In this paper, we apply graph machine learning methods to predict unseen interactions within the Weapons of Mass Destruction (WMD) dataset, developed by DARPA and IARPA.This dataset captures complex online activities, including sales, purchases, and forum discussions, with a focus on topics such as weapons, explosives, and other sensitive subjects. We represent the data as a knowledge graph, where nodes correspond to entities and edges denote relationships between them. Among various knowledge graph embedding techniques and graph neural networks, semantic matching models like DistMult demonstrate the ability to accurately predict 84% of relations, particularly due to their strength in capturing the one-to-many relationships common in the WMD data. To streamline the analysis, we implement an automated pipeline that stores the knowledge graph in a Neo4j database, extracts subgraphs using Cypher queries, trains knowledge graph embedding models on these subgraphs, predicts links, and reintegrates high-confidence edges back into the main graph.