Ontology Engineering to Model the European Cultural Heritage: The Case of Cultural Gems

• URL: http://arxiv.org/abs/2402.07351v1
• Date: Mon, 12 Feb 2024 00:44:58 GMT
• Title: Ontology Engineering to Model the European Cultural Heritage: The Case of Cultural Gems
• Authors: Valentina Alberti, Cinzia Cocco, Sergio Consoli, Valentina Montalto, Francesco Panella
• Abstract summary: Cultural gems is a web application conceived by the European Commission's Joint Research Centre (DG JRC) The main goal is to provide a vision of European culture in order to strengthen a sense of identity within a single European cultural realm. Cultural gems maps more than 130,000 physical places in over 300 European cities and towns, and since 2020 it also lists online cultural initiatives.
• Score: 0.8324691721547202
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cultural gems is a web application conceived by the European Commission's Joint Research Centre (DG JRC), which aims at engaging people and organisations across Europe to create a unique repository of cultural and creative places. The main goal is to provide a vision of European culture in order to strengthen a sense of identity within a single European cultural realm. Cultural gems maps more than 130,000 physical places in over 300 European cities and towns, and since 2020 it also lists online cultural initiatives. The new release aims, among other, to increase the interoperability of the application. At this purpose, we provide an overview on the current development of an ontology for Cultural gems used to map cultural heritage in European cities by using Linked Open Data (LOD) standards, and making the data FAIR, that is Findable, Accessible, Interoperable, and Reusable. We provide an overview of the methodology, presenting the structure of the ontology, and the services and tools we are currently building on top.

Related papers

• Navigating the Cultural Kaleidoscope: A Hitchhiker's Guide to Sensitivity in Large Language Models [4.771099208181585]
  LLMs are increasingly deployed in global applications, ensuring users from diverse backgrounds feel respected and understood. Cultural harm can arise when these models fail to align with specific cultural norms, resulting in misrepresentations or violations of cultural values. We present two key contributions: A cultural harm test dataset, created to assess model outputs across different cultural contexts through scenarios that expose potential cultural insensitivities, and a culturally aligned preference dataset, aimed at restoring cultural sensitivity through fine-tuning based on feedback from diverse annotators.
  arXiv  Detail & Related papers  (2024-10-15T18:13:10Z)
• Beyond Aesthetics: Cultural Competence in Text-to-Image Models [34.98692829036475]
  CUBE is a first-of-its-kind benchmark to evaluate cultural competence of Text-to-Image models. CUBE covers cultural artifacts associated with 8 countries across different geo-cultural regions. CUBE-CSpace is a larger dataset of cultural artifacts that serves as grounding to evaluate cultural diversity.
  arXiv  Detail & Related papers  (2024-07-09T13:50:43Z)
• Crossroads of Continents: Automated Artifact Extraction for Cultural Adaptation with Large Multimodal Models [22.92083941222383]
  We introduce DalleStreet, a large-scale dataset generated by DALL-E 3 and validated by humans. We find disparities in cultural understanding at geographic sub-region levels with both open-source (LLaVA) and closed-source (GPT-4V) models. Our findings reveal a nuanced picture of the cultural competence of LMMs, highlighting the need to develop culture-aware systems.
  arXiv  Detail & Related papers  (2024-07-02T08:55:41Z)
• From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Models [10.121734731147376]
  Vision-language models' performance remains suboptimal on images from non-western cultures. Various benchmarks have been proposed to test models' cultural inclusivity, but they have limited coverage of cultures. We introduce the GlobalRG benchmark, comprising two challenging tasks: retrieval across universals and cultural visual grounding.
  arXiv  Detail & Related papers  (2024-06-28T23:28:28Z)
• Extrinsic Evaluation of Cultural Competence in Large Language Models [53.626808086522985]
  We focus on extrinsic evaluation of cultural competence in two text generation tasks. We evaluate model outputs when an explicit cue of culture, specifically nationality, is perturbed in the prompts. We find weak correlations between text similarity of outputs for different countries and the cultural values of these countries.
  arXiv  Detail & Related papers  (2024-06-17T14:03:27Z)
• CulturePark: Boosting Cross-cultural Understanding in Large Language Models [63.452948673344395]
  This paper introduces CulturePark, an LLM-powered multi-agent communication framework for cultural data collection. It generates high-quality cross-cultural dialogues encapsulating human beliefs, norms, and customs. We evaluate these models across three downstream tasks: content moderation, cultural alignment, and cultural education.
  arXiv  Detail & Related papers  (2024-05-24T01:49:02Z)
• CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language Technologies [53.2331634010413]
  CultureBank is a knowledge base built upon users' self-narratives. It contains 12K cultural descriptors sourced from TikTok and 11K from Reddit. We offer recommendations for future culturally aware language technologies.
  arXiv  Detail & Related papers  (2024-04-23T17:16:08Z)
• CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting [73.94059188347582]
  We uncover culture perceptions of three SOTA models on 110 countries and regions on 8 culture-related topics through culture-conditioned generations. We discover that culture-conditioned generation consist of linguistic "markers" that distinguish marginalized cultures apart from default cultures.
  arXiv  Detail & Related papers  (2024-04-16T00:50:43Z)
• Massively Multi-Cultural Knowledge Acquisition & LM Benchmarking [48.21982147529661]
  This paper introduces a novel approach for massively multicultural knowledge acquisition. Our method strategically navigates from densely informative Wikipedia documents on cultural topics to an extensive network of linked pages. Our work marks an important step towards deeper understanding and bridging the gaps of cultural disparities in AI.
  arXiv  Detail & Related papers  (2024-02-14T18:16:54Z)
• Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenship [67.5613995938273]
  We introduce commonality as a new measure that reflects the degree to which recommendations familiarize a given user population with specified categories of cultural content. Our results demonstrate that commonality captures a property of system behavior complementary to existing metrics and suggest the need for alternative, non-personalized interventions in recommender systems oriented to strengthening cultural citizenship across populations of users.
  arXiv  Detail & Related papers  (2022-08-02T19:14:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.