Towards a Framework for Seismic Data.

Over the past decade, Knowledge Graphs (KGs) gained significant attention as a powerful method for knowledge representation. Driven by increasing interest, a paradigm shift has occurred, where the technology of KGs has transitioned from the research domain to the industry and public sector, with companies and organizations increasingly representing their data as Linked Open Data, gaining in that way significant traction for this technology. This paper focuses on KGs in the context of environmental challenges. More specifically, this work concerns KGs that contain seismic event data, such as location, timestamp, magnitude, depth, target date, as well as images before and after the event occurrence. Moreover, a Natural Language Processing (NLP) module is integrated to enhance the KG. That module enables users to query for seismic events in a free-text manner, before addressing them with a relevant response through a dedicated Information Retrieval (IR) component. The KG was constructed with data retrieved from the Instituto Nazionale di Geofisica e Vulcanologia , a rich resource that comes with earthquake-related information, such as magnitude, depth, occurrence location, and timestamp. Additionally, public APIs from the Copernicus Open Access Data Hub and ONDA DIAS are leveraged to provide access to sentinel data, such as images of the event location before and after its occurrence.