Accurator: Nichesourcing for Cultural Heritage

With more and more cultural heritage data being published online, their usefulness in this open context depends on the quality and diversity of descriptive metadata for collection objects. In many cases, existing metadata is not adequate for a variety of retrieval and research tasks and more specific annotations are necessary. However, eliciting such annotations is a challenge since it often requires domain-specific knowledge. Where crowdsourcing can be successfully used for eliciting simple annotations, identifying people with the required expertise might prove troublesome for tasks requiring more complex or domain-specific knowledge. Nichesourcing addresses this problem, by tapping into the expert knowledge available in niche communities. This paper presents Accurator, a methodology for conducting nichesourcing campaigns for cultural heritage institutions, by addressing communities, organizing events and tailoring a web-based annotation tool to a domain of choice. The contribution of this paper is threefold: 1) a nichesourcing methodology, 2) an annotation tool for experts and 3) validation of the methodology and tool in three case studies. The three domains of the case studies are birds on art, bible prints and fashion images. We compare the quality and quantity of obtained annotations in the three case studies, showing that the nichesourcing methodology in combination with the image annotation tool can be used to collect high quality annotations in a variety of domains and annotation tasks. A user evaluation indicates the tool is suited and usable for domain specific annotation tasks

Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding

Narrative understanding involves capturing the author's cognitive processes, providing insights into their knowledge, intentions, beliefs, and desires. Although large language models (LLMs) excel in generating grammatically coherent text, their ability to comprehend the author's thoughts remains uncertain. This limitation hinders the practical applications of narrative understanding. In this paper, we conduct a comprehensive survey of narrative understanding tasks, thoroughly examining their key features, definitions, taxonomy, associated datasets, training objectives, evaluation metrics, and limitations. Furthermore, we explore the potential of expanding the capabilities of modularized LLMs to address novel narrative understanding tasks. By framing narrative understanding as the retrieval of the author's imaginative cues that outline the narrative structure, our study introduces a fresh perspective on enhancing narrative comprehension

Crowdsourcing in cultural heritage

The aims of this study, within the framework of the Europeana Common Culture project are to: 1. Determine current and planned approaches and practices within the Europeana aggregation ecosystem in relation to crowdsourced metadata and content. 2. Investigate, as comprehensively as possible, past and existing DCH crowdsourcing initiatives across Europe, systematically describing their status and gaining a sound understanding of current practices. 3. Assess the feasibility, desirability and challenges faced in any effort to strengthen the pipeline from such initiatives to enable ingestion of their metadata or access to their content through Europeana. 4. Provide recommendations and guidelines for consideration by Europeana, aggregators and Cultural Heritage Institutions. 5. Support the creation of training materials for the Europeana ecosystem in terms of any agreed interaction with Europeana around crowdsourced assets and deliver this by suitable means (e.g. webinars, Europeana Pro). The work carried out has involved a 9 month programme (April-December 2020) consisting of desk research, , three online questionnaire surveys (to national aggregators; thematic/domain aggregators and external crowdsourcing initiatives respectively), a series of interviews and three consultative on-line events. The survey data are summarised in extensive annexes

CulturAI: Semantic Enrichment of Cultural Data Leveraging Artificial Intelligence

In this paper, we propose an innovative tool able to enrich cultural and creative spots (gems, hereinafter) extracted from the European Commission Cultural Gems portal, by suggesting relevant keywords (tags) and YouTube videos (represented with proper thumbnails). On the one hand, the system queries the YouTube search portal, selects the videos most related to the given gem, and extracts a set of meaningful thumbnails for each video. On the other hand, each tag is selected by identifying semantically related popular search queries (i.e., trends). In particular, trends are retrieved by querying the Google Trends platform. A further novelty is that our system suggests contents in a dynamic way. Indeed, as for both YouTube and Google Trends platforms the results of a given query include the most popular videos/trends, such that a gem may constantly be updated with trendy content by periodically running the tool. The system has been tested on a set of gems and evaluated with the support of human annotators. The results highlighted the effectiveness of our proposal