A knowledge graph (KG) is a machine-readable structured representation of knowledge consisting of entities (entity and entity type) and relationships in various forms (e.g., labeled property graphs and resource description frameworks (RDFs)) (Sheth et al., 2019b). KiL based on Machine Learning/Deep Learning seamlessly integrates external knowledge to address challenging problems in low-resource and open-domain natural language processing tasks and domain-specific problems. Domain-specific problems require the application of task-specific knowledge (implicit or explicit) to generic AI models. For example, to detect emerging events in a stream of crisis-related posts (e.g., Hurricane, COVID-19 Pandemic), a generic language model (e.g., Word2Vec Mikolov et al., 2013, BERT) can be fine tuned using the concepts and relationships found in disaster ontology (e.g., empathi from Gaur et al., 2019a). Lowresource problems are characterized by having few labeled samples, making further labeling difficult in terms of effort, quality, and time. For instance, annotating millions of posts from users in various mental health communities on Reddit would require (a) establishing guidelines for annotation, (b) training annotators, (c) resolving annotation conflicts, and (d) enriching quality over multiple iterations to achieve high annotator agreement. A study by Gaur et al. (2021b) proposed a KiL pipeline to annotate such extensive social data at scale, shifting the human role from annotators to evaluators.