@inproceedings{aladdasi-etal-2026-chronolearn,
title = "{C}hrono{L}earn: A {GRAG} {LLM}-Based System for Structuring and Exploring Historical Narratives",
author = "ALADDASI, Mohammad O. and
Abu Hijleh, Shahd L. and
Qawasmeh, Omar",
editor = "Jarrar, Mustafa and
El-Haj, Mo and
Haddad, Amal and
Atiani, Serin and
Abudalfa, Shadi and
Regier, Terry and
Rayson, Paul and
Sima{'}an, Khalil and
Mansour, Camille",
booktitle = "Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.nakbanlp-1.4/",
doi = "10.63317/2bbamet766xn",
pages = "43--49",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T ChronoLearn: A GRAG LLM-Based System for Structuring and Exploring Historical Narratives
%A ALADDASI, Mohammad O.
%A Abu Hijleh, Shahd L.
%A Qawasmeh, Omar
%Y Jarrar, Mustafa
%Y El-Haj, Mo
%Y Haddad, Amal
%Y Atiani, Serin
%Y Abudalfa, Shadi
%Y Regier, Terry
%Y Rayson, Paul
%Y Sima’an, Khalil
%Y Mansour, Camille
%S Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F aladdasi-etal-2026-chronolearn
%X 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.
%R 10.63317/2bbamet766xn
%U https://aclanthology.org/2026.nakbanlp-1.4/
%U https://doi.org/10.63317/2bbamet766xn
%P 43-49
Markdown (Informal)
[ChronoLearn: A GRAG LLM-Based System for Structuring and Exploring Historical Narratives](https://aclanthology.org/2026.nakbanlp-1.4/) (ALADDASI et al., NakbaNLP 2026)
ACL