| dc.contributor | 企管系 | |
| dc.creator (作者) | 侯佩妤 | |
| dc.creator (作者) | Hou, Pei-Yu;Korn, Daniel R.;Melo-Filho, Cleber C.;Wright, David R.;Tropsha, Alexander;Chirkova, Rada | |
| dc.date (日期) | 2022-06 | |
| dc.date.accessioned | 26-Dec-2024 13:26:26 (UTC+8) | - |
| dc.date.available | 26-Dec-2024 13:26:26 (UTC+8) | - |
| dc.date.issued (上傳時間) | 26-Dec-2024 13:26:26 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/154901 | - |
| dc.description.abstract (摘要) | Knowledge-graph (KG) embeddings have emerged as a promise in addressing challenges faced by modern biomedical research, including the growing gap between therapeutic needs and available treatments. The popularity of KG embeddings in graph analytics is on the rise, due at least partially to the presumed semanticity of the learned embeddings. Unfortunately, the ability of a node neighborhood picked up by an embedding to capture the node's semantics may depend on the characteristics of the data. One of the reasons for this problem is that KG nodes can be promiscuous, that is, associated with a number of different relationships that are not unique or indicative of the properties of the nodes. | |
| dc.format.extent | 103 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Proceedings of the 2022 International Conference on Management of Data (SIGMOD '22), Association for Computing Machinery | |
| dc.subject (關鍵詞) | Biomedical knowledge graphs (KGs); KG embeddings; domain- and task-specific regular expressions for creating node neighborhoods | |
| dc.title (題名) | Compact Walks: Taming Knowledge-Graph Embeddings with Domain- and Task-Specific Pathways | |
| dc.type (資料類型) | conference | |
| dc.identifier.doi (DOI) | 10.1145/3514221.3517903 | |
| dc.doi.uri (DOI) | https://doi.org/10.1145/3514221.3517903 | |
