Prob2FOIL: rule learner for probabilistic logic

## Project description

ProbFOIL is a probabilistic extension of FOIL that is capable of learning probabilistic rules from probabilistic data.

ProbFOIL 2.1 is a redesign of the Prob2FOIL algorithm that was introduced in https://lirias.kuleuven.be/handle/123456789/499989. It works on top of ProbLog 2.1.

If you are looking for the version used in the paper, you should check out the tag paper_version.

## Installation

ProbFOIL 2.1 requires ProbLog 2.1. You can install ProbLog by using the command:

pip install problog

ProbFOIL does not require any further installation.

## Usage

The input of ProbFOIL consists of two parts: settings and data. These are both specified in Prolog (or ProbLog) files, and they can be combined into one.

The data consists of (probabilistic) facts. The settings define

• target: the predicate we want to learn

• modes: which predicates can be added to the rules

• types: type information for the predicates

• other settings related to the data

To use:

probfoil data.pl

or, in the repository version

python probfoil/probfoil.py data.pl

Multiple files can be specified and the information in them is concatenated. (For example, it is advisable to separate settings from data).

Several command line arguments are available. Use --help to get more information.

## Settings format

### Target

The target should be specified by adding a fact learn(predicate/arity).

### Modes

The modes should be specified by adding facts of the form mode(predicate(mode1, mode2, ...), where modeX is the mode specifier for argument X. Possible mode specifiers are:

• +: the variable at this position must already exist when the literal is added

• -: the variable at this position does not exist yet in the rule (note that this is stricter than usual)

• c: a constant should be introduced here; possible value are derived automatically from the data

### Types

For each relevant predicate (target and modes) there should be a type specifier. This specifier is of the form base(predicate(type1, type2, ...), where typeX is a type identifier. Type can be identified by arbitrary Prolog atoms (e.g. person, a, etc.)

### Example generation

By default, examples are generated by quering the data for the target predicate. Negative examples can be specified by adding zero-probability facts, e.g.:

0.0::grandmother(john, mary).

Alternatively, ProbFOIL can derive negative examples automatically by taking combinations of possible values for the target arguments. Note that this can lead to a combinatorial explosion. To enable this behavior, you can specify the fact

example_mode(auto).

## Example

% Modes
mode(male(+)).
mode(parent(+,+)).
mode(parent(+,-)).
mode(parent(-,+)).

% Type definitions
base(parent(person,person)).
base(male(person)).
base(female(person)).
base(mother(person,person)).
base(grandmother(person,person)).
base(father(person,person)).
base(male_ancestor(person,person)).
base(female_ancestor(person,person)).

% Target
learn(grandmother/2).

% How to generate negative examples
example_mode(auto).

Further examples can be found in the directory examples.

## Project details

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