Semantic Web and Creative AI -- A Technical Report from ISWS 2023

The International Semantic Web Research School (ISWS) is a week-long intensive program designed to immerse participants in the field. This document reports a collaborative effort performed by ten teams of students, each guided by a senior researcher as their mentor, attending ISWS 2023. Each team provided a different perspective to the topic of creative AI, substantiated by a set of research questions as the main subject of their investigation. The 2023 edition of ISWS focuses on the intersection of Semantic Web technologies and Creative AI. ISWS 2023 explored various intersections between Semantic Web technologies and creative AI. A key area of focus was the potential of LLMs as support tools for knowledge engineering. Participants also delved into the multifaceted applications of LLMs, including legal aspects of creative content production, humans in the loop, decentralised approaches to multimodal generative AI models, nanopublications and AI for personal scientific knowledge graphs, commonsense knowledge in automatic story and narrative completion, generative AI for art critique, prompt engineering, automatic music composition, commonsense prototyping and conceptual blending, and elicitation of tacit knowledge. As Large Language Models and semantic technologies continue to evolve, new exciting prospects are emerging: a future where the boundaries between creative expression and factual knowledge become increasingly permeable and porous, leading to a world of knowledge that is both informative and inspiring.

A Planet Scale Spatial-Temporal Knowledge Graph Based On OpenStreetMap And H3 Grid

Geospatial data plays a central role in modeling our world, for which OpenStreetMap (OSM) provides a rich source of such data. While often spatial data is represented in a tabular format, a graph based representation provides the possibility to interconnect entities which would have been separated in a tabular representation. We propose in our paper a framework which supports a planet scale transformation of OpenStreetMap data into a Spatial Temporal Knowledge Graph. In addition to OpenStreetMap data, we align the different OpenStreetMap geometries on individual h3 grid cells. We compare our constructed spatial knowledge graph to other spatial knowledge graphs and outline our contribution in this paper. As a basis for our computation, we use Apache Sedona as a computational framework for our Spatial Temporal Knowledge Graph construction