|
|
--- |
|
|
license: mit |
|
|
--- |
|
|
# Systematic, Multipath, disjunctive Spatio-Temporal Reasoning (STaR) benchmark |
|
|
|
|
|
## Main idea |
|
|
This is part of the [paper](https://huggingface.co/papers/2407.17396) that was published in ICLR 2025 where further details can be found. |
|
|
|
|
|
The main idea is to expand the concept of binary relational composition to not just be atomic and require that a model reason over multiple |
|
|
paths instead of a single one in contrast with previous art. |
|
|
|
|
|
This can be seen from the example below contrasting a popular previous benchmark. |
|
|
|
|
|
 |
|
|
|
|
|
|
|
|
## Details |
|
|
The layout is the same for each dataset in the benchmark. There are two axes of complexity |
|
|
|
|
|
1. **k**: the path length between the source and target node and |
|
|
2. **b**: the number of paths between the source and target nodes |
|
|
|
|
|
Each dataset has the following rows: |
|
|
|
|
|
| Edge Index | Edge Labels | Query Edge | Query Label | |
|
|
|------------|-------------|------------|-------------| |
|
|
| [(0,1), ...] | EQ | (0,5) | TPP | |
|
|
|
|
|
- **Edge Index**: `List[Tuple[int, int]]`. A list of edges characterizing the graph. |
|
|
- **Edge Labels**: `List[str]`. A list of edge labels corresponding to the edge index. |
|
|
- **Query Edge**: `Tuple[int, int]`. The target edge that a model needs to predict. |
|
|
- **Query Label**: `str`. The label corresponding to the query edge. |
|
|
|
|
|
The training sets marked by `train_*.csv` for RCC-8 and Interval algebra (the semigroups that constitute the datasets) contain small graphs of $k=2,3,4$ and $b=1,2,3$. |
|
|
|
|
|
The rest of the datasets are for testing systematic generalization. The target classes are balanced. |
