@inproceedings{eldessouky-etal-2025-structured,
title = "Structured Knowledge meets {G}en{AI}: A Framework for Logic-Driven Language Models",
author = "Eldessouky, Farida Helmy and
Ehab, Nourhan and
Schindler, Carolin and
Abuelkheir, Mervat and
Minker, Wolfgang",
editor = "Gesese, Genet Asefa and
Sack, Harald and
Paulheim, Heiko and
Merono-Penuela, Albert and
Chen, Lihu",
booktitle = "Proceedings of the Workshop on Generative AI and Knowledge Graphs (GenAIK)",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2025.genaik-1.7/",
pages = "66--68",
abstract = "Large Language Models (LLMs) excel at generating fluent text but struggle with context sensitivity, logical reasoning, and personalization without extensive fine-tuning. This paper presents a logical modulator: an adaptable communication layer between Knowledge Graphs (KGs) and LLMs as a way to address these limitations. Unlike direct KG-LLM integrations, our modulator is domain-agnostic and incorporates logical dependencies and commonsense reasoning in order to achieve contextual personalization. By enhancing KG interaction, this method will produce linguistically coherent and logically sound outputs, increasing interpretability and reliability in generative AI."
}
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<abstract>Large Language Models (LLMs) excel at generating fluent text but struggle with context sensitivity, logical reasoning, and personalization without extensive fine-tuning. This paper presents a logical modulator: an adaptable communication layer between Knowledge Graphs (KGs) and LLMs as a way to address these limitations. Unlike direct KG-LLM integrations, our modulator is domain-agnostic and incorporates logical dependencies and commonsense reasoning in order to achieve contextual personalization. By enhancing KG interaction, this method will produce linguistically coherent and logically sound outputs, increasing interpretability and reliability in generative AI.</abstract>
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%0 Conference Proceedings
%T Structured Knowledge meets GenAI: A Framework for Logic-Driven Language Models
%A Eldessouky, Farida Helmy
%A Ehab, Nourhan
%A Schindler, Carolin
%A Abuelkheir, Mervat
%A Minker, Wolfgang
%Y Gesese, Genet Asefa
%Y Sack, Harald
%Y Paulheim, Heiko
%Y Merono-Penuela, Albert
%Y Chen, Lihu
%S Proceedings of the Workshop on Generative AI and Knowledge Graphs (GenAIK)
%D 2025
%8 January
%I International Committee on Computational Linguistics
%C Abu Dhabi, UAE
%F eldessouky-etal-2025-structured
%X Large Language Models (LLMs) excel at generating fluent text but struggle with context sensitivity, logical reasoning, and personalization without extensive fine-tuning. This paper presents a logical modulator: an adaptable communication layer between Knowledge Graphs (KGs) and LLMs as a way to address these limitations. Unlike direct KG-LLM integrations, our modulator is domain-agnostic and incorporates logical dependencies and commonsense reasoning in order to achieve contextual personalization. By enhancing KG interaction, this method will produce linguistically coherent and logically sound outputs, increasing interpretability and reliability in generative AI.
%U https://aclanthology.org/2025.genaik-1.7/
%P 66-68
Markdown (Informal)
[Structured Knowledge meets GenAI: A Framework for Logic-Driven Language Models](https://aclanthology.org/2025.genaik-1.7/) (Eldessouky et al., GenAIK 2025)
ACL