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  "author": "Disha Purohit",
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  "author": "Disha Purohit",
            
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  "author_email": "disha.purohit@tib.eu",
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  "author_email": "disha.purohit@tib.eu",
            
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  "doi": "10.57702/w3ghyku2",
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  "doi": "10.57702/w3ghyku2",
            
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  "doi_date_published": "2024-09-14",
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  "doi_date_published": "2024-09-14",
            
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      "extra_author": "Yashrajsinh Chudasama",
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      "extra_author": "Yashrajsinh Chudasama",
            
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      "extra_author": "Maria Torrente",
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      "extra_author": "Maria-Esther Vidal",
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  "maintainer": "Disha Purohit",
            
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  "maintainer_email": "disha.purohit@tib.eu",
            
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  "metadata_modified": "2024-10-01T15:27:36.562610",
            
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  "name": "vise-kg",
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  "name": "vise-kg",
            
t
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  "notes": "the dataset used in the following paper:\r\nVISE: 
t

            
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Validated and Invalidated Symbolic Explanations for Knowledge Graph 
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  "notes": "VISE: Validated and Invalidated Symbolic Explanations for 
            
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Integrity",
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Knowledge Graph Integrity\r\n\r\nThis collection includes all the data 
            
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necessary to reproduce the results from the experimental evaluation of 
            
67
VISE at EXPLIMED @ ECAI'24.\r\nThe data is an anonymized synthetic 
            
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lung cancer benchmark that comprises clinical data extracted from 
            
69
heterogeneous sources such as publications, clinical trials, and 
            
70
clinical records representing patients diagnosed with lung cancer. We 
            
71
evaluate the VISE approach on three anonymized Lung Cancer KGs: 
            
72
LC-\ud835\udc3e\ud835\udc3a1, LC-\ud835\udc3e\ud835\udc3a2,and 
            
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LC-\ud835\udc3e\ud835\udc3a3\r\n\r\nThe collection comprises nine data 
            
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sets of three different sizes:\r\n\r\n- LC Knowledge Graph 1 (LC-KG1) 
            
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models 29 lung cancer patients\r\n- LC Knowledge Graph 2 (LC-KG2) 
            
76
models 203 lung cancer patients\r\n- LC Knowledge Graph 3 (LC-KG3) 
            
77
models 319 lung cancer patients\r\n\r\nThree distinct KGs of different 
            
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sizes are available, each with its own characteristics. \r\n\r\n- 
            
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\"Original KG\": The original KG comprises of anonymized lung cancer 
            
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patients with different medical characteristics. \r\n- \"Enriched 
            
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KG\": Utilizes an inductive learnng technique of KG completion through 
            
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self-supervised symbolic learning over the original KG. \r\n- 
            
83
\"Transformed KG\": Denotes a transformation of the KG depending on 
            
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SHACL shapes evaluated across the enriched KGs. This procedure is used 
            
85
to determine the validity of the data. \r\n\r\nVISE is also evaluated 
            
86
with KGs comprising 1242 lung cancer patients (LungCancer-OriginalKG, 
            
87
LungCancer-EnrichedKG, and LungCancer-TransformedKG).\r\n\r\nOur 
            
88
experimental results demonstrate the effectiveness of this hybrid 
            
89
strategy, which combines the strengths of symbolic, numerical, and 
            
90
constraint validation\r\nparadigms. VISE implementation is publicly 
            
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accessible on GitHub (https://github.com/SDM-TIB/VISE).",
            
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    "description": "The German National Library of Science and 
            
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Technology, abbreviated TIB, is the national library of the Federal 
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Technology, abbreviated TIB, is the national library of the Federal 
            
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Republic of Germany for all fields of engineering, technology, and the 
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Republic of Germany for all fields of engineering, technology, and the 
            
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natural sciences.",
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natural sciences.",
            
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      "display_name": "Health-care",
209
      "display_name": "Health-care",
            
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      "id": "9483f09b-a7a4-4ad6-a737-882fe99975d4",
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      "id": "9483f09b-a7a4-4ad6-a737-882fe99975d4",
            
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      "name": "Health-care",
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      "name": "Health-care",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
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      "vocabulary_id": null
            
189
    },
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    },
            
190
    {
215
    {
            
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      "display_name": "Knowledge Graph",
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      "display_name": "Knowledge Graph",
            
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      "id": "12b0c897-01cf-4275-99e8-21d85c09a38d",
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      "id": "12b0c897-01cf-4275-99e8-21d85c09a38d",
            
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      "name": "Knowledge Graph",
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      "name": "Knowledge Graph",
            
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      "state": "active",
219
      "state": "active",
            
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      "vocabulary_id": null
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      "vocabulary_id": null
            
196
    },
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    },
            
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    {
222
    {
            
198
      "display_name": "Symbolic Learning",
223
      "display_name": "Symbolic Learning",
            
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      "id": "42bfaed0-47e1-4b46-9f1d-95002032bff9",
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      "id": "42bfaed0-47e1-4b46-9f1d-95002032bff9",
            
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      "name": "Symbolic Learning",
225
      "name": "Symbolic Learning",
            
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      "state": "active",
226
      "state": "active",
            
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      "vocabulary_id": null
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      "vocabulary_id": null
            
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    }
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    }
            
204
  ],
229
  ],
            
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  "temporal_resolution": "",
230
  "temporal_resolution": "",
            
206
  "title": "VISE-KG",
231
  "title": "VISE-KG",
            
207
  "type": "dataset",
232
  "type": "dataset",
            
208
  "url": "https://github.com/SDM-TIB/VISE",
233
  "url": "https://github.com/SDM-TIB/VISE",
            
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  "version": "",
234
  "version": "",
            
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  "version_note": ""
235
  "version_note": ""
            
211
}
236
}