Abstract

not-yet-known not-yet-known not-yet-known unknown Mining logic rules from structured knowledge bases is the basis of knowledge engineering. Due to the NP-hardness of the rule mining problem, logic rules cannot be efficiently induced from knowledge bases, especially large-scale ones, and most mining techniques employ algorithmic and architectural optimizations to improve efficiency. Data-oriented optimizations have also been explored to some extent, but the data efficiency is relatively low, and the memory consumption is thus becoming a new challenge for state-of-the-art systems. In this article, we propose a compact and efficient index structure for the maintenance of the intermediate data during top-down rule mining. The index is based on a mapping from constant symbols to integers and the sorting of the mapped integers. We evaluate our method on six datasets which contain up to 160K records and are frequently used as benchmarks in knowledge engineering related tasks. The experimental results show that the proposed technique speeds up the rule mining procedure by 5x on average and reduces memory consumption by up to 70%. The space overhead of the data structure is about twice that of the indexed records, which is more than 80% lower than that of the state-of-the-art technique.