  As mobile applications become increasingly integral to our daily lives,
concerns about ethics have grown drastically. Users share their experiences,
report bugs, and request new features in application reviews, often
highlighting safety, privacy, and accountability concerns. Approaches using
machine learning techniques have been used in the past to identify these
ethical concerns. However, understanding the underlying reasons behind them and
extracting requirements that could address these concerns is crucial for safer
software solution development. Thus, we propose a novel approach that leverages
a knowledge graph (KG) model to extract software requirements from app reviews,
capturing contextual data related to ethical concerns. Our framework consists
of three main components: developing an ontology with relevant entities and
relations, extracting key entities from app reviews, and creating connections
between them. This study analyzes app reviews of the Uber mobile application (a
popular taxi/ride app) and presents the preliminary results from the proposed
solution. Initial results show that KG can effectively capture contextual data
related to software ethical concerns, the underlying reasons behind these
concerns, and the corresponding potential requirements.
