Probabilistic Logic Neural Networks for Reasoning
Authors: Meng Qu, Jian Tang
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on multiple knowledge graphs prove the effectiveness of p Logic Net over many competitive baselines. |
| Researcher Affiliation | Academia | Meng Qu1,2, Jian Tang1,3,4 1Mila Quebec AI Institute 2University of Montréal 3HEC Montréal 4CIFAR AI Research Chair |
| Pseudocode | No | The paper describes the variational EM algorithm and its E-step and M-step procedures in detail, but it does not present them in a structured pseudocode block or a clearly labeled 'Algorithm' section. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of its source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | In experiments, we evaluate the p Logic Net on four benchmark datasets. The FB15k [3] and FB15k-237 [43] datasets are constructed from Freebase [2]. WN18 [3] and WN18RR [8] are constructed from Word Net [24]. The detailed statistics of the datasets are summarized in appendix. |
| Dataset Splits | No | The paper mentions observed and hidden triplets, and uses standard benchmark datasets, but it does not explicitly provide specific train/validation/test split percentages, sample counts for each split, or detail a cross-validation setup. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using Trans E [3] as the default knowledge graph embedding model, but it does not list any specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, or specific library versions). |
| Experiment Setup | Yes | To generate the candidate rules in the p Logic Net, we search for all the possible composition rules, inverse rules, symmetric rules and subrelations rules from the observed triplets, which is similar to [10, 15]. Then, we compute the empirical precision of each rule, i.e. pl = |Sl O| / |Sl| , where Sl is the set of triplets extracted by the rule l and O is the set of the observed triplets. We only keep rules whose empirical precision is larger than a threshold τrule. Trans E [3] is used as the default knowledge graph embedding model to parameterize qθ. We update the weights of logic rules with gradient descent. The detailed hyperparameters settings are available in the appendix. |