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In contemporary defense training and operations, users regularly encounter complicated and dynamic environments that generate large amounts of knowledge derived from locally acquired data. In order to facilitate collaborative decision making, users need to effectively share and distribute locally learned knowledge in a timely manner. This paper presents a semantic-based knowledge and information sharing system (S-KISS): a forum application for efficient peer-to-peer knowledge sharing. S-KISS enables simple and casual peer-to-peer information exchange, while retaining the quality of widely disseminated content for judicious knowledge consumption. Based on advanced semantic analysis technologies, S-KISS also supports effective semantic-based knowledge searching and semi-automated knowledge management with two knowledge management methods: (1) knowledge similarity searching based on WordNet and BERTScore, and (2) semantic similarity-based knowledge graph construction and knowledge grouping. The searching method focused on the semantics of text instead of word spans. Meanwhile, the grouping method constructs a knowledge graph where each node represents a posting and the links between nodes along with their semantic similarities. Postings can be grouped into multiple clusters of similar topics using Markov clustering algorithm, which allows users to look up related content quickly and effectively. The feasibility and effectiveness of S-KISS is demonstrated via a web-based prototype using practical scenarios and a real-world benchmark dataset curated from the sub-Reddit online forum ‘r/newtothenavy’. With broad and generic language models, the capabilities developed in S-KISS are applicable for knowledge information management in any space, air, sea, marine, and cyber domains. S-KISS can be utilized in other relevant software applications such as collaborative communication platforms and e-training discussion forums.
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