Domain-specific knowledge is necessary for critical analysis and decision-making in any scientific field. As a result, it is important that we have mechanisms for collecting and applying knowledge contributed by the larger scientific community. The current paradigm involves collecting knowledge in human-readable scientific papers across various scientific journals. Extracting useful information from these papers is a labor-intensive task and the growing population of papers makes it difficult to consider older works. The implementation of a knowledge network would allow for the automation of this process, but there is no existing pipeline for the reorganization of data collected through Ecological Momentary Assessment (EMA) into knowledge graphs. In this paper, we present EMA-KN, an automatically generated knowledge graph built using the AI-KG architecture. This architecture features state-of-the-art extraction by employing the DyGIE++ and StanfordCoreNLP tools. We test our pipeline using a dataset of 74 EMA-related papers and compare the output to that of AI-KG using a dataset of 74 CS-related papers to capture the success of knowledge graph construction. Further, we evaluate knowledge graph embedding using different metrics. Results show that our pipeline has a slightly lower performance rate than AI-KG, sacrificing triple quality for domain plug-ability. In the future, we seek to improve the system to match the performance of dedicated domain-specific solutions.