fabien; Precioso, Fŕed́eric

doi:10.1145/3485844

Cited by 10 publications

(3 citation statements)

References 42 publications

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“…As a consequence, we recommend (1) a more extended reuse of Iconography and iconology in LOD existing domain-specific controlled vocabularies; (2) development of domain-specific ontologies that thoroughly cover iconography and iconology; and as a result of this, (3) either the creation of new domain data, formally expressed at a finer granularity, or the re-engineering of current data following newly developed ontologies. This recommendation is extended to current studies in the enhancement of iconographical cultural metadata, such as Bobasheva et al (2022), which focus on adding new knowledge to artistic linked open data.…”

Section: Discussionmentioning

confidence: 99%

Is dc:subject enough? A landscape on iconography and iconology statements of knowledge graphs in the semantic web

Baroncini

Sartini

Erp³

et al. 2023

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PurposeIn the last few years, the size of Linked Open Data (LOD) describing artworks, in general or domain-specific Knowledge Graphs (KGs), is gradually increasing. This provides (art-)historians and Cultural Heritage professionals with a wealth of information to explore. Specifically, structured data about iconographical and iconological (icon) aspects, i.e. information about the subjects, concepts and meanings of artworks, are extremely valuable for the state-of-the-art of computational tools, e.g. content recognition through computer vision. Nevertheless, a data quality evaluation for art domains, fundamental for data reuse, is still missing. The purpose of this study is filling this gap with an overview of art-historical data quality in current KGs with a focus on the icon aspects.Design/methodology/approachThis study’s analyses are based on established KG evaluation methodologies, adapted to the domain by addressing requirements from art historians’ theories. The authors first select several KGs according to Semantic Web principles. Then, the authors evaluate (1) their structures’ suitability to describe icon information through quantitative and qualitative assessment and (2) their content, qualitatively assessed in terms of correctness and completeness.FindingsThis study’s results reveal several issues on the current expression of icon information in KGs. The content evaluation shows that these domain-specific statements are generally correct but often not complete. The incompleteness is confirmed by the structure evaluation, which highlights the unsuitability of the KG schemas to describe icon information with the required granularity.Originality/valueThe main contribution of this work is an overview of the actual landscape of the icon information expressed in LOD. Therefore, it is valuable to cultural institutions by providing them a first domain-specific data quality evaluation. Since this study’s results suggest that the selected domain information is underrepresented in Semantic Web datasets, the authors highlight the need for the creation and fostering of such information to provide a more thorough art-historical dimension to LOD.

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Section: Discussionmentioning

confidence: 99%

Is dc:subject enough? A landscape on iconography and iconology statements of knowledge graphs in the semantic web

Baroncini

Sartini

Erp³

et al. 2023

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“…Nevertheless, these models are not pipelines, leading to an accumulation of errors. Additionally, Bobasheva et al [25] used machine learning to enhance cultural metadata and information retrieval, but the model struggles with overlapping entities and relations, which diminishes the extraction effect. In summary, the research presented above may need to be improved due to missing links, an appropriate metadata format, the relevance of datasets to cultural heritage, the availability of training sets, and the complexity of scenes.…”

Section: Introductionmentioning

confidence: 99%

Using knowledge graphs and deep learning algorithms to enhance digital cultural heritage management

Huang,

Yu,

Chu

et al. 2023

Herit Sci

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Cultural heritage management poses significant challenges for museums due to fragmented data, limited intelligent frameworks, and insufficient applications. In response, a digital cultural heritage management approach based on knowledge graphs and deep learning algorithms is proposed to address the above challenges. A joint entity-relation triple extraction model is proposed to automatically identify entities and relations from fragmented data for knowledge graph construction. Additionally, a knowledge completion model is presented to predict missing information and improve knowledge graph completeness. Comparative simulations have been conducted to demonstrate the effectiveness and accuracy of the proposed approach for both the knowledge extraction model and the knowledge completion model. The efficacy of the knowledge graph application is corroborated through a case study utilizing ceramic data from the Palace Museum in China. This method may benefit users since it provides automated, interconnected, visually appealing, and easily accessible information about cultural heritage.

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“…Bobasheva Anna combined semantic reasoning and machine learning to create tools that enable curators of visual art collections to recognize and correct annotations of artworks, and improve the relevance of content-based search results in these collections. Bobasheva Anna's research results indicated that the cross fusion between symbolic AI and machine learning can indeed provide tools to address the challenges of museum curators describing artworks and searching for related images [5]. For art exhibits in museums, Frank Steven J suggested using a new AI tool -convolutional neural networksto extend scienti c analysis to the visual features of two-dimensional artworks.…”

Section: Introductionmentioning

confidence: 99%

Application of AI Interactive Device Based on Database Management System in Multidimensional Design of Museum Exhibition Content

Cai

Younghwan

2023

Preprint

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In the current digital wave sweeping the world, museums are facing unprecedented opportunities and challenges. How to adapt to the requirements of the times and use digital technology to more effectively utilize cultural relics resources in the museum collection, display exhibition content more scientifically and reasonably, and disseminate historical and cultural knowledge more widely is an urgent problem that every museum manager needs to solve. This article analyzed the AI (Artificial Intelligence) interactive device of the database management system and explored the digital construction of museums. Based on this, simulation experiments were conducted to analyze the design of museum exhibition content in a multidimensional manner. The experimental results of this article indicated that in the design of multi-dimensional exhibition content, the satisfaction score of the experimental group visitors was 8.92; in terms of voice guidance, visitor satisfaction reached 91.98%; in terms of tourist flow management, the rationality score of personnel allocation in the experimental group was 93; in terms of intelligent recommendation systems, the experimental group achieved a satisfaction rate of 86.62% for personalized needs, which was better than the traditional control group. It showed that the application of AI interactive device based on database management system can optimize many aspects of museum Digital transformation, so as to improve the visiting experience, management efficiency and cultural value transmission.

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Learning and Reasoning for Cultural Metadata Quality: Coupling Symbolic AI and Machine Learning over a Semantic Web Knowledge Graph to Support Museum Curators in Improving the Quality of Cultural Metadata and Information Retrieval

Cited by 10 publications

References 42 publications

Is dc:subject enough? A landscape on iconography and iconology statements of knowledge graphs in the semantic web

Is dc:subject enough? A landscape on iconography and iconology statements of knowledge graphs in the semantic web

Using knowledge graphs and deep learning algorithms to enhance digital cultural heritage management

Application of AI Interactive Device Based on Database Management System in Multidimensional Design of Museum Exhibition Content

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