Structured Knowledge meets G en AI : A Framework for Logic-Driven Language Models (2025)

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.

## introduction as llms gain prominence in generating natural language, surveys point out that their reasoning capabilities become increasingly apparent @xcite . when dealing with critical applications like healthcare @xcite , trust is vital and well-informed decisions need to be guaranteed. hence, the gaps related to the black box nature of llms highlight the need for models that cannot only generate human-like text but also make informed, context-aware decisions. unlike existing methods that attempt to incorporate kgs as external sources @xcite , our approach centers on a bidirectional mediator that enables a dynamic and adaptable exchange between the llm and kg. the kg serves as the primary source for grounded, factual, and explainable information, while the llm provides the necessary fluency and cohesiveness to translate structured data into human-readable language. this framework ensures that responses are not only accurate but also explainable, aligning well with applications where trust and transparency are essential. ## related work llms rely on statistical patterns learned through extensive amounts of data, rather than true logical reasoning. this can lead to errors in contextsensitive tasks @xcite . this limitation is especially problematic in sensitive fields like medicine and law @xcite , where accuracy and logical consistency are crucial. approaches following retrieval-augmented generation (rag) @xcite revolutionize factual accuracy by incorporating external knowledge. however, they primarily rely on surface-level matching and embedding similarities, lacking the depth needed for complex reasoning @xcite . graph-based enhancements of this approach, such as graphrag @xcite , offer structured knowledge integration through kgs but reduce kg data to vector embeddings, which strips away important logical dependencies. some studies explore utilizing kgs as a prompt mechanism to support graph-based reasoning tasks in llms. @xcite . they find that embedding graph data into llm prompts can improve reasoning capabilities, while there is also a key limitation: the integration often fails to retain the full relational structure of the kg, leading to limitations in multi-step reasoning and contextual consistency. ## a framework for kg-enhanced llm reasoning to enhance reasoning and personalization in llms, we propose a domain-independent end-to-end framework centered around an independent mediator as shown in figure 1 . this mediator can be perceived as a way to perform the retrieval part of rag. the main difference in our architecture from traditional methods is that it better structures the reasoning behind the retrieval through a decompositional querying mechanism. this way a more optimal use of structured knowledge can be achieved. the mediator breaks down user inputs into kg queries, interprets kg responses for the llm by extracting the needed nodes and relationships (which can be not very prose friendly), and handles the back and forth communication between separate llm and kg modules to achieve comprehensive answers. by coordinating these interactions, the mediator enables the kg to act as the primary source of reasoning, while the llm provides fluent language generation. this design offers a versatile solution that addresses gaps in llm reasoning and adapts seamlessly to various domains. separating the modules in our systems and connecting the dots through our proposed modulator organizes the communication between these modules, making the pipeline more flexible and interpretable. with this approach, we aim at aligning outputs with user-specific data from the kg, preserving logical depth that simpler kg-llm integrations lack. moreover, integrating symbolic ai with llms supports explainability, a key issue in ai @xcite , by allowing responses to be traced back to specific kg elements and rules, enhancing transparency and fostering trust. furthermore, we aim for organizing the pipeline by separating the modules and facilitating the communication between them. unlike retrieval-based approaches (e.g., graphrag, lightrag @xcite ), which reduce kg data to vector represen-tations, our framework directly interacts with the kgs through a dynamic reasoning layer. this hybrid approach combines the structured knowledge of kgs with the linguistic capabilities of llms, improving factual accuracy, logical depth, and personalization. ## conclusion and future work by introducing a decompositional reasoning layer to interface with kgs, this research offers a novel approach to improve reasoning in llms. our method targets responses that are contextually grounded and linguistically fluent by giving llms fine-grained access to structures of kgs. applications where sophisticated reasoning is crucial are particularly promising for this hybrid architecture. kg-enhanced models that adjust to particular user demands while retaining logical accuracy could be extremely helpful in domains including career development, healthcare planning, and legal advising. these domains offer use-cases to be explored when conducting empirical studies with our approach. moreover, since knowledge needs to be updated regularly in practical applications, future work includes investigating how kg completion with llms can benefit from our proposed reasoning mediator. this way, the bidirectional property of the mediator is leveraged to dynamically enrich the kg with new, contextually relevant knowledge, without the need to manually modify it.