JudgeD: a probabilistic datalog with dependencies

Research output: Contribution to conferencePaper

2 Citations (Scopus)
15 Downloads (Pure)

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 languageEnglish
Pages-
Number of pages6
Publication statusPublished - 13 Feb 2016
EventWorkshop on Declarative Learning Based Programming, DeLBP 2016 - Phoenix, United States
Duration: 13 Feb 201613 Feb 2016

Workshop

WorkshopWorkshop on Declarative Learning Based Programming, DeLBP 2016
Abbreviated titleDeLBP 2016
CountryUnited States
CityPhoenix
Period13/02/1613/02/16
OtherIn conjunction with the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16)

Keywords

  • probabilistic Datalog
  • EWI-26661
  • METIS-315143
  • IR-98966
  • Datalog

Cite this

Wanders, B., van Keulen, M., & Flokstra, J. (2016). JudgeD: a probabilistic datalog with dependencies. -. Paper presented at Workshop on Declarative Learning Based Programming, DeLBP 2016, Phoenix, United States.