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ZSI_Reconnect_China
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added new keywords including refined syntax

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radvanyimome 2 years ago
parent 57952c8dc0
commit fd24b72359
7 changed files with 2715 additions and 632 deletions
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1
.gitignore vendored
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@ -1 +1,2 @@
/PATSTAT/EU_CH_scope/cpc_defs.csv
/misc_code/

40
WOS/ai_scope_keywords.txt
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@ -153,11 +153,10 @@ markov chain,
markov process,
markov decision process,
monte carlo method,
bayesian interference,
bayesian inference,
kernel method,
eigendecomposition,
eigen decomposition,
kernel method,
radial basis function,
QR decomposition,
LU decomposition,
@ -169,4 +168,39 @@ convex optimization,
nonlinear optimization,
L? regulari*,
ridge regression,
gaussian process
gaussian process,
manifold learning,
locally linear embedding*,
vector database*,
vector embedding*,
text mining,
human-robot interact*,
semantic web*,
fuzzy set*,
face recognition &! brain,
object detection &! brain,
multi agent system*,
speech recognition &! brain,
brain computer interface,
intelligent robot*,
remote sensing,
image reconstruction,
representation learning,
data augmentation,
adversarial robustness,
meta learning,
learning system,
adversarial training,
adversarial example*,
generative model*,
large language model*,
few shot learning,
image representation,
optimization algorithm,
swarm optimization,
variational inference,
kalman network*,
knowledge distillation,
kernel learning,
classifier,
lasso regression

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WOS/wos_extract/geckodriver.log
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WOS/wos_extract/wos_query_generator_simplesyntax.ipynb
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WOS/wos_extract/wos_records_concatter.ipynb
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WOS/wos_extract/wos_search_kw_analysis.ipynb
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@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 72,
"execution_count": 1,
"metadata": {
"collapsed": true
},
@ -16,7 +16,7 @@
},
{
"cell_type": "code",
"execution_count": 73,
"execution_count": 2,
"outputs": [],
"source": [
"agg_df = pd.DataFrame()\n",
@ -39,7 +39,7 @@
},
{
"cell_type": "code",
"execution_count": 74,
"execution_count": 3,
"outputs": [],
"source": [
"agg_df[\"region\"] = agg_df[\"query\"].apply(lambda x: \"EU+China\" if \"CU\" in x else \"Global\")\n",
@ -52,7 +52,7 @@
},
{
"cell_type": "code",
"execution_count": 83,
"execution_count": 4,
"outputs": [],
"source": [
"agg_df = agg_df[~agg_df[\"Record Count\"].isna()]"
@ -63,14 +63,14 @@
},
{
"cell_type": "code",
"execution_count": 62,
"execution_count": 5,
"outputs": [
{
"data": {
"text/plain": " query Record Count\n0 CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST... 972.0\n1 CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST... 451.0\n2 CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST... 12.0\n3 CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST... 5.0\n4 CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST... 2631.0\n.. ... ...\n275 TS=(\"ubiquitous computing\") AND PY=(2011-2022) 3655.0\n276 TS=(\"unstructured data*\") AND PY=(2011-2022) 3386.0\n277 TS=(\"unsupervised deep learning\") AND PY=(2011... 728.0\n278 TS=(\"word embedding*\") AND PY=(2011-2022) 7068.0\n279 TS=(\"word vector*\") AND PY=(2011-2022) 1747.0\n\n[280 rows x 2 columns]",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>query</th>\n <th>Record Count</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...</td>\n <td>972.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...</td>\n <td>451.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...</td>\n <td>12.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...</td>\n <td>5.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...</td>\n <td>2631.0</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>275</th>\n <td>TS=(\"ubiquitous computing\") AND PY=(2011-2022)</td>\n <td>3655.0</td>\n </tr>\n <tr>\n <th>276</th>\n <td>TS=(\"unstructured data*\") AND PY=(2011-2022)</td>\n <td>3386.0</td>\n </tr>\n <tr>\n <th>277</th>\n <td>TS=(\"unsupervised deep learning\") AND PY=(2011...</td>\n <td>728.0</td>\n </tr>\n <tr>\n <th>278</th>\n <td>TS=(\"word embedding*\") AND PY=(2011-2022)</td>\n <td>7068.0</td>\n </tr>\n <tr>\n <th>279</th>\n <td>TS=(\"word vector*\") AND PY=(2011-2022)</td>\n <td>1747.0</td>\n </tr>\n </tbody>\n</table>\n<p>280 rows × 2 columns</p>\n</div>"
"text/plain": " query Record Count\n0 CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST... 972.0\n1 CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST... 451.0\n2 CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST... 30.0\n3 CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST... 12.0\n4 CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST... 5.0\n.. ... ...\n384 TS=(\"word embedding*\") AND PY=(2011-2022) 7068.0\n385 TS=(\"word vector*\") AND PY=(2011-2022) 1747.0\n386 TS=((\"face recognition\" NOT \"brain\")) AND PY=(... 19690.0\n387 TS=((\"object detection\" NOT \"brain\")) AND PY=(... 28989.0\n388 TS=((\"speech recognition\" NOT \"brain\")) AND PY... 19912.0\n\n[389 rows x 2 columns]",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>query</th>\n <th>Record Count</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...</td>\n <td>972.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...</td>\n <td>451.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...</td>\n <td>30.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...</td>\n <td>12.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUST...</td>\n <td>5.0</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>384</th>\n <td>TS=(\"word embedding*\") AND PY=(2011-2022)</td>\n <td>7068.0</td>\n </tr>\n <tr>\n <th>385</th>\n <td>TS=(\"word vector*\") AND PY=(2011-2022)</td>\n <td>1747.0</td>\n </tr>\n <tr>\n <th>386</th>\n <td>TS=((\"face recognition\" NOT \"brain\")) AND PY=(...</td>\n <td>19690.0</td>\n </tr>\n <tr>\n <th>387</th>\n <td>TS=((\"object detection\" NOT \"brain\")) AND PY=(...</td>\n <td>28989.0</td>\n </tr>\n <tr>\n <th>388</th>\n <td>TS=((\"speech recognition\" NOT \"brain\")) AND PY...</td>\n <td>19912.0</td>\n </tr>\n </tbody>\n</table>\n<p>389 rows × 2 columns</p>\n</div>"
},
"execution_count": 62,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@ -84,7 +84,7 @@
},
{
"cell_type": "code",
"execution_count": 63,
"execution_count": 6,
"outputs": [],
"source": [
"# agg_df = agg_df[agg_df[\"Publication Years\"].str.startswith(\"20\", na=False)].copy()\n",
@ -97,19 +97,10 @@
},
{
"cell_type": "code",
"execution_count": 84,
"outputs": [
{
"data": {
"text/plain": "Publication Years\n2022 314\n2019 305\n2021 305\n2020 302\n2018 296\n2017 287\n2016 281\n2015 271\n2014 258\n2013 251\n2012 233\n2011 224\n2023 52\n2017 4\n2014 4\n2019 4\n2021 4\n2018 4\n2020 4\n2022 4\n2016 3\n2015 3\n2013 3\n2012 3\n2011 3\n2023 2\nName: count, dtype: int64"
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 7,
"outputs": [],
"source": [
"agg_df[\"Publication Years\"].value_counts()"
"# agg_df[\"Publication Years\"].value_counts()"
],
"metadata": {
"collapsed": false
@ -117,20 +108,20 @@
},
{
"cell_type": "code",
"execution_count": 64,
"execution_count": 8,
"outputs": [],
"source": [],
"source": [
"agg_df.to_excel(r'C:\\Users\\radvanyi\\PycharmProjects\\ZSI_analytics\\WOS\\wos_processed_data\\query_yearly_agg.xlsx', index=False)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 85,
"execution_count": 64,
"outputs": [],
"source": [
"agg_df.to_excel(r'C:\\Users\\radvanyi\\PycharmProjects\\ZSI_analytics\\WOS\\wos_processed_data\\query_yearly_agg.xlsx', index=False)"
],
"source": [],
"metadata": {
"collapsed": false
}

832
WOS/wos_processing_pipeline.ipynb
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