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Ontological Pathfinding

Ontological Pathfinding (OP) is a scalable first-order rule mining algorithm. It achieves scalability 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, OP is the first rule mining algorithm that mines 36,625 inference rules from Freebase, the largest public knowledge base with 112 million entities and 388 million facts.

License

This repository is released under the BSD license.

If you use Ontological Pathfinding in your research, please cite our paper, Ontological Pathfinding: Mining First-Order Knowledge from Large Knowledge Bases from ACM SIGMOD 2016:

@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 ACM SIGMOD international conference on Management of data},
  year={2016},
  organization={ACM}
}

Prerequisites

  • Scala 2.10.4
  • sbt >= 0.13.7
  • Spark >= 1.5.1
  • PostgreSQL >= 9.2.3

Quick Start

To build the project, run:

~/op$ sbt assembly

Extract the dataset:

~/op$ cd data/YAGOData
~/op/data/YAGOData$ gzip -d YAGOFacts.csv.gz
~/op/data/YAGOData$ gzip -d YAGOSchema.csv.gz
~/op/data/YAGOData$ unzip YAGORules.zip

We use PostgreSQL to construct candidate rules. To correctly set up the database, you need a user, e.g., "op":

$ createuser -s -P op

and a database, e.g., "op":

$ createdb op

Variables to set in run.sh:

SPARK_PATH=${HOME}/spark-1.5.1/bin/spark-submit
JAR_PATH=target/scala-2.10/Ontological-Pathfinding-assembly-1.0.jar
MAIN_CLASS=Main
NCORES=64
DRIVER_MEMORY=400G
EXECUTOR_MEMORY=100G

POSTGRESQL_BIN=  # default to /usr/local/pgsql/bin
POSTGRESQL_HOST=localhost
POSTGRESQL_USER=op
POSTGRESQL_DB=op

Run the script:

~/op$ ./run.sh
16:55:33 [INFO] Ontological Pathfinding
16:55:33 [INFO] 1. Rule mining.
16:55:33 [INFO] 2. Knowledge expansion.
Choice: [1/2/q]1
16:55:34 [INFO] Mapping facts file "data/YAGOData/YAGOFacts.csv" to integer representation.
16:56:47 [INFO] Mapping rules file "data/YAGOData/YAGORules.csv-1" to integer representation.
16:56:47 [INFO] Partitioning KB "data/YAGOData/YAGOFacts.csv" and "data/YAGOData/YAGORules.csv-1" into subsets with max facts = 2000000 and max rules = 1000.
16:57:02 [INFO] Mining rules from "data/YAGOData/YAGOFacts.csv" and "data/YAGOData/YAGORules.csv-1."
16:57:02 [INFO] Mining partition 1/1.
16:57:21 [INFO] Writing output rules to "output-rules/1."
...
17:17:34 [INFO] Rule mining finishes.

If a KB schema schema is not provided, OP will try to generate a "universal schema"

predicate_i universal universal

for every predicate.

Finally, view the result:

~/op$ less output-rules/[1-6]/rules

Supported Rule Types

To use one of the following rule types, specify it as the --rule-type argument.

  1. p(x, y) <- q(x, y)
  2. p(x, y) <- q(y, x)
  3. p(x, y) <- q(z, x), r(z, y)
  4. p(x, y) <- q(x, z), r(z, y)
  5. p(x, y) <- q(z, x), r(y, z)
  6. p(x, y) <- q(x, z), r(y, z)
  7. p(x, y) <- q1(x, z1), q2(z2, z3), q3(z3, y)
  8. p(x, y) <- q1(x, z1), q2(z2, z3), q3(z3, z4), q4(z4, y)

To add a new rule type, you need to

  1. Add the data file to the data directory as defined in run.sh.
  2. Implement the rule construction query in op.sql.
  3. Implement the rule mining algorithm in src/main/scala/OPLearners.scala.
  4. Update INPUT_RULES_TYPES in run.sh.

Data

Acknowledgments

The ProbKB project is partially supported by NSF IIS Award # 1526753, DARPA under FA8750-12-2-0348-2 (DEFT/CUBISM), and a generous gift from Google. We also thank Dr. Milenko Petrovic and Dr. Alin Dobra for the helpful discussions on query optimization.

Contact

If you have any questions about Ontological Pathfinding, please visit the project website or contact Yang Chen, Dr. Daisy Zhe Wang, DSR Lab @ UF.