In decision-making scenarios, an information need arises when an agent, human, or machine needs more knowledge to decide due to a knowledge gap. Users can consciously take the initiative to acquire knowledge to fill this gap through information search tasks. User queries can be incomplete, inaccurate, and ambiguous. It occurs because part of the information needed is implicit or because the user does not fully understand the domain or the task that motivates the search. This condition is foreseen within the exploratory search approaches. Although Knowledge Graphs (KG) are recognized as information sources with great potential for data integration and exploratory search, they are incomplete by nature. Besides, Crowdsourced KGs, or KGs constructed by integrating several different information sources of varying quality, need a Trust Layer to be effective. The evaluation of knowledge truthfulness depends upon the contexts of claims and tasks being carried out or intended (purpose). This research aims to prepare and query KGs to support context-aware exploration in decision-making scenarios. The contributions include a framework for Context Augmented Knowledge Graphs-based Decision Support Systems composed of a Decision Layer, a Trust Layer, and a Knowledge Layer that operates under a Dual Open World Assumption. The Knowledge Layer comprises a Context Augmented KG (CoaKG) and a CoaKG Query Engine. CoaKG contains contextual mappings to identify explicit context and rules to infer implicit context. CoaKG Query Engine is designed as a query-answering approach that retrieves all contextualized (possible answers) from the CoaKG. Wikidata is the object of a Proof of Concept to evaluate the effectiveness of the Knowledge Layer.