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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": "10.57702/38jfs1vi",
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  "doi": "10.57702/38jfs1vi",
            
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  "doi_date_published": "2024-09-19",
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  "doi_date_published": "2024-09-19",
            
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  "doi_publisher": "TIB",
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  "extra_authors": [
            
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    {
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      "extra_author": "Yashrajsinh Chudasama",
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      "extra_author": "Yashrajsinh Chudasama",
            
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      "orcid": "https://orcid.org/0000-0003-3422-366X"
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      "orcid": "https://orcid.org/0000-0003-3422-366X"
            
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    },
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      "extra_author": "Maria Torrente",
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      "extra_author": "Maria-Esther Vidal",
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      "extra_author": "Maria-Esther Vidal",
            
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      "orcid": "https://orcid.org/0000-0003-1160-8727"
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      "orcid": "https://orcid.org/0000-0003-1160-8727"
            
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    }
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  "license_title": "Creative Commons Attribution",
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  "maintainer": "Disha Purohit",
            
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  "name": 
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  "name": 
            
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-and-invalidated-symbolic-explanations-for-knowledge-graph-integrity",
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-and-invalidated-symbolic-explanations-for-knowledge-graph-integrity",
            
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  "notes": "VISE represents a novel hybrid strategy that integrates 
56
  "notes": "VISE represents a novel hybrid strategy that integrates 
            
57
symbolic learning, constraint validation, and numerical learning 
57
symbolic learning, constraint validation, and numerical learning 
            
58
approaches. VISE employs KGE to capture implicit information and 
58
approaches. VISE employs KGE to capture implicit information and 
            
59
represent negation in KGs, thereby enhancing the prediction 
59
represent negation in KGs, thereby enhancing the prediction 
            
60
performance of numerical models. The experimental results demonstrate 
60
performance of numerical models. The experimental results demonstrate 
            
61
the efficacy of this hybrid technique, which effectively integrates 
61
the efficacy of this hybrid technique, which effectively integrates 
            
62
the strengths of symbolic, numerical, and constraint validation 
62
the strengths of symbolic, numerical, and constraint validation 
            
63
paradigms.\r\n\r\nThis collection includes all the data necessary to 
63
paradigms.\r\n\r\nThis collection includes all the data necessary to 
            
64
reproduce the results from the experimental evaluation of VISE at 
64
reproduce the results from the experimental evaluation of VISE at 
            
65
EXPLIMED @ ECAI'24. The data is an anonymized synthetic lung cancer 
65
EXPLIMED @ ECAI'24. The data is an anonymized synthetic lung cancer 
            
66
benchmark that comprises clinical data extracted from heterogeneous 
66
benchmark that comprises clinical data extracted from heterogeneous 
            
67
sources such as publications, clinical trials, and clinical records 
67
sources such as publications, clinical trials, and clinical records 
            
68
representing patients diagnosed with lung cancer. We evaluate the VISE 
68
representing patients diagnosed with lung cancer. We evaluate the VISE 
            
69
approach on three anonymized Lung Cancer KGs: 
69
approach on three anonymized Lung Cancer KGs: 
            
70
LC-\ud835\udc3e\ud835\udc3a1, LC-\ud835\udc3e\ud835\udc3a2,and 
70
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 
71
LC-\ud835\udc3e\ud835\udc3a3.\r\n\r\nThe collection comprises nine 
            
72
data sets of three different sizes:\r\n\r\n- LC Knowledge Graph 1 
72
data sets of three different sizes:\r\n\r\n- LC Knowledge Graph 1 
            
73
(LC-KG1) models 29 lung cancer patients\r\n- LC Knowledge Graph 2 
73
(LC-KG1) models 29 lung cancer patients\r\n- LC Knowledge Graph 2 
            
74
(LC-KG2) models 203 lung cancer patients\r\n- LC Knowledge Graph 3 
74
(LC-KG2) models 203 lung cancer patients\r\n- LC Knowledge Graph 3 
            
75
(LC-KG3) models 319 lung cancer patients\r\n\r\nThree distinct KGs of 
75
(LC-KG3) models 319 lung cancer patients\r\n\r\nThree distinct KGs of 
            
76
different sizes are available, each with its own characteristics. 
76
different sizes are available, each with its own characteristics. 
            
77
\r\n\r\n- \"Original KG\": The original KG comprises of anonymized 
77
\r\n\r\n- \"Original KG\": The original KG comprises of anonymized 
            
78
lung cancer patients with different medical characteristics. \r\n- 
78
lung cancer patients with different medical characteristics. \r\n- 
            
79
\"Enriched KG\": Utilizes an inductive learning technique of KG 
79
\"Enriched KG\": Utilizes an inductive learning technique of KG 
            
80
completion through self-supervised symbolic learning over the original 
80
completion through self-supervised symbolic learning over the original 
            
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KG. \r\n- \"Transformed KG\": Denotes a transformation of the KG 
81
KG. \r\n- \"Transformed KG\": Denotes a transformation of the KG 
            
82
depending on SHACL shapes evaluated across the enriched KGs. This 
82
depending on SHACL shapes evaluated across the enriched KGs. This 
            
83
procedure is used to determine the validity of the data. \r\n\r\nVISE 
83
procedure is used to determine the validity of the data. \r\n\r\nVISE 
            
84
is also evaluated with KGs comprising 1242 lung cancer patients 
84
is also evaluated with KGs comprising 1242 lung cancer patients 
            
85
(LungCancer-OriginalKG, LungCancer-EnrichedKG, and 
85
(LungCancer-OriginalKG, LungCancer-EnrichedKG, and 
            
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LungCancer-TransformedKG).\r\n",
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LungCancer-TransformedKG).\r\n",
            
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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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