Knowledge Mining Algorithm for Corpus Based on Graph Computing and Reasoning

The corpus contains a vast amount of valuable knowledge, but traditional methods face challenges when mining knowledge of complex relationships. This study helps graph data structures visually represent entities and relationships in the corpus, utilizes the powerful processing capabilities of graph computing, and combines reasoning mechanisms to reveal potential knowledge associations. First, preprocess the corpus, identify and extract entities and relations, and construct KG. Then, by using the graph computing algorithm, the graph structure is analyzed to explore key entities, important relationships and potential communities. On this basis, through techniques such as rule reasoning and semantic reasoning, implicit knowledge is deeply derived to enhance the depth and breadth of knowledge mining. Compared with traditional algorithms, the advantage of this algorithm lies in its ability to handle complex corpora, accurately mine multihop relationships and implicit semantics of entities, and improve the efficiency and quality of knowledge mining.