ChronoLearn: A GRAG LLM-Based System for Structuring and Exploring Historical Narratives

Mohammad O. ALADDASI, Shahd L. Abu Hijleh, Omar Qawasmeh


Abstract
ChronoLearn is a KG–LLM framework to structure and ex- plore Arabic historical narratives. It transforms unstructured texts into knowledge graphs using an ETL-based NLP pipeline for entity and re- lation extraction, followed by schema-guided graph construction. The system integrates graph retrieval with LLM generation (GRAG) to pro- duce grounded, explainable narratives and support semantic querying. The approach is evaluated in heterogeneous Palestinian and Jordanian sources, including Nakba-related content, using both quantitative met- rics and comparative analysis. The results demonstrate improved factual grounding and structured reasoning, addressing limitations of text-only approaches in the processing of historical knowledge in Arabic.
Anthology ID:
2026.nakbanlp-1.4
Volume:
Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026
Month:
May
Year:
2026
Address:
Palma, Mallorca (Spain)
Editors:
Mustafa Jarrar, Mo El-Haj, Amal Haddad, Serin Atiani, Shadi Abudalfa, Terry Regier, Paul Rayson, Khalil Sima’an, Camille Mansour
Venues:
NakbaNLP | WS
SIG:
Publisher:
ELRA Language Resources Association (ELRA)
Note:
Pages:
43–49
Language:
External URL:
https://lrec.elra.info/lrec2026-ws-nakbanlp-04
DOI:
10.63317/2bbamet766xn
Bibkey:
Cite (ACL):
Mohammad O. ALADDASI, Shahd L. Abu Hijleh, and Omar Qawasmeh. 2026. ChronoLearn: A GRAG LLM-Based System for Structuring and Exploring Historical Narratives. In Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026, pages 43–49, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).
Cite (Informal):
ChronoLearn: A GRAG LLM-Based System for Structuring and Exploring Historical Narratives (ALADDASI et al., NakbaNLP 2026)
Copy Citation: