We have provided a Dockerfile that you can use to setup the environment by running the following commands:
$ docker build -t unsupervised_kg docker/.
$ docker rm unsupervised_kg
$ CURRENT_DIR=`pwd` && docker run --gpus all -d -v $CURRENT_DIR:/working_dir/ --name=unsupervised_kg unsupervised_kg:latest sleep infinity
$ docker exec -it unsupervised_kg bash
$ conda env create -f environment.yml
$ conda activate unsupervised_kg
The macrostrat_db/database_explorer.ipynb contains code to explore the database dump of the macrostrat datbase. It produces two files, a all_columns.csv which contains metadata
about all the columns and tables in the macrostrat schema and macrostrat_graph.csv which contains data for a graph about units metadata extracted from the query:
SELECT *
FROM units u
JOIN unit_liths ul
ON u.id = ul.unit_id
JOIN liths l
ON l.id = ul.lith_id
-- Linking table between unit lithologies and their attributes
-- (essentially, adjectives describing that component of the rock unit)
-- Examples: silty mudstone, dolomitic sandstone, mottled shale, muscovite garnet granite
JOIN unit_lith_atts ula
ON ula.unit_lith_id = ul.id
JOIN lith_atts la
ON ula.lith_att_id = la.id
To extract relationships from the text corpus, we utilize the REBEL model: https://github.com/Babelscape/rebel which is a seq2sel model for relationship extraction.
In the rebel_kg directory, you can use the kg_runner.py to generate a knowledge graph for a text corpus. Running python kg_runner.py --help you can see the arguments to pass to generate the kg:
usage: kg_runner.py [-h] [--directory DIRECTORY] [--file FILE] [--processes PROCESSES] [--num_files NUM_FILES] --save SAVE [--model_type MODEL_TYPE] [--model_path MODEL_PATH]
optional arguments:
-h, --help show this help message and exit
--directory DIRECTORY
The directory containing the text corpus we want to process (default: )
--file FILE The file we want to generate the kg for (default: )
--processes PROCESSES
Number of process we want running (default: 1)
--num_files NUM_FILES
Number of files in the directory we want to save (default: -1)
--save SAVE The html file we want to save the network in (default: None)
--model_type MODEL_TYPE
The type of model we want to use (default: rebel)
--model_path MODEL_PATH
The path to the model weights we want to use (default: Babelscape/rebel-large)
Alongside saving the html file, it will also save a csv file representing the knowledge graph in the same directory as the html. An example of running the command for a directory:
$ python kg_runner.py --directory /ssd/dsarda/geoarchive_datasets/filtered_geoarchive_text/ --save /ssd/dsarda/geoarchive_datasets/filtered_results/temp.html --num_files 2 --processes 2`
This will use 2 processes to procecess 2 files from the /ssd/dsarda/geoarchive_datasets/filtered_geoarchive_text/ directory and will save the kg network to /ssd/dsarda/geoarchive_datasets/filtered_results/temp.html as well as a csv file representing the kg to /ssd/dsarda/geoarchive_datasets/filtered_results/temp.csv
Similarily, to run for the provided example file, you can use the command:
$ python kg_runner.py --file example.txt --save example.html
Running this script for the sentence "The formation consists of massive and cross-bedded quartz sandstones with ferruginous concretions" produces the knowledge graph:
It also produce the following csv file:
src,type,dst,article_id,sentence
Jaguar,country,Canadian,example,"Jaguar is a Canadian-listed junior gold mining, development, and exploration company operating in Brazil with three gold mining complexes and a large land package covering approximately 20,000 ha."
junior,subclass of,mining,example,"Jaguar is a Canadian-listed junior gold mining, development, and exploration company operating in Brazil with three gold mining complexes and a large land package covering approximately 20,000 ha."
Jaguar,product or material produced,gold,example,"Jaguar is a Canadian-listed junior gold mining, development, and exploration company operating in Brazil with three gold mining complexes and a large land package covering approximately 20,000 ha."
To upload to Neo4j, we need to have a txt containing the following information:
NEO4J_URI=<uri>
NEO4J_USERNAME=<username>
NEO4J_PASSWORD=<password>
This information is generally provided by Neo4j. In the example below, we assume that this information is stored in neo4j_login.txt.
To upload the csv file produced by kg_runner, in this case example.csv, you can use the neo4j_uploader code as such:
$ python neo4j_uploader.py --login_file neo4j_login.txt --graph_file example.csv
The problem is that REBEL model generally focuses on common terms and ignores terms that are more domain specific. For example, for the sentence:
Origins of dolomite in the offshore facies of the Bonneterre Formation
This knowledge graph is generated which completely ignores the term dolomite:
Thus, we try to finetune the REBEL model so that it recognizes these terms. The rebel_finetuning directory is based on the original rebel repo.
We first generate a finetuning dataset using the Snippets API in rebel_finetuning/dataset_creator.py for the edges generated from running macrostrat_db/database_explorer.ipynb. We then finetune the dataset using rebel_finetuning/src/finetune.py and then convert it to a hugging face model using rebel_finetuning/src/checkpoint_to_model.py. To generate the kg using the finetuned model, pass the directory that the model is saved in to kg_runner.py using the model_path argument.
Running this for example text, "The formation consists of massive and cross-bedded quartz sandstones with ferruginous concretions.", we get the output knowledge graph:
where the relationships type are custom defined relationships we have included in our training set.
We noticed that even after finetuning that some of the uncommon terms were not being recognized so that we decided to use, seqrel which allows us to do entity hinting and specify the term that we care about. We first reformatted the rebel dataset using the seq_to_rel/create_dataset.py and then trained the model using seq_to_rel/training.py. This well save a model.tar.gz to an output directory and you can generate a knowledge graph using the command like this:
$ python kg_runner.py --file example.txt --save example.html --model_type seq2rel --model_path <path_to_model_zip>
where <path_to_model_zip> is the path to the model.tar.gz file. Using this on the example setence, "The formation consists of massive and cross-bedded quartz sandstones with ferruginous concretions.", gives us the knowledge graph:
We developed the following system to extract a strat relationship tree from a given piece of text:
To generate the tree on the example text, run the following command in the tree_extraction directory:
$ python tree_generator.py --data_dir macrostrat_finetuned_spanbert --txt_file tree_example.txt --save_path tree_example.json
where macrostrat_finetuned_spanbert is the path to the directory containing the model as well as the all_terms.csv. To verify that you have the correct directory,
ensure that you have the following files:
all_terms.csv config.json eval_results.txt label_mapping.json predictions.txt pytorch_model.bin test_results.txt train.log vocab.txt
For the following example txt:
the mount galen volcanics consists of basalt, andesite, dacite, and rhyolite lavas and dacite and rhyolite tuff and tuff-breccia. The Hayhook formation was named, mapped and discussed by lasky and webber (1949). the formation ranges up to at least 2500 feet in thickness.
It will generate the following tree:
{
"original_txt": "the mount galen volcanics consists of basalt, andesite, dacite, and rhyolite lavas and dacite and rhyolite tuff and tuff-breccia. The Hayhook formation was named, mapped and discussed by lasky and webber (1949). the formation ranges up to at least 2500 feet in thickness.",
"formatted_txt": "the mount galen volcanics consists of basalt, andesite, dacite, and rhyolite lavas and dacite and rhyolite tuff and tuff breccia. The Hayhook formation was named, mapped and discussed by lasky and webber 1949. the formation ranges up to at least 2500 feet in thickness.",
"words": [
"the",
"mount",
"galen",
"volcanics",
"consists",
"of",
"basalt",
",",
"andesite",
",",
"dacite",
",",
"and",
"rhyolite",
"lavas",
"and",
"dacite",
"and",
"rhyolite",
"tuff",
"and",
"tuff",
"breccia",
".",
"The",
"Hayhook",
"formation",
"was",
"named",
",",
"mapped",
"and",
"discussed",
"by",
"lasky",
"and",
"webber",
"1949",
".",
"the",
"formation",
"ranges",
"up",
"to",
"at",
"least",
"2500",
"feet",
"in",
"thickness",
"."
],
"strats": [
{
"term_type": "strat_name",
"term_tokens": [
"mount",
"galen",
"volcanics"
],
"occurences": [
[
1,
4
]
],
"txt_range": [
[
4,
25
]
],
"children": [
{
"child_probability": "0.9997174",
"child": {
"term_type": "lith",
"term_tokens": [
"basalt"
],
"occurences": [
[
6,
7
]
],
"txt_range": [
[
38,
44
]
]
}
},
{
"child_probability": "0.99978334",
"child": {
"term_type": "lith",
"term_tokens": [
"andesite"
],
"occurences": [
[
8,
9
]
],
"txt_range": [
[
46,
54
]
]
}
},
{
"child_probability": "0.99986625",
"child": {
"term_type": "lith",
"term_tokens": [
"dacite"
],
"occurences": [
[
10,
11
]
],
"txt_range": [
[
56,
62
]
]
}
},
{
"child_probability": "0.99990284",
"child": {
"term_type": "lith",
"term_tokens": [
"dacite"
],
"occurences": [
[
16,
17
]
],
"txt_range": [
[
87,
93
]
]
}
},
{
"child_probability": "0.9998845",
"child": {
"term_type": "lith",
"term_tokens": [
"tuff"
],
"occurences": [
[
19,
20
]
],
"txt_range": [
[
107,
111
]
],
"children": [
{
"child_probability": "-1.0",
"child": {
"term_type": "att_amod",
"term_tokens": [
"rhyolite"
],
"occurences": [
[
18,
19
]
],
"txt_range": [
[
98,
106
]
]
}
}
]
}
},
{
"child_probability": "0.99982506",
"child": {
"term_type": "lith",
"term_tokens": [
"tuff"
],
"occurences": [
[
21,
22
]
],
"txt_range": [
[
116,
120
]
]
}
},
{
"child_probability": "0.99983096",
"child": {
"term_type": "lith",
"term_tokens": [
"breccia"
],
"occurences": [
[
22,
23
]
],
"txt_range": [
[
121,
128
]
]
}
},
{
"child_probability": "0.99974006",
"child": {
"term_type": "lith_NOUN",
"term_tokens": [
"lavas"
],
"occurences": [
[
14,
15
]
],
"txt_range": [
[
77,
82
]
],
"children": [
{
"child_probability": "-1.0",
"child": {
"term_type": "att_amod",
"term_tokens": [
"rhyolite"
],
"occurences": [
[
13,
14
]
],
"txt_range": [
[
68,
76
]
]
}
}
]
}
}
]
},
{
"term_type": "strat_name",
"term_tokens": [
"Hayhook",
"formation"
],
"occurences": [
[
24,
27
],
[
39,
41
]
],
"txt_range": [
[
130,
151
],
[
210,
223
]
]
}
]
}