Heri-Graphs: A Workflow of Creating Datasets for Multi-modal Machine Learning on Graphs of Heritage Values and Attributes with Social Media
Values (why to conserve) and Attributes (what to conserve) are essential
concepts of cultural heritage. Recent studies have been using social media to
map values and attributes conveyed by public to cultural heritage. However, it
is rare to connect heterogeneous modalities of images, texts, geo-locations,
timestamps, and social network structures to mine the semantic and structural
characteristics therein. This study presents a methodological workflow for
constructing such multi-modal datasets using posts and images on Flickr for
graph-based machine learning (ML) tasks concerning heritage values and
attributes. After data pre-processing using state-of-the-art ML models, the
multi-modal information of visual contents and textual semantics are modelled
as node features and labels, while their social relationships and
spatiotemporal contexts are modelled as links in Multi-Graphs. The workflow is
tested in three cities containing UNESCO World Heritage properties - Amsterdam,
Suzhou, and Venice, which yielded datasets with high consistency for
semi-supervised learning tasks. The entire process is formally described with
mathematical notations, ready to be applied in provisional tasks both as ML
problems with technical relevance and as urban/heritage study questions with
societal interests. This study could also benefit the understanding and mapping
of heritage values and attributes for future research in global cases, aiming
at inclusive heritage management practices.