Datafication of Expert Knowledge and AI for the Humanities and Social Sciences
Bibliographic Data
| ID | 19231964 |
|---|---|
| Authors | Qiu Zeqi (Peking University), Z Qiu (Peking University), Haolin Li (0000-0001-9779-0675, University of International Business and Economics), Li Haolin (University of International Business and Economics), Zhang Pingwen (Wuhan University), Qiao Tianyu (Peking University) |
| Year | 2026 |
| Volume | 47 |
| Issue | 1 |
| Pages | 53-69 |
| Publication date | 2026-01-02 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Social Sciences in China (JOURNAL) |
| Journal identifiers | ISSN: 0252-9203 • E-ISSN: 1940-5952 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/02529203.2026.2673704 |
| OpenAlex | W7164127133 |
| Language | EN |
Intelligent digital technologies are reshaping the paradigms of knowledge production, with AI for the humanities and social sciences (AI for HSS) emerging as a key direction for future development. High-quality data is a prerequisite for this evolution. One path toward generating such data is datafication of accumulated human knowledge. Here, expert knowledge, characterized by its iterative refinement, condensed form, and specialized structure, is especially valuable due to its innovative potential. Within the humanities and social sciences, legal expert knowledge is particularly representative. Using law as a case study, datafication of expert knowledge can follow a “three-step” approach: delineating the scope of expert knowledge, reconstructing its internal structure, and designing technological pathways for its transformation. This model is broadly applicable and helps consolidate the data foundations of AI for HSS, while also marking a solid step toward a new paradigm in HSS knowledge production
Knowledge creation · Knowledge production · Social knowledge · Sociology of knowledge · Artificial Intelligence Applications · Computational and Text Analysis Methods · Digital Humanities and Scholarship
| Highly cited | No |
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