Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Inducing Probabilistic Relational Rules from Probabilistic Examples
Authors: Luc De Raedt, Anton Dries, Ingo Thon, Guy Van den Broeck, Mathias Verbeke
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We answer two questions experimentally. |
| Researcher Affiliation | Collaboration | KU Leuven, Department of Computer Science Celestijnenlaan 200A, BE-3001 Heverlee, Belgium. Now at Siemens AG, Otto-Hahn-Ring 6, GE-81739 Munich. Now at Sirris, A. Reyerslaan 80, BE-1030 Brussels |
| Pseudocode | Yes | Algorithm 1 The Prob FOIL+ learning algorithm. |
| Open Source Code | Yes | Prob FOIL+ and the datasets used in this paper in Prob FOIL+ format can be downloaded from https://dtai.cs.kuleuven.be/software/probfoil/. |
| Open Datasets | Yes | We use BNGenerator to randomly generate a Bayesian network structure. The generated network has 45 nodes, 70 edges, a maximal degree of 6 and an induced width of 5. ... we extracted the facts for all predicates related to the sports domain from iteration 850 of the NELL knowledge base. |
| Dataset Splits | Yes | For each of these, we trained Prob FOIL, Prob FOIL+ and standard regression learners from the Weka suite on 500 training examples. The learned models are evaluated on 500 test examples... We used 3-fold cross-validation. To create the folds, for each target predicate, the facts were randomly split into 3 parts. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running experiments. It only generally states that experiments were performed. |
| Software Dependencies | No | The paper mentions using the "Weka suite" and "Prob Log2 system" but does not specify their version numbers (e.g., Weka 3.9, Prob Log2 vX.Y.Z) which are crucial for reproducibility. |
| Experiment Setup | Yes | For all predicates, the m-estimate s m value was set to 1 and the beam width to 5. The value of p for rule significance was set to 0.99. Furthermore, to avoid a bias towards the majority class, the examples are balanced, i.e., a part of the negative examples is removed. ...we also tested all settings with a high m-value (1000), and a rule significance p of 0.9 (parameter setting B). |