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TLogic
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# TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs This repository contains the code for the paper [TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs](https://arxiv.org/abs/2112.08025). <h3> How to run </h3> The dependencies required to run the code are specified in [`pyproject.toml`](https://github.com/liu-yushan/TLogic/blob/main/pyproject.toml). Run `poetry install` to install the dependencies from [`poetry.lock`](https://github.com/liu-yushan/TLogic/blob/main/poetry.lock). For more information about Poetry, a tool for dependency management and packaging in Python, see https://python-poetry.org/docs/. The commands for running TLogic and recreating the results from the paper can be found in [`run.txt`](https://github.com/liu-yushan/TLogic/blob/main/mycode/run.txt). [`demo.ipynb`](https://github.com/liu-yushan/TLogic/blob/main/mycode/demo.ipynb) contains a demonstration of the components rule learning and rule application. <h3> Datasets </h3> Each event in the temporal knowledge graph is written in the format `subject predicate object timestamp`, with tabs as separators. The dataset is split into `train.txt`, `valid.txt`, and `test.txt`, where we use the same split as provided by [Han et al.](https://github.com/TemporalKGTeam/xERTE) The files `entity2id.json`, `relation2id.json`, `ts2id.json` define the mapping of entities, relations, and timestamps to their corresponding IDs, respectively. The file `statistics.yaml` summarizes the statistics of the dataset and is not needed for running the code. <h3> Parameters </h3> In `learn.py`: `--dataset`, `-d`: str. Dataset name. `--rule_lengths`, `-l`: int. Length(s) of rules that will be learned, e.g., `2`, `1 2 3`. `--num_walks`, `-n`: int. Number of walks that will be extracted during rule learning. `--transition_distr`: str. Transition distribution; either `unif` for uniform distribution or `exp` for exponentially weighted distribution. `--num_processes`, `-p`: int. Number of processes to be run in parallel. `--seed`, `-s`: int. Random seed for reproducibility. In `apply.py`: `--dataset`, `-d`: str. Dataset name. `--test_data`: str. Data for rule application; either `test` for test set or any other string for validation set. `--rules`, `-r`: str. Name of the rules file. `--rule_lengths`, `-l`: int. Length(s) of rules that will be applied, e.g., `2`, `1 2 3`. `--window`, `-w`: int. Size of the time window before the query timestamp for rule application. `--top_k`: int. Minimum number of candidates. The rule application stops for a query if this number is reached. `--num_processes`, `-p`: int. Number of processes to be run in parallel. In `evaluate.py`: `--dataset`, `-d`: str. Dataset name. `--test_data`: str. Data for rule application; either `test` for test set or any other string for validation set. `--candidates`, `-c`: str. Name of the candidates file.
