According to WWF, 1.1 billion people lack access to water, and 2.7 billion experience water scarcity at least one month a year. By 2025, two-thirds of the world's population may be facing water shortages. This highlights the urgency of managing water usage efficiently, especially in water-intensive sectors like food. This paper proposes a recommendation engine, powered by knowledge graphs, aiming to facilitate sustainable and healthy food consumption. The engine recommends ingredient substitutes in user recipes that improve nutritional value and reduce environmental impact, particularly water footprint. The system architecture includes source identification, information extraction, schema alignment, knowledge graph construction, and user interface development. The research offers a promising tool for promoting healthier eating habits and contributing to water conservation efforts.

Internet memes have emerged as a novel format for communication and expressing ideas on the web. Their fluidity and creative nature are reflected in their widespread use, often across platforms and occasionally for unethical or harmful purposes. While computational work has already analyzed their high-level virality over time and developed specialized classifiers for hate speech detection, there have been no efforts to date that aim to holistically track, identify, and map internet memes posted on social media. To bridge this gap, we investigate whether internet memes across social media platforms can be contextualized by using a semantic repository of knowledge, namely, a knowledge graph. We collect thousands of potential internet meme posts from two social media platforms, namely Reddit and Discord, and perform an extract-transform-load procedure to create a data lake with candidate meme posts. By using vision transformer-based similarity, we match these candidates against the memes cataloged in a recently released knowledge graph of internet memes, IMKG. We provide evidence that memes published online can be identified by mapping them to IMKG. We leverage this grounding to study the prevalence of memes on different platforms, discover popular memes, and select common meme channels and subreddits. Finally, we illustrate how the grounding can enable users to get context about memes on social media thanks to their link to the knowledge graph.

Knowledge engineering is the process of creating and maintaining Knowledge engineering (KE) is the discipline of building and maintaining knowledge-producing systems. Throughout the history of computer processes that produce knowledge. Per [31], knowledge science and AI, knowledge engineering workflows have been widely can be defined as a set of beliefs that are "(i) true, (ii) certain, (iii) used because high-quality knowledge is assumed to be crucial for obtained by a reliable process". KE workflows have been popular reliable intelligent agents. However, the landscape of knowledge throughout the evolution of computer science and AI under the engineering has changed, presenting four challenges: unaddressed intuitive assumption that the reliability of intelligent agents (e.g., stakeholder requirements, mismatched technologies, adoption barriers chatbots) strongly depends on high-quality knowledge [1, 6, 7, 11, for new organizations, and misalignment with software engineering 12, 14, 17, 19, 26, 30-32, 35]. And yet, KE as a discipline has changed practices. In this paper, we propose to address these challenges considerably since its initial flowering during the period associated by developing a reference architecture using a mainstream with expert systems development in the nineteen-eighties.