diff --git a/Neural graph module/ngm.ipynb b/Neural graph module/ngm.ipynb
index a9d65ee24701f9458b591a0d099a587efc82f298..2bb5850f4f33da0ede889f42cf4fce1193047870 100644
--- a/Neural graph module/ngm.ipynb
+++ b/Neural graph module/ngm.ipynb
@@ -2,9 +2,18 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "b:\\Programs\\Miniconda\\envs\\tdde19\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
"source": [
"import datasets\n",
"import torch\n",
@@ -21,7 +30,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -31,7 +40,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -51,7 +60,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -82,7 +91,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -221,7 +230,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -244,7 +253,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -258,25 +267,31 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 2052/2052 [00:01<00:00, 1068.18it/s]\n"
+ "100%|██████████| 2052/2052 [00:00<00:00, 2085.36it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Finished with batches\n",
- "features: ['[CLS] what ingredients are used in preparing the dish of ragout fin? [SEP] [ sub ] [SEP] ragout _ fin [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]'] mask: tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
- " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
- " 0, 0, 0, 0, 0, 0, 0, 0, 0]]) label_index tensor(336)\n",
- "valid features: tensor([[ 101, 2054, 2003, 1996, 2344, 1997, 2577, 10424, 2483, 11283,\n",
- " 7570, 2906, 1029, 102, 1031, 4942, 1033, 102, 2577, 1035,\n",
- " 10424, 2483, 11283, 1035, 7570, 2906, 102, 0, 0, 0,\n",
- " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
- " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
- " 0, 0, 0, 0, 0, 0, 0]]) valid mask: tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
- " 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
- " 0, 0, 0, 0, 0, 0, 0, 0, 0]]) valid label_index tensor(297)\n"
+ "Finished with batches\n"
+ ]
+ },
+ {
+ "ename": "ValueError",
+ "evalue": "'dbo:commander' is not in list",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32mc:\\Users\\Albin\\Documents\\TDDE19\\codebase\\Neural graph module\\ngm.ipynb Cell 7\u001b[0m in \u001b[0;36m<cell line: 28>\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#W6sZmlsZQ%3D%3D?line=25'>26</a>\u001b[0m train_dataloader \u001b[39m=\u001b[39m DataLoader(train_data, batch_size\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m, shuffle\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#W6sZmlsZQ%3D%3D?line=26'>27</a>\u001b[0m \u001b[39m#show first entry\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#W6sZmlsZQ%3D%3D?line=27'>28</a>\u001b[0m train_features, train_mask, train_label, ents \u001b[39m=\u001b[39m \u001b[39mnext\u001b[39;49m(\u001b[39miter\u001b[39;49m(train_dataloader))\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#W6sZmlsZQ%3D%3D?line=28'>29</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mfeatures:\u001b[39m\u001b[39m\"\u001b[39m, tokenizer\u001b[39m.\u001b[39mbatch_decode(train_features), \u001b[39m\"\u001b[39m\u001b[39mmask:\u001b[39m\u001b[39m\"\u001b[39m,train_mask,\u001b[39m\"\u001b[39m\u001b[39mlabel_index\u001b[39m\u001b[39m\"\u001b[39m, train_label[\u001b[39m0\u001b[39m])\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#W6sZmlsZQ%3D%3D?line=31'>32</a>\u001b[0m valid_dataloader \u001b[39m=\u001b[39m DataLoader(valid_data, batch_size\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m, shuffle\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n",
+ "File \u001b[1;32mb:\\Programs\\Miniconda\\envs\\tdde19\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:681\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 678\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sampler_iter \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 679\u001b[0m \u001b[39m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m 680\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reset() \u001b[39m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 681\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_next_data()\n\u001b[0;32m 682\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m 683\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dataset_kind \u001b[39m==\u001b[39m _DatasetKind\u001b[39m.\u001b[39mIterable \u001b[39mand\u001b[39;00m \\\n\u001b[0;32m 684\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \\\n\u001b[0;32m 685\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m>\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called:\n",
+ "File \u001b[1;32mb:\\Programs\\Miniconda\\envs\\tdde19\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:721\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 719\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_next_data\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m 720\u001b[0m index \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_next_index() \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 721\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dataset_fetcher\u001b[39m.\u001b[39;49mfetch(index) \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m 722\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory:\n\u001b[0;32m 723\u001b[0m data \u001b[39m=\u001b[39m _utils\u001b[39m.\u001b[39mpin_memory\u001b[39m.\u001b[39mpin_memory(data, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory_device)\n",
+ "File \u001b[1;32mb:\\Programs\\Miniconda\\envs\\tdde19\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:49\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m 47\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfetch\u001b[39m(\u001b[39mself\u001b[39m, possibly_batched_index):\n\u001b[0;32m 48\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mauto_collation:\n\u001b[1;32m---> 49\u001b[0m data \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m 50\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 51\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[possibly_batched_index]\n",
+ "File \u001b[1;32mb:\\Programs\\Miniconda\\envs\\tdde19\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:49\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 47\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfetch\u001b[39m(\u001b[39mself\u001b[39m, possibly_batched_index):\n\u001b[0;32m 48\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mauto_collation:\n\u001b[1;32m---> 49\u001b[0m data \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdataset[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m 50\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 51\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[possibly_batched_index]\n",
+ "File \u001b[1;32mb:\\Programs\\Miniconda\\envs\\tdde19\\lib\\site-packages\\torch\\utils\\data\\dataset.py:290\u001b[0m, in \u001b[0;36mSubset.__getitem__\u001b[1;34m(self, idx)\u001b[0m\n\u001b[0;32m 288\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(idx, \u001b[39mlist\u001b[39m):\n\u001b[0;32m 289\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mindices[i] \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m idx]]\n\u001b[1;32m--> 290\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdataset[\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mindices[idx]]\n",
+ "\u001b[1;32mc:\\Users\\Albin\\Documents\\TDDE19\\codebase\\Neural graph module\\ngm.ipynb Cell 7\u001b[0m in \u001b[0;36mMyDataset.__getitem__\u001b[1;34m(self, idx)\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#W6sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__getitem__\u001b[39m(\u001b[39mself\u001b[39m, idx):\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#W6sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minputs[idx], \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mattention_mask[idx], \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrelations\u001b[39m.\u001b[39;49mindex(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcorrect_rels[idx]), \u001b[39mself\u001b[39m\u001b[39m.\u001b[39ments[idx]\n",
+ "\u001b[1;31mValueError\u001b[0m: 'dbo:commander' is not in list"
]
}
],
@@ -289,12 +304,12 @@
" return json.load(f)\n",
"\n",
"\n",
- "#relations = open_json(\"../data/relations-query-qald-9-linked.json\")\n",
- "relations = open_json(\"../data/relations-all-no-http-lowercase.json\")\n",
+ "relations = open_json(\"../data/relations-all-lc-quad-no-http.json\")\n",
+ "#relations = open_json(\"../data/relations-all-no-http-lowercase.json\")\n",
"\n",
"# \"../data/qald-9-train-linked.json\"\n",
"#pred = \"../LC-QuAD/combined-requeried-linked-train.json\"\n",
- "inputs, attention_mask, correct_rels, sub_objs = make_batch(src=\"../LC-QuAD/combined-requeried-linked-train.json\", http_prefix = True) #train\n",
+ "inputs, attention_mask, correct_rels, sub_objs = make_batch(src=\"../data/lcquad-train.json\", http_prefix = True) #train\n",
"\n",
"# relations = open_json(\"../data/relations-lcquad-without-http-train-linked.json\")\n",
"# train_set = MyDataset(*make_batch(), relations=relations)\n",
@@ -319,16 +334,18 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": null,
"metadata": {},
"outputs": [
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight']\n",
- "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
- "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+ "ename": "NameError",
+ "evalue": "name 'NgmOne' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32mc:\\Users\\Albin\\Documents\\TDDE19\\codebase\\Neural graph module\\ngm.ipynb Cell 8\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#X10sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m headers \u001b[39m=\u001b[39m {\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#X10sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m \u001b[39m'\u001b[39m\u001b[39mAccept\u001b[39m\u001b[39m'\u001b[39m: \u001b[39m'\u001b[39m\u001b[39mapplication/sparql-results+json\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#X10sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m \u001b[39m'\u001b[39m\u001b[39mContent-Type\u001b[39m\u001b[39m'\u001b[39m: \u001b[39m'\u001b[39m\u001b[39mapplication/x-www-form-urlencoded\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#X10sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m }\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#X10sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m \u001b[39m# Initialize model\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#X10sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m model \u001b[39m=\u001b[39m NgmOne(device, relations)\n",
+ "\u001b[1;31mNameError\u001b[0m: name 'NgmOne' is not defined"
]
}
],
@@ -350,7 +367,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": null,
"metadata": {},
"outputs": [
{
@@ -376,9 +393,9 @@
"optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
"#optimizer = optim.SGD(model.parameters(), lr=0.0001, momentum=0.5)\n",
"\n",
- "epoch = 10\n",
+ "epoch = 5\n",
"batch_size = 8\n",
- "alpha = 0.5\n",
+ "alpha = 0.8\n",
"train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True)\n",
"valid_dataloader = DataLoader(valid_data, batch_size=batch_size, shuffle=True)\n",
"\n",
@@ -464,7 +481,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": null,
"metadata": {},
"outputs": [
{
@@ -641,7 +658,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3.9.11 64-bit",
+ "display_name": "Python 3.10.4 ('tdde19')",
"language": "python",
"name": "python3"
},
@@ -655,12 +672,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.11"
+ "version": "3.10.4"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
- "hash": "64e7cd3b4b88defe39dd61a4584920400d6beb2615ab2244e340c2e20eecdfe9"
+ "hash": "8e4aa0e1a1e15de86146661edda0b2884b54582522f7ff2b916774ba6b8accb1"
}
}
},
diff --git a/data/relations-all-lc-quad-no-http-2.json b/data/relations-all-lc-quad-no-http-2.json
new file mode 100644
index 0000000000000000000000000000000000000000..72112bd66c04a658e3a98b1b5642de6d30ea9fca
--- /dev/null
+++ b/data/relations-all-lc-quad-no-http-2.json
@@ -0,0 +1,443 @@
+[
+ "dbp:doctoraladvisor",
+ "dbp:editing",
+ "dbo:bronzemedalist",
+ "dbp:knownfor",
+ "dbp:creators",
+ "dbp:leadertitle",
+ "dbp:architect",
+ "dbp:designer",
+ "dbp:junction",
+ "dbo:producer",
+ "dbo:federalstate",
+ "dbo:militaryrank",
+ "dbo:nationality",
+ "dbo:associatedmusicalartist",
+ "dbo:architect",
+ "dbo:routestart",
+ "dbo:manager",
+ "dbp:managerclubs",
+ "dbo:architecturalstyle",
+ "dbp:buildingtype",
+ "dbo:cpu",
+ "dbo:ideology",
+ "dbp:engine",
+ "dbp:president",
+ "dbp:placeofbirth",
+ "dbp:draftteam",
+ "dbo:publisher",
+ "dbo:formerteam",
+ "dbo:network",
+ "dbo:rivermouth",
+ "dbo:family",
+ "dbo:river",
+ "dbo:composer",
+ "dbo:territory",
+ "dbo:stylisticorigin",
+ "dbp:label",
+ "dbp:founded",
+ "dbp:office",
+ "dbo:leader",
+ "dbp:nationalteam",
+ "dbo:associatedband",
+ "dbo:inflow",
+ "dbo:kingdom",
+ "dbp:university",
+ "dbp:firstteam",
+ "dbo:hometown",
+ "dbp:notableworks",
+ "dbo:writer",
+ "dbo:starring",
+ "dbp:editor",
+ "dbo:lyrics",
+ "dbo:veneratedin",
+ "dbp:deathdate",
+ "dbo:phylum",
+ "dbp:affiliation",
+ "dbp:owner",
+ "dbo:builder",
+ "dbo:director",
+ "dbp:album",
+ "dbp:starring",
+ "dbp:chancellor",
+ "dbp:company",
+ "dbp:maininterests",
+ "dbo:school",
+ "dbp:governingbody",
+ "dbp:narrated",
+ "dbp:recorded",
+ "dbo:artist",
+ "dbp:international",
+ "dbo:automobileplatform",
+ "dbp:deathplace",
+ "dbp:citizenship",
+ "dbp:domain",
+ "dbo:servingrailwayline",
+ "dbp:allegiance",
+ "dbo:colour",
+ "dbo:citizenship",
+ "dbp:operatingsystem",
+ "dbp:guests",
+ "dbo:binomialauthority",
+ "dbp:purpose",
+ "dbp:commander",
+ "dbo:mountainrange",
+ "dbp:employer",
+ "dbp:almamater",
+ "dbo:county",
+ "dbp:appointer",
+ "dbp:associatedacts",
+ "dbo:parentcompany",
+ "dbp:occupation",
+ "dbo:related",
+ "dbp:education",
+ "dbo:owningcompany",
+ "dbo:hubairport",
+ "dbp:programminglanguage",
+ "dbp:locationcountry",
+ "dbp:district",
+ "dbo:team",
+ "dbp:battles",
+ "dbp:keypeople",
+ "dbp:homestadium",
+ "dbp:playedfor",
+ "dbo:basedon",
+ "dbp:team",
+ "dbo:coach",
+ "dbp:club",
+ "dbp:awards",
+ "dbp:currentclub",
+ "dbp:currentmembers",
+ "dbo:genre",
+ "dbo:academicdiscipline",
+ "dbo:largestcity",
+ "dbp:stadium",
+ "dbo:president",
+ "dbo:voice",
+ "dbo:language",
+ "dbp:cities",
+ "dbo:illustrator",
+ "dbo:denomination",
+ "dbp:successor",
+ "dbo:cinematography",
+ "dbp:broadcastarea",
+ "dbo:album",
+ "dbp:music",
+ "dbp:jurisdiction",
+ "dbo:product",
+ "dbo:trainer",
+ "dbp:spouse",
+ "dbo:militarybranch",
+ "dbp:league",
+ "dbp:characters",
+ "dbp:commandstructure",
+ "dbo:board",
+ "dbo:otherparty",
+ "dbp:youthclubs",
+ "dbo:designer",
+ "dbo:assembly",
+ "dbo:stadium",
+ "dbo:relative",
+ "dbp:foundation",
+ "dbp:rank",
+ "dbp:deathcause",
+ "dbo:movement",
+ "dbo:silvermedalist",
+ "dbo:service",
+ "dbo:party",
+ "dbp:crosses",
+ "dbp:school",
+ "dbo:maintainedby",
+ "dbo:placeofburial",
+ "dbp:placeofburial",
+ "dbp:notableinstruments",
+ "dbp:design",
+ "dbp:order",
+ "dbo:author",
+ "dbo:militaryunit",
+ "dbp:branch",
+ "dbp:relatives",
+ "dbo:series",
+ "dbp:title",
+ "dbp:birthname",
+ "dbo:locatedinarea",
+ "dbo:neighboringmunicipality",
+ "dbo:opponent",
+ "dbo:targetairport",
+ "dbo:employer",
+ "dbp:province",
+ "dbp:place",
+ "dbo:musicby",
+ "dbo:poledriver",
+ "dbp:manufacturer",
+ "dbo:executiveproducer",
+ "dbp:director",
+ "dbo:university",
+ "dbp:fields",
+ "dbp:hometown",
+ "dbo:league",
+ "dbo:languagefamily",
+ "dbo:majorshrine",
+ "dbo:portrayer",
+ "dbo:distributor",
+ "dbo:recordedin",
+ "dbo:literarygenre",
+ "dbo:city",
+ "dbp:firstdriver",
+ "dbp:tenants",
+ "dbo:gender",
+ "dbp:format",
+ "dbp:writers",
+ "dbo:academicadvisor",
+ "dbo:knownfor",
+ "dbp:licensee",
+ "dbo:keyperson",
+ "dbp:nationality",
+ "dbp:neighboringmunicipalities",
+ "dbo:operatingsystem",
+ "dbp:arena",
+ "dbo:ethnicity",
+ "dbp:affiliations",
+ "dbp:ground",
+ "dbp:race",
+ "dbo:campus",
+ "dbo:breeder",
+ "dbo:affiliation",
+ "dbp:name",
+ "dbo:computingplatform",
+ "dbp:country",
+ "dbp:cinematography",
+ "dbo:headquarter",
+ "dbo:battle",
+ "dbp:largestcity",
+ "dbo:foundationplace",
+ "dbp:pastmembers",
+ "dbp:screenplay",
+ "dbo:programminglanguage",
+ "dbo:broadcastarea",
+ "dbo:founder",
+ "dbp:residence",
+ "dbp:partner",
+ "dbp:teamname",
+ "dbp:party",
+ "dbo:discoverer",
+ "dbo:foundedby",
+ "dbp:deputy",
+ "dbp:founder",
+ "dbp:mission",
+ "dbo:highschool",
+ "dbp:titles",
+ "dbo:previouswork",
+ "dbp:notablecommanders",
+ "dbp:locationtown",
+ "dbo:almamater",
+ "dbo:destination",
+ "dbo:debutteam",
+ "dbo:deathcause",
+ "dbp:animator",
+ "dbp:creator",
+ "dbo:governmenttype",
+ "dbo:education",
+ "dbo:award",
+ "dbp:veneratedin",
+ "dbp:genre",
+ "dbo:authority",
+ "dbp:hubs",
+ "dbo:garrison",
+ "dbp:coach",
+ "dbp:languages",
+ "dbp:flagbearer",
+ "dbp:sisternames",
+ "dbp:subject",
+ "dbo:college",
+ "dbo:subsequentwork",
+ "dbp:restingplace",
+ "dbp:position",
+ "dbo:sport",
+ "dbp:prizes",
+ "dbp:currency",
+ "dbo:owner",
+ "dbp:assembly",
+ "dbp:services",
+ "dbp:religiousaffiliation",
+ "dbo:homestadium",
+ "dbo:deathplace",
+ "dbo:order",
+ "dbo:birthplace",
+ "dbp:combatant",
+ "dbo:species",
+ "dbo:commandstructure",
+ "dbp:nationalorigin",
+ "dbo:field",
+ "dbp:parent",
+ "dbo:chairman",
+ "dbo:ingredient",
+ "dbp:officialname",
+ "dbp:leader",
+ "dbp:distributor",
+ "dbp:language",
+ "dbo:tenant",
+ "dbo:presenter",
+ "dbp:manager",
+ "dbo:training",
+ "dbo:sisterstation",
+ "dbp:carries",
+ "dbp:houses",
+ "dbo:editor",
+ "dbp:architecture",
+ "dbo:programmeformat",
+ "dbp:lieutenant",
+ "dbp:birthplace",
+ "dbo:creator",
+ "dbp:placeofdeath",
+ "dbp:role",
+ "dbo:license",
+ "dbo:type",
+ "dbo:editing",
+ "dbp:beatifiedby",
+ "dbp:borough",
+ "dbo:subsidiary",
+ "dbo:stateoforigin",
+ "dbp:majorshrine",
+ "dbp:author",
+ "dbo:country",
+ "dbp:birthdate",
+ "dbo:notablework",
+ "dbp:college",
+ "dbp:location",
+ "dbo:region",
+ "dbp:operator",
+ "dbp:workinstitutions",
+ "dbo:firstascentperson",
+ "dbp:predecessor",
+ "dbo:season",
+ "dbp:training",
+ "dbo:commander",
+ "dbo:ceremonialcounty",
+ "dbp:executiveproducer",
+ "dbo:athletics",
+ "dbo:occupation",
+ "dbo:mayor",
+ "dbo:spouse",
+ "dbp:children",
+ "dbp:type",
+ "dbp:firstaired",
+ "dbp:constituency",
+ "dbp:sisterstations",
+ "dbp:governor",
+ "dbo:managerclub",
+ "dbo:location",
+ "dbo:regionserved",
+ "dbp:primeminister",
+ "dbo:nearestcity",
+ "dbp:athletics",
+ "dbo:predecessor",
+ "dbp:lyrics",
+ "dbo:launchsite",
+ "dbo:capital",
+ "dbo:lieutenant",
+ "dbp:line",
+ "dbo:firstdriver",
+ "dbo:child",
+ "dbp:coverartist",
+ "dbp:address",
+ "dbp:discipline",
+ "dbp:developer",
+ "dbp:highschool",
+ "dbp:cityserved",
+ "dbo:successor",
+ "dbp:garrison",
+ "dbp:champion",
+ "dbp:artist",
+ "dbp:producer",
+ "dbp:state",
+ "dbo:formerbandmember",
+ "dbo:parentorganisation",
+ "dbo:profession",
+ "dbp:material",
+ "dbo:origin",
+ "dbp:poledriver",
+ "dbo:locationcity",
+ "dbp:mother",
+ "dbo:doctoraladvisor",
+ "dbp:city",
+ "dbo:institution",
+ "dbp:architecturalstyle",
+ "dbp:related",
+ "dbp:area",
+ "dbp:religion",
+ "dbp:region",
+ "dbo:layout",
+ "dbp:inflow",
+ "dbp:magazine",
+ "dbo:currency",
+ "dbp:writer",
+ "dbo:wineregion",
+ "dbp:pastteams",
+ "dbp:membership",
+ "dbo:nonfictionsubject",
+ "dbo:incumbent",
+ "dbp:venue",
+ "dbo:parent",
+ "dbo:countyseat",
+ "dbo:distributinglabel",
+ "dbp:headquarters",
+ "dbp:nickname",
+ "dbo:residence",
+ "dbo:timezone",
+ "dbp:destinations",
+ "dbo:mouthmountain",
+ "dbp:debutteam",
+ "dbo:anthem",
+ "dbo:jurisdiction",
+ "dbp:mascot",
+ "dbo:partner",
+ "dbp:doctoralstudents",
+ "dbo:primeminister",
+ "dbo:club",
+ "dbp:haircolor",
+ "dbo:instrument",
+ "dbp:thememusiccomposer",
+ "dbp:leadername",
+ "dbo:monarch",
+ "dbo:developer",
+ "dbp:os",
+ "dbp:meaning",
+ "dbo:operator",
+ "dbo:relation",
+ "dbp:archipelago",
+ "dbo:significantbuilding",
+ "dbo:narrator",
+ "dbo:formerpartner",
+ "dbo:doctoralstudent",
+ "dbo:ground",
+ "dbp:presenter",
+ "dbp:license",
+ "dbo:division",
+ "dbo:bandmember",
+ "dbp:schooltype",
+ "dbo:religion",
+ "dbp:field",
+ "dbp:origin",
+ "dbp:locationcity",
+ "dbp:agencyname",
+ "dbp:gender",
+ "dbp:owners",
+ "dbo:manufacturer",
+ "dbo:officiallanguage",
+ "dbo:honours",
+ "dbp:hostcity",
+ "dbp:products",
+ "dbo:race",
+ "dbp:canonizedby",
+ "dbo:routeend",
+ "dbp:nearestcity",
+ "dbp:mainingredient",
+ "dbo:outflow",
+ "dbo:restingplace",
+ "dbp:publisher",
+ "dbp:trainer",
+ "dbp:style",
+ "dbp:outflow",
+ "dbp:chairman"
+]