Enriching Exhibition Scholarship: Reconciling Knowledge Graphs and Social Media from Newspaper Articles to Twitter

丰富展览奖学金：协调从报纸文章到 Twitter 的知识图谱和社交媒体

• 批准号: AH/W00559X/1
• 负责人: Clare Llewellyn
• 金额: $ 25.77万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=AH%2FW00559X%2F1
• 关键词: Enriching; Exhibition; Scholarship; Reconciling; Knowledge

Artwork exhibitions bring diverse cultures together, exposing artists, scholars, and the public to new experiences. These events and the subsequent interactions affect stylistic trends, art markets, and cultural perceptions. Exhibitions are difficult to study because we lack holistic, structured data about co-exhibited objects and the participation of visitors. This project brings together an international and interdisciplinary team of experts in the fields of linked open data (LOD), exhibitions, art history, and AI to create and enhance machine-accessible exhibition descriptions. The Linked Art collaboration (https://linked.art/) has developed a modern Linked Open Data-based (LOD) set of standards to describe and publish art-related knowledge, including which objects were exhibited during which exhibitions. For this data to be truly connected, and allow scholars to seamlessly cross-institutional silos, descriptions of exhibition events and art objects must be "reconciled": the descriptions of the same object can be connected together by matching identifiers in different systems at different institutions.We will combine metadata graphs and cutting-edge natural language processing techniques to rich texts such as newspaper archives and social media. We will identify and extract objects, relevant descriptive information, and audience reactions from social texts across time. We will explore national and international exhibitions using open data, and detailed knowledge from university art galleries, including engagement with a contemporary Ashmolean exhibition via social media.

艺术展览将不同的文化融合在一起，让艺术家、学者和公众获得新的体验。这些事件和随后的互动影响着风格趋势、艺术市场和文化观念。展览很难研究，因为我们缺乏关于共同展出的物品和参观者参与的整体、结构化数据。该项目汇集了一个由链接开放数据（LOD）、展览、艺术史和人工智能领域专家组成的国际跨学科团队，以创建和增强机器可访问的展览描述。互联艺术合作组织（https://linked.art/）开发了一套现代的基于互联开放数据（LOD）的标准来描述和发布与艺术相关的知识，包括哪些展品在哪些展览期间展出。为了使这些数据真正连接起来，并使学者们能够无缝地跨机构竖井，对展览事件和艺术品的描述必须“协调”：通过在不同机构的不同系统中匹配标识符，可以将同一对象的描述连接在一起。我们将把元数据图和前沿的自然语言处理技术结合到丰富的文本中，如报纸档案和社交媒体。我们将识别和提取对象，相关的描述性信息，以及观众的反应，从社会文本跨越时间。我们将利用开放数据和大学艺术画廊的详细知识探索国内和国际展览，包括通过社交媒体参与当代阿什莫尔展览。