- Prepare an input directory <input_dir> with TA2 KBs, each in a sub-directory named by the TA2 run name. For example:
<input_dir>
├── GAIA_TA1.Colorado_TA2
│ └── NIST
│ └── GAIA_TA1.Colorado_TA2.ttl
└── OPERA_TA1.OPERA_TA2
└── NIST
└── OPERA_TA1.OPERA_TA2.ttl
- Prepare a SIN directory <sin_dir> with Statement of Information Need (SIN) xml files. For example:
<sin_dir>
├── T201.xml
├── T202.xml
└── T203.xml
- (Recommended) Create a virtual environment with either virtualenv or Conda.
- Install PyTorch: https://pytorch.org/get-started/locally/.
- Install other python dependencies by
pip install -r requirements.txt.
./scripts/download_models.sh --force
INPUT=<input_dir> SIN=<sin_dir> OUTPUT=<output_dir> \
./scripts/run_simple.sh <TA2_run_name> <TA3_run_name> [optional_args]
<input_dir>and<sin_dir>are as described in the Date Preparation section.<output_dir>is an output directory to write intermediate results and final hypotheses.<TA2_run_name>is the full TA2 run name, for example,GAIA_TA1.Colorado_TA2.<TA3_run_name>is the TA3 run name to be appended, for example,UTexas_1.[optional_args]include:--num_hyps <NUM_HYPS>: number of hypotheses to produce for each SIN, default = 50--max_num_hops <MAX_NUM_HOPS>: maximum number of hops to expand from a cluster seed to extract a subgraph, default = 5--min_num_eres <MIN_NUM_ERES>: minimum number of EREs to stop subgraph expansion, default = 100--min_num_stmts <MIN_NUM_STMTS>: minimum number of statements to stop subgraph expansion, default = 200--coref_compress: when specified, first compress ERE coreference on the input TA2 KB--plaus_rerank: when specified, use a plausibility classifier to rerank hypothesis seeds--device: which CUDA device to use for the neural module, default = -1 (CPU)--sin_id_prefix <SIN_ID_PREFIX>: the prefix of SIN IDs to use in naming the final hypotheses, default = AIDA_M36_TA3--force: if specified, overwrite existing output files without warning
After execution, the final hypotheses would be written to <output_dir>/<TA2_run_name>.<TA3_run_name>/, for example, <output_dir>/GAIA_TA1.Colorado_TA2.UTexas_1/. The intermediate files would be written to <output_dir>/WORKING/<TA2_run_name>.<TA3_run_name>/.
- Alternatively, you can run the pipeline with our docker image without configuring dependencies.
- The latest version of docker image (v0.4.1) can be found at: https://hub.docker.com/repository/docker/pxch/aida-utexas.
- To run the docker image:
docker run \
-v <input_dir>:/input:ro \
-v <sin_dir>:/sin:ro \
-v <output_dir>:/output \
-e INPUT=/input \
-e SIN=/sin \
-e OUTPUT=/output \
pxch/aida-utexas:0.4.1 \
<TA2_run_name> <TA3_run_name> [optional_args]
- See the Run Pipeline section for definitions of
<input_dir>,<sin_dir>,<output_dir>,<TA2_run_name>,<TA3_run_name>, and[optional_args].