BUILDING KNOWLEDGE GRAPHS SUITABLE FOR KNOWLEDGE RECOMMENDATION: EXPERIENCE FROM SHIPBUILDING INDUSTRY

Knowledge graphs have been widely used in recent years to build recommendation systems. However, in related research, the main focus has been on improving recommendation algorithms, with less attention paid to the type of knowledge graph that is more conducive to knowledge recommendations. This paper addresses knowledge recommendation systems in the shipbuilding domain by constructing knowledge graphs in two ways and comparing their performance in knowledge recommendation. One type of knowledge graph is built based on classification tags of knowledge documents, characterized by its simplicity and sparsity; the other is constructed automatically using machine learning, linking knowledge documents together based on concepts and relationships extracted by the algorithm, featuring complexity and density. To recommend shipbuilding knowledge, a context-aware mechanism was employed, gathering information from the user's task environment and linking it to the knowledge graph. Then, using RippleNet, the system spreads the user's interests within the knowledge graph and infers the required knowledge documents. Experimental results show that the sparse knowledge graph achieved better recommendation results. We believe this is due to the human expert experience relied upon during the construction of the sparse knowledge graph, namely a knowledge classification system oriented towards knowledge applications.