Linked Open Images: Visual similarity for the Semantic Web

Klic, Lukas

doi:10.3233/sw-212893

2022

DOI: 10.3233/sw-212893

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Linked Open Images: Visual similarity for the Semantic Web

Lukas Klic¹

Abstract: This paper presents ArtVision, a Semantic Web application that integrates computer vision APIs with the ResearchSpace platform, allowing for the matching of similar artworks and photographs across cultural heritage image collections. The field of Digital Art History stands to benefit a great deal from computer vision, as numerous projects have already made good progress in tackling issues of visual similarity, artwork classification, style detection, gesture analysis, among others. Pharos, the International Co… Show more

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Cited by 6 publications

References 16 publications

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Mapping the landscape of digital cultural heritage research: a quantitative and visual bibliometric study

Huang,

He,

Lian

et al. 2024

LHT

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PurposeThis study explores the deep integration of digital technology and cultural heritage to promote the preservation and inheritance of cultural heritage. Focusing on Digital Cultural Heritage (DCH), this research investigates its key role in activating theoretical research and practical applications in cultural heritage.Design/methodology/approachThis study conducted an extensive bibliometric analysis utilizing VOSviewer and Bibliometrix visualization software to meticulously examine DCH research. Insights were gleaned from a dataset comprising 2,997 DCH-related publications harvested from the Web of Science database.FindingsThe bibliometric analysis reveals several notable findings: driven by active contributions from Italy, China, Spain, and the USA, the number of DCH publications shows a linear upward trend. Consiglio Nazionale delle Ricerche in Italy emerges as a prominent institution, while the Journal of Cultural Heritage stands out as the most influential journal in the DCH field. Scholars such as Remondino, Guidi, Barazzetti, and Carrozzino have significantly impacted DCH research. Furthermore, an in-depth analysis of keyword co-occurrence networks elucidates six major research trajectories in the DCH field, covering various aspects from cultural heritage digitization to digital humanities.Practical implicationsThe study emphasizes the value of global knowledge exchange, interdisciplinary collaboration, innovative technology applications, and digital content provision practices in advancing DCH research.Originality/valueBy delving into the multifaceted landscape of DCH research, this study brings forth original insights into the escalating trends, pivotal contributors, and burgeoning research directions.

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Mapping the landscape of digital cultural heritage research: a quantitative and visual bibliometric study

Huang,

He,

Lian

et al. 2024

LHT

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Critical-Reflective Human-AI Collaboration: Exploring Computational Tools for Art Historical Image Retrieval

Glinka,

Müller-Birn

2023

Proc. ACM Hum.-Comput. Interact.

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Just as other disciplines, the humanities explore how computational research approaches and tools can meaningfully contribute to scholarly knowledge production. Building on related work from the areas of CSCW and HCI, we approach the design of computational tools through the analytical lens of 'human-AI collaboration.' Such work investigates how human competencies and computational capabilities can be effectively and meaningfully combined. However, there is no generalizable concept of what constitutes 'meaningful' human-AI collaboration. In terms of genuinely human competencies, we consider criticality and reflection as guiding principles of scholarly knowledge production and as deeply embedded in the methodologies and practices of the humanities. Although (designing for) reflection is a recurring topic in CSCW and HCI discourses, it has not been centered in work on human-AI collaboration. We posit that integrating both concepts is a viable approach to supporting 'meaningful' human-AI collaboration in the humanities and other qualitative, interpretivist, and hermeneutic research areas. Our research, thus, is guided by the question of how critical reflection can be enabled in human-AI collaboration. We address this question with a use case that centers on computer vision (CV) tools for art historical image retrieval. Specifically, we conducted a qualitative interview study with art historians to explore a) what potentials and affordances art historians ascribe to human-AI collaboration and CV in particular, and b) in what ways art historians conceptualize critical reflection in the context of human-AI collaboration. We extended the interviews with a think-aloud software exploration. We observed and recorded participants' interaction with a ready-to-use CV tool in a possible research scenario. We found that critical reflection, indeed, constitutes a core prerequisite for 'meaningful' human-AI collaboration in humanities research contexts. However, we observed that critical reflection was not fully realized during interaction with the CV tool. We interpret this divergence as supporting our hypothesis that computational tools need to be intentionally designed in such a way that they actively scaffold and support critical reflection during interaction. Based on our findings, we suggest four empirically grounded design implications for 'critical-reflective human-AI collaboration': supporting reflection on the basis of transparency, foregrounding epistemic presumptions, emphasizing the situatedness of data, and strengthening interpretability through contextualized explanations.

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VeriDKG: A Verifiable SPARQL Query Engine for Decentralized Knowledge Graphs

Zhou,

Guo,

Hong

et al. 2023

Proc. VLDB Endow.

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The ability to decentralize knowledge graphs (KG) is important to exploit the full potential of the Semantic Web and realize the Web 3.0 vision. However, decentralization also renders KGs more prone to attacks with adverse effects on data integrity and query verifiability. While existing studies focus on ensuring data integrity, how to ensure query verifiability - thus guarding against incorrect, incomplete, or outdated query results - remains unsolved. We propose VeriDKG, the first SPARQL query engine for decentralized knowledge graphs (DKG) that offers both data integrity and query verifiability guarantees. The core of VeriDKG is the RGB-Trie, a new blockchain-maintained authenticated data structure (ADS) facilitating correctness proofs for SPARQL query results. VeriDKG enables verifiability of subqueries by gathering global index information on subgraphs using the RGB-Trie, which is implemented as a new variant of the Merkle prefix tree with an RGB color model. To enable verifiability of the final query result, the RGB-Trie is integrated with a cryptographic accumulator to support verifiable aggregation operations. A rigorous analysis of query verifiability in VeriDKG is presented, along with evidence from an extensive experimental study demonstrating its state-of-the-art query performance on the largeRDFbench benchmark.

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Linked Open Images: Visual similarity for the Semantic Web

Cited by 6 publications

References 16 publications

Mapping the landscape of digital cultural heritage research: a quantitative and visual bibliometric study

Mapping the landscape of digital cultural heritage research: a quantitative and visual bibliometric study

Critical-Reflective Human-AI Collaboration: Exploring Computational Tools for Art Historical Image Retrieval

VeriDKG: A Verifiable SPARQL Query Engine for Decentralized Knowledge Graphs

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