ScaLeKB: scalable learning and inference over large knowledge bases

Recent years have seen a drastic rise in the construction of web knowledge bases (e.g., Freebase, YAGO, DBPedia). These knowledge bases store structured information about real-world people, places, organizations, etc. However, due to the limitations of human knowledge, web corpora, and information extraction algorithms, the knowledge bases are still far from complete. To infer the missing knowledge, we propose the Ontological Pathfinding (OP) algorithm to mine first-order inference rules from these web knowledge bases. The OP algorithm scales up via a series of optimization techniques, including a new parallel-rule-mining algorithm, a pruning strategy to eliminate unsound and inefficient rules before applying them, and a novel partitioning algorithm to break the learning task into smaller independent sub-tasks. Combining these techniques, we develop a first rule mining system that scales to Freebase, the largest public knowledge base with 112 million entities and 388 million facts. We mine 36,625 inference rules in 34 h; no existing system achieves this scale. Based on the mining algorithm and the optimizations, we develop an efficient inference engine. As a result, we infer 0.9 billion new facts from Freebase in 17.19 h. We use cross validation to evaluate the inferred facts and estimate a degree of expansion by 0.6 over Freebase, with a precision approaching 1.0. Our approach outperforms state-of-the-art mining algorithms and inference engines in terms of both performance and quality.