Semantic Data Retrieval and Integration for Supporting Artworks Interpretation Through Integrative Narrative Networks

Significant recent advances in AI are progressively giving Digital Humanities a range of powerful tools to analyse and contextualise artworks using techniques from computer vision, pattern recognition, ontology engineering, natural language processing, and the semantic web. These tools help to analyse artworks and link them to insightful descriptions. However, to obtain the full potential of these tools we need to tackle two issues: (i) how to integrate the fragmented and sometimes contradictory information these various tools provide, and (ii) how to make it much easier for art historians, curators, and artists to use and extend these tools. This paper addressed these questions with a focus on semantic web information retrieval and integration. It introduces a data structure called an Integrative Narrative Network (INN) that supports the integration of information from different knowledge sources, which formally represents the process of understanding as a question-answering approach. It further introduces the ongoing development of a tool by which an art historian can build up narrative networks by retrieving and selecting information queried from online available Knowledge Graphs. In particular, we show how semantic web resources can help to raise questions and find answers to them, through the real case study of a Late Renaissance artwork interpretation.