Recent years have seen a drastic rise in the construction of webscale knowledge bases (e.g., Freebase, YAGO, DBPedia). These knowledge bases store structured information about real-world people, places, organizations, etc. However, due to limitations of human knowledge and information extraction algorithms, these knowledge bases are still far from complete. In this paper, we study the problem of mining first-order inference rules to facilitate knowledge expansion. We propose the Ontological Pathfinding algorithm (OP) that scales to web-scale knowledge bases via a series of parallelization and optimization techniques: a relational knowledge base model to apply inference rules in batches, a new rule mining algorithm that parallelizes the join queries, a novel partitioning algorithm to break the mining tasks into smaller independent sub-tasks, and a pruning strategy to eliminate unsound and resource-consuming rules before applying them. Combining these techniques, we develop the 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 hours; no existing approach achieves this scale.
Authors:
Yang Chen, Sean Goldberg, Daisy Zhe Wang, Soumitra Siddharth Johri
Bibtex:
@inproceedings{chen2016ontological,
title={Ontological Pathfinding: Mining First-Order Knowledge from Large Knowledge Bases},
author={Chen, Yang and Goldberg, Sean and Wang, Daisy Zhe and Johri, Soumitra Siddharth},
booktitle={Proceedings of the 2016 International Conference on Management of Data},
pages={835--846},
year={2016},
organization={ACM}
}
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