| dc.contributor | 企管系 | |
| dc.creator (作者) | 侯佩妤 | |
| dc.creator (作者) | Hou, Pei-Yu;Schatz, Kara;Gulyuk, Alexey V.;Yingling, Yaroslava G.;Chirkova, Rada | |
| dc.date (日期) | 2023-12 | |
| dc.date.accessioned | 3-Oct-2025 10:44:22 (UTC+8) | - |
| dc.date.available | 3-Oct-2025 10:44:22 (UTC+8) | - |
| dc.date.issued (上傳時間) | 3-Oct-2025 10:44:22 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/159787 | - |
| dc.description.abstract (摘要) | Knowledge graphs (KGs), with their flexible encoding of heterogeneous data, have been increasingly used in a variety of applications. At the same time, domain data are routinely stored in formats such as spreadsheets, text, or figures. Storing such data in KGs can open the door to more complex types of analytics, which might not be supported by the data sources taken in isolation. Giving domain experts the option to use a predefined automated workflow for integrating heterogeneous data from multiple sources into a single unified KG could significantly alleviate their data-integration time and resource burden, while potentially resulting in higher-quality KG data capable of enabling meaningful rule mining and machine learning.In this paper we introduce a domain-agnostic workflow called BUILD-KG for integrating heterogeneous scientific and experimental data from multiple sources into a single unified KG potentially enabling richer analytics. BUILD-KG is broadly applicable, accepting input data in popular structured and unstructured formats. BUILD-KG is also designed to be carried out with end users as humans-in-the-loop, which makes it domain aware. We present the workflow, report on our experiences with applying it to scientific and experimental data in the materials science domain, and provide suggestions for involving domain scientists in BUILD-KG as humans-in-the-loop. | |
| dc.format.extent | 114 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | 2023 IEEE International Conference on Big Data (BigData), IEEE, pp.2965-2974 | |
| dc.subject (關鍵詞) | Integrating heterogeneous data into knowledge graphs; domain-agnostic integration workflow enabling richer data analytics; domain experts as humans in the loop | |
| dc.title (題名) | BUILD-KG: Integrating Heterogeneous Data Into Analytics-Enabling Knowledge Graphs | |
| dc.type (資料類型) | conference | |
| dc.identifier.doi (DOI) | 10.1109/BigData59044.2023.10386570 | |
| dc.doi.uri (DOI) | https://doi.org/10.1109/BigData59044.2023.10386570 | |
