Automated Rule Base Completion as Bayesian Concept Induction
Authors: Zied Bouraoui, Steven Schockaert6228-6235
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experimental results that demonstrate the effectiveness of our method. |
| Researcher Affiliation | Academia | Zied Bouraoui CRIL CNRS & Univ Artois, France zied.bouraoui@cril.fr Steven Schockaert Cardiff University, UK Schockaert S1@Cardiff.ac.uk |
| Pseudocode | No | The paper does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about releasing source code for the described methodology. |
| Open Datasets | Yes | As knowledge bases we consider several well-known OWL ontologies, which we converted to existential rule bases. In particular, we consider two large-scale open-domain ontologies: SUMO and Open Cyc. We also test the performance on a number of smaller domain-specific ontologies: Wine ontology, Economy, Transport and Vicodi. As word embedding, We used a standard pre-trained 300-dimensional, which was learned using Skip-gram on the 100B words Google News corpus. |
| Dataset Splits | Yes | To split the rule bases into training and test rules, we use 10-fold cross validation. To set the parameters of our model (and the baselines), we select 10% of the training data as validation data, and only use the remaining 90% for training the model. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, or memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'pellet reasoner' and 'Skip-gram' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | This validation data is used for selecting the parameter ยต and for choosing the number of dimensions in the vector space representations v R r (chosen from {10, 25, 50, 100}). For the classification experiments, we also tune a threshold on the probability for a rule to be predicted as valid. |