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| 4 | "author": "Disha Purohit", | 4 | "author": "Disha Purohit", | ||
| 5 | "author_email": "disha.purohit@tib.eu", | 5 | "author_email": "disha.purohit@tib.eu", | ||
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| 10 | "doi": "10.57702/38jfs1vi", | 10 | "doi": "10.57702/38jfs1vi", | ||
| 11 | "doi_date_published": "2024-09-19", | 11 | "doi_date_published": "2024-09-19", | ||
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| 18 | "extra_author": "Yashrajsinh Chudasama", | 18 | "extra_author": "Yashrajsinh Chudasama", | ||
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| 22 | "extra_author": "Maria Torrente", | 22 | "extra_author": "Maria Torrente", | ||
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| 25 | { | 25 | { | ||
| 26 | "extra_author": "Maria-Esther Vidal", | 26 | "extra_author": "Maria-Esther Vidal", | ||
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| 45 | "language": "English", | 45 | "language": "English", | ||
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| 47 | "license_title": "Creative Commons Attribution", | 47 | "license_title": "Creative Commons Attribution", | ||
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| 50 | "maintainer": "Disha Purohit", | 50 | "maintainer": "Disha Purohit", | ||
| 51 | "maintainer_email": "disha.purohit@tib.eu", | 51 | "maintainer_email": "disha.purohit@tib.eu", | ||
| 52 | "metadata_created": "2024-09-19T13:44:11.745534", | 52 | "metadata_created": "2024-09-19T13:44:11.745534", | ||
| 53 | "metadata_modified": "2024-10-01T14:09:45.015732", | 53 | "metadata_modified": "2024-10-01T14:09:45.015732", | ||
| 54 | "name": | 54 | "name": | ||
| 55 | -and-invalidated-symbolic-explanations-for-knowledge-graph-integrity", | 55 | -and-invalidated-symbolic-explanations-for-knowledge-graph-integrity", | ||
| 56 | "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.", | 63 | paradigms.", | ||
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| 71 | Technology, abbreviated TIB, is the national library of the Federal | 71 | Technology, abbreviated TIB, is the national library of the Federal | ||
| 72 | Republic of Germany for all fields of engineering, technology, and the | 72 | Republic of Germany for all fields of engineering, technology, and the | ||
| 73 | natural sciences.", | 73 | natural sciences.", | ||
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| 173 | "tags": [ | 173 | "tags": [ | ||
| 174 | { | 174 | { | ||
| 175 | "display_name": "Knowledge Graph", | 175 | "display_name": "Knowledge Graph", | ||
| 176 | "id": "1bea6e8a-7d3e-45b6-8ebb-3c23ad1b748b", | 176 | "id": "1bea6e8a-7d3e-45b6-8ebb-3c23ad1b748b", | ||
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| 187 | } | 187 | } | ||
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| 190 | "title": "VISE: Validated and Invalidated Symbolic Explanations for | 190 | "title": "VISE: Validated and Invalidated Symbolic Explanations for | ||
| 191 | Knowledge Graph Integrity", | 191 | Knowledge Graph Integrity", | ||
| 192 | "type": "dataset", | 192 | "type": "dataset", | ||
| 193 | "url": "https://github.com/SDM-TIB/VISE?tab=readme-ov-file", | 193 | "url": "https://github.com/SDM-TIB/VISE?tab=readme-ov-file", | ||
| 194 | "version": "", | 194 | "version": "", | ||
| 195 | "version_note": "" | 195 | "version_note": "" | ||
| 196 | } | 196 | } |