Rebecca Schneider: knowledge graphs and enterprise content strategy

Rebecca Schneider is an expert on taxonomy, ontology, semantic modeling, knowledge graphs and enterprise content strategy.

Community-Standards for modelling fuzziness & wobbliness in research data using Semantic Web technologies and formalisms (FuzzyWobblySW)

Ashleigh Faith: knowledge graph modeling and AI architectures

Ashleigh Faith is an expert on semantic modeling, knowledge graph construction, and AI architectures and hosts a popular YouTube channel.

EKAW-24 Tutorial: Semantic Knowledge Modeling

This tutorial covers a new approach to knowledge graph modeling based on metaphactory's visual and user-friendly interface for creating, exploring, visualizing,

ReconTraj4Drones: A Framework for the Reconstruction and Semantic Modeling of UAVs’ Trajectories on MovingPandas

Unmanned aerial vehicles (UAVs), also known as drones, are important for several application domains, such as the military, agriculture, cultural heritage documentation, surveillance, and the delivery of goods/products/services. A drone’s trajectory can be enriched with external and heterogeneous data beyond latitude, longitude, and timestamp to create its semantic trajectory, providing meaningful and contextual information on its movement data, enabling decision makers to acquire meaningful and enriched contextual information about the current situation in the field of its operation and eventually supporting simulations and predictions of high-level critical events. In this paper, we present an ontology-based, tool-supported framework for the reconstruction, modeling, and enrichment of drones’ semantic trajectories. This framework extends MovingPandas, a widely used and open-source trajectory analytics and visualization tool. The presented research extends our preliminary work on drones’ semantic trajectories by contributing (a) an updated methodology for the reconstruction of drones’ trajectories from geo-tagged photos taken by drones during their flights in cases in which flight plans and/or real-time movement data have been lost or corrupted; (b) an enrichment of the reconstructed trajectories with external data; (c) the semantic annotation of the enriched trajectories based on a related ontology; and (d) the use of SPARQL queries to analyze and retrieve knowledge related to the flight of a drone and the field of operations (context). An evaluation of the presented framework, namely, ReconTraj4Drones, was conducted against several criteria, using real and open datasets.