From Text to Historical Ecological Knowledge: The Construction and Application of the Shan Jing Knowledge Base

Ke Liang, Chu-Ren Huang, Xin-Lan Jiang


Abstract
Traditional Ecological Knowledge (TEK) has been recognized as a shared cultural heritage and a crucial instrument to tackle today’s environmental challenges. In this paper, we deal with historical ecological knowledge, a special type of TEK that is based on ancient language texts. In particular, we aim to build a language resource based on Shanhai Jing (The Classic of Mountains and Seas). Written 2000 years ago, Shanhai Jing is a record of flora and fauna in ancient China, anchored by mountains (shan) and seas (hai). This study focuses on the entities in the Shan Jing part and builds a knowledge base for them. We adopt a pattern-driven and bottom-up strategy to accommodate two features of the source: highly stylized narrative and juxtaposition of knowledge from multiple domains. The PRF values of both entity and relationship extraction are above 96%. Quality assurance measures like entity disambiguation and resolution were done by domain experts. Neo4j graph database is used to visualize the result. We think the knowledge base, containing 1432 systematically classified entities and 3294 relationships, can provide the foundation for the construction of a historical ecological knowledge base of China. Additionally, the ruled-based text-matching method can be helpful in ancient language processing.
Anthology ID:
2024.lrec-main.664
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
7521–7530
Language:
URL:
https://aclanthology.org/2024.lrec-main.664
DOI:
Bibkey:
Cite (ACL):
Ke Liang, Chu-Ren Huang, and Xin-Lan Jiang. 2024. From Text to Historical Ecological Knowledge: The Construction and Application of the Shan Jing Knowledge Base. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 7521–7530, Torino, Italia. ELRA and ICCL.
Cite (Informal):
From Text to Historical Ecological Knowledge: The Construction and Application of the Shan Jing Knowledge Base (Liang et al., LREC-COLING 2024)
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PDF:
https://preview.aclanthology.org/landing_page/2024.lrec-main.664.pdf