02159nas a2200205   4500000000100000000000100001008004100002260001200043653003300055653002300088653003100111100001100142700001200153700001300165245010900178856015100287490000700438520148800445020002001933       2023                            d  c2023///10aIntangible cultural heritage10aDigital humanities10aMultimodal knowledge graph1 aT. Fan1 aH. Wang1 aT. Hodel00aCICHMKG: a large-scale and comprehensive Chinese intangible cultural heritage multimodal knowledge graph  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85160098451&doi=10.1186%2fs40494-023-00927-2&partnerID=40&md5=99b2b8c49466764e0ef181fb5080cbf50 v113 aIntangible 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.  a20507445 (ISSN)