Abstract
We present JudgeD, a probabilistic datalog. A JudgeD program defines a distribution over a set of traditional datalog programs by attaching logical sentences to clauses to implicitly specify traditional data programs. Through the logical sentences, JudgeD provides a novel method for the expression of complex dependencies between both rules and facts. JudgeD is implemented as a proof-of-concept in the language Python. The implementation allows connection to external data sources, and features both a Monte Carlo probability approximation as well as an exact solver supported by BDDs. Several directions for future work are discussed and the implementation is released under the MIT license.
| Original language | English |
|---|---|
| Pages | - |
| Number of pages | 6 |
| Publication status | Published - 13 Feb 2016 |
| Event | Workshop on Declarative Learning Based Programming, DeLBP 2016 - Phoenix, United States Duration: 13 Feb 2016 → 13 Feb 2016 |
Workshop
| Workshop | Workshop on Declarative Learning Based Programming, DeLBP 2016 |
|---|---|
| Abbreviated title | DeLBP 2016 |
| Country/Territory | United States |
| City | Phoenix |
| Period | 13/02/16 → 13/02/16 |
| Other | In conjunction with the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) |
Keywords
- probabilistic Datalog
- EWI-26661
- METIS-315143
- IR-98966
- Datalog