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Intangible Cultural Heritage (ICH) witnesses human creativity and wisdom in long histories, composed of a variety of immaterial manifestations. The rapid development of digital technologies accelerates the record of ICH, generating a sheer number of heterogenous data but in a state of fragmentation. To resolve that, existing studies mainly adopt approaches of knowledge graphs (KGs) which can provide rich knowledge representation. However, most KGs are text-based and text-derived, and incapable to give related images and empower downstream multimodal tasks, which is also unbeneficial for the public to establish the visual perception and comprehend ICH completely especially when they do not have the related ICH knowledge. Hence, aimed at that, we propose to, taking the Chinese nation-level ICH list as an example, construct a large-scale and comprehensive Multimodal Knowledge Graph (CICHMKG) combining text and image entities from multiple data sources and give a practical construction framework. Additionally, in this paper, to select representative images for ICH entities, we propose a method composed of the denoising algorithm (CNIFA) and a series of criteria, utilizing global and local visual features of images and textual features of captions. Extensive empirical experiments demonstrate its effectiveness. Lastly, we construct the CICHMKG, consisting of 1,774,005 triples, and visualize it to facilitate the interactions and help the public dive into ICH deeply.

A single abstract from the DHd-2020 Book of Abstracts., Sofern eine editorische Arbeit an dieser Publikation stattgefunden hat, dann bestand diese aus der Eliminierung von Bindestrichen in Überschriften, die aufgrund fehlerhafter Silbentrennung entstanden sind, der Vereinheitlichung von Namen der Autor*innen in das Schema "Nachname, Vorname" und/oder der Trennung von Überschrift und Unterüberschrift durch die Setzung eines Punktes, sofern notwendig., {"references":["https://doi.org/10.5281/zenodo.3666690","https://github.com/DHd-Verband/DHd-Abstracts-2020"]}

We summarize the results of a competition on Automated Text Recognition targeting the effective adaptation of recognition engines to essentially new data. The task consists in achieving a minimum character error rate on a previously unknown text corpus from which only a few pages are available for adjusting an already pre-trained recognition engine. This issue addresses a frequent application scenario where only a small amount of task-specific training data is available, because producing this data usually requires much effort. We present the results of five submission. They show that the task is a challenging issue but for certain documents 16 pages of transcription are sufficient to adapt a pre-trained recognition system.