Ask and Learn: Building Conversational AI That Evolves With Knowledge

Author: Denis Avetisyan

Researchers have developed a new framework that allows AI agents to not only answer complex questions using knowledge graphs, but also improve their reasoning abilities over time through self-directed learning.

This study demonstrates a comparative analysis between SEAL and KB-Binder models when applied to a multi-turn question answering task utilizing the SPICE dataset.

This paper introduces SEAL, a two-stage agentic learning framework utilizing S-expression cores and a self-evolving mechanism for accurate and efficient conversational question answering over knowledge graphs.

Despite advances in knowledge-based conversational question answering, accurately resolving complex queries over large knowledge graphs remains challenging due to limitations in structural fidelity and computational cost. This paper introduces ‘SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs’, a novel two-stage framework that leverages self-evolving agentic learning and S-expression cores to enhance both the accuracy and efficiency of conversational reasoning. By integrating local and global memory with a reflection module, SEAL adapts continuously from dialog history and execution feedback without explicit retraining. Could this self-evolving capability unlock truly scalable and robust conversational AI systems capable of complex knowledge graph interrogation?

Bridging the Semantic Gap: Unveiling Meaning in Knowledge Graphs

Historically, enabling computers to answer questions using knowledge graphs has proven remarkably difficult, despite advancements in both natural language processing and graph database technology. Traditional methods, reliant on keyword matching or simplistic pattern recognition, frequently misinterpret the nuanced meaning within complex queries. These approaches struggle with questions requiring multi-hop reasoning – where information must be gathered from several interconnected parts of the graph – or those containing implicit assumptions and contextual dependencies. Consequently, systems often return irrelevant or incomplete answers, failing to bridge the gap between human intent and the structured data within the knowledge graph. The limitations of these early techniques highlight the necessity for more sophisticated methods capable of truly understanding the question, rather than merely processing its surface-level features, and underscore the ongoing challenge in realizing the full potential of knowledge-driven applications.

The conversion of human language into a precise, machine-understandable query represents a significant hurdle in knowledge graph question answering. This translation isn’t merely a matter of keyword matching; it demands a nuanced understanding of semantics, context, and the underlying structure of the knowledge graph. Ambiguity inherent in natural language – pronouns with multiple possible referents, implied relationships, or vague quantifiers – requires sophisticated parsing techniques to resolve. A successful translation must accurately map the question’s intent to a formal query language, such as SPARQL, enabling the system to traverse the graph and retrieve the correct answer. The difficulty escalates with complex questions involving multiple entities, relationships, or constraints, necessitating algorithms capable of capturing these intricacies and expressing them in a logically sound and executable form. Ultimately, the efficacy of any knowledge graph question answering system hinges on its ability to bridge this linguistic divide and faithfully represent the user’s query within the formal framework of the graph.

Semantic parsing, the process of converting natural language into a machine-understandable query, frequently encounters difficulties when dealing with the nuances of human language. Current techniques struggle to resolve ambiguous references – pronouns or incomplete descriptions that require contextual understanding to decipher their intended meaning. For example, a question like “What is his occupation?” necessitates identifying “he” within the preceding conversation or knowledge graph data. Furthermore, contextual dependencies, where the meaning of a word or phrase relies heavily on surrounding information, present a significant hurdle. These systems often fail to accurately interpret questions requiring inference or the integration of multiple pieces of information, hindering their ability to provide precise and relevant answers when querying complex knowledge graphs. This limitation underscores the need for more sophisticated parsing methods capable of capturing the subtle relationships and implicit meanings embedded within natural language.

The true power of knowledge graphs – their ability to connect disparate information and facilitate insightful discovery – remains largely untapped without robust and scalable question answering systems. Current limitations in translating natural language into precise knowledge graph queries hinder widespread adoption, especially as graph sizes and complexity increase. Developing solutions capable of handling ambiguity, contextual nuances, and the sheer volume of data is not merely a technical challenge, but a critical step toward unlocking the potential of these graphs in areas like drug discovery, personalized medicine, and intelligent assistants. Without scalable infrastructure and resilient algorithms, the promise of knowledge graphs to revolutionize information access will remain largely unrealized, restricting their impact to niche applications and limiting their broader societal benefits.

Successfully applying large language models to knowledge base question answering presents significant challenges.

SEAL: A Two-Stage Framework for Semantic Clarity

The SEAL framework initiates semantic parsing with the extraction of a simplified S-expression core. This core represents the fundamental semantic components of a given question, stripping away surface-level linguistic variations and focusing on essential relationships between entities and actions. The S-expression format utilizes nested lists to denote hierarchical structures, enabling a concise and unambiguous representation of the question’s meaning. This initial stage prioritizes identifying the core intent and relevant information, rather than generating a complete logical form directly, thereby decoupling semantic understanding from the complexities of target database schemas or knowledge bases.

Following the generation of a simplified S-expression core, the SEAL framework utilizes template composition to construct a complete logical form suitable for query execution. This process involves identifying pre-defined templates corresponding to different semantic patterns and filling them with the extracted information from the S-expression core. These templates, representing valid logical forms, ensure syntactical correctness and facilitate translation into executable queries for knowledge bases or databases. The template composition stage effectively bridges the gap between the simplified semantic representation and the formal query language, enabling the system to retrieve accurate and relevant answers.

The SEAL framework utilizes a Large Language Model (LLM)-based Agent to manage both the core extraction and logical form expansion stages. This Agent architecture enables dynamic processing of varied question structures without requiring pre-defined parsing rules for each input. The LLM’s generative capabilities allow it to adapt to unseen phrasing and complex linguistic patterns, offering increased robustness compared to traditional rule-based semantic parsers. By leveraging the LLM’s contextual understanding, the Agent can effectively identify key semantic elements and construct accurate logical representations, even with questions exhibiting significant syntactic diversity. This approach minimizes the need for extensive training data specific to each question type and supports generalization to novel queries.

The decoupling of semantic parsing into core extraction and logical form completion within the SEAL framework enables distinct optimization strategies for each stage. By isolating the simplification process from the final query construction, developers can focus on improving the robustness of core semantic representation without impacting the template-based expansion. This modularity is particularly beneficial in complex scenarios involving nested queries or ambiguous language, as errors in initial simplification are less likely to propagate and derail the complete logical form generation. Consequently, targeted training and fine-tuning of the LLM agent for core extraction, alongside refinement of the template composition process, results in a measurable increase in parsing accuracy and successful query execution rates compared to end-to-end approaches.

The number of SPARQL queries generated varies depending on the settings used for S-expression core extraction.

Fine-Grained Analysis: Refining Queries for Precision

The LLM-based Agent utilizes calibration techniques to address syntactic errors and ensure semantic alignment between user queries and the knowledge graph. These techniques involve post-processing the initial LLM output to correct grammatical mistakes and resolve ambiguities in entity and relation identification. Specifically, the agent employs methods to standardize entity names, disambiguate relation types, and map them to their corresponding representations within the knowledge graph. This calibration process is critical for transforming natural language input into a structured logical form suitable for querying, thereby improving the accuracy and reliability of information retrieval.

Question type prediction serves to constrain the potential logical forms generated by the LLM-based Agent by classifying incoming queries into predefined categories. This classification process reduces the search space for template selection, enabling the agent to identify the most relevant logical form template based on the predicted question type. By focusing on a subset of appropriate templates, the system improves parsing efficiency and minimizes the likelihood of generating incorrect or irrelevant logical forms. This targeted approach to template selection is a key component in improving the overall accuracy and performance of knowledge graph querying.

Coreference resolution is implemented within the LLM-based Agent to identify and link expressions that refer to the same entity within a query. This process addresses ambiguities arising from pronouns, definite noun phrases, and other referring expressions, ensuring that the system accurately tracks entities and their relationships across multiple clauses or sentences. By resolving these references, the Agent maintains contextual consistency during parsing and logical form generation, preventing misinterpretations and improving the precision of knowledge graph queries. The system utilizes algorithms to determine the most likely referent based on linguistic features, contextual information, and knowledge graph data, ultimately contributing to more accurate and reliable results.

The integration of fine-grained analytical steps – including calibration, question type prediction, and coreference resolution – demonstrably improves the accuracy of logical form generation. Evaluations on the SPICE dataset indicate an overall accuracy of 66.83% is achievable through this methodology. This performance metric reflects the system’s ability to correctly translate natural language queries into a structured representation suitable for knowledge graph querying, thereby minimizing misinterpretations of user intent and maximizing the relevance of retrieved information.

The self-evolving mechanism's F1 score is demonstrably impacted by s-expression length, dialog turn depth, and cumulative dialog context coverage. — The self-evolving mechanism’s F1 score is demonstrably impacted by s-expression length, dialog turn depth, and cumulative dialog context coverage.

Self-Evolving Intelligence: A Dynamic Conversational System

The framework’s adaptability is significantly enhanced through a self-evolving mechanism that strategically integrates two distinct memory systems. Local Memory functions as a short-term buffer, capturing the immediate dependencies and nuances within the ongoing conversation to maintain coherence and relevant responses. Complementing this is Global Memory, a structured repository that accumulates knowledge extracted from previous interactions – effectively enabling the system to learn and refine its understanding over time. This combined approach allows the intelligence to not only respond appropriately to the present context but also to build upon past experiences, fostering a dynamic and increasingly sophisticated conversational ability. The interplay between these memories ensures the system can navigate complex dialogues and demonstrate a more human-like understanding of evolving topics.

The system’s ability to maintain a coherent dialogue hinges on its Local Memory, a mechanism designed to capture the immediate dependencies within a conversation. This short-term contextual awareness allows the framework to track entities, references, and evolving topics as they unfold, preventing the disjointed responses often characteristic of systems lacking such memory. By retaining information from previous turns, the model effectively builds upon prior statements, ensuring that each utterance is relevant and logically connected to the ongoing exchange. This dynamic tracking isn’t simply about remembering words; it’s about understanding the relationships between them, and using that understanding to predict and generate responses that feel natural and consistent within the conversational flow, ultimately improving the user experience.

The system’s capacity for long-term learning is fundamentally supported by its Global Memory, a structured repository built from the insights of every prior interaction. This isn’t simply data storage; rather, it’s an evolving knowledge base where information is organized and interconnected, allowing the system to recognize patterns, generalize from experiences, and apply previously learned concepts to novel conversational situations. Unlike fleeting short-term memory, Global Memory retains crucial information across sessions, enabling the framework to progressively refine its understanding of user intent and improve the accuracy of its responses over time. This continuous accumulation of knowledge allows for a nuanced and increasingly sophisticated level of conversational ability, extending far beyond the limitations of purely reactive dialogue systems.

The system’s capacity for logical reasoning is significantly enhanced through a dedicated Reflection Module, which undertakes a post-analysis of generated logical forms. This module doesn’t simply produce an output; it critically evaluates it, identifying potential errors or inconsistencies and initiating correction loops to refine the process. By iteratively improving its own outputs based on this internal review, the framework achieves a Macro-F1 Score of 73.08 in logical reasoning tasks. This self-corrective capability represents a crucial step towards more robust and reliable conversational AI, allowing the system to move beyond superficial responses and engage with complex queries in a logically sound manner.

Our method utilizes a framework integrating perception, prediction, and planning to achieve robust robotic manipulation.

Beyond SPARQL: Towards a Future of Conversational Knowledge Access

The current landscape of knowledge access often relies on SPARQL, a query language demanding precise syntax and a deep understanding of data structures. This research introduces a framework designed to overcome these limitations, fostering a more fluid and human-like interaction with knowledge graphs. By integrating large language models with semantic parsing techniques, the system translates natural language questions into executable SPARQL queries, abstracting away the complexities of the underlying data model. This allows users to pose questions in everyday language, receiving accurate and relevant answers without needing specialized knowledge of database querying. The result is a significant advancement towards truly conversational knowledge access, promising to democratize information retrieval and unlock the full potential of structured data.

The convergence of large language models (LLMs) and structured semantic parsing is revolutionizing knowledge access, moving beyond the limitations of traditional query languages. This approach leverages the LLM’s capacity for natural language understanding to interpret complex user requests, translating them into precise, machine-readable semantic representations. These representations then enable efficient querying of knowledge graphs, retrieving accurate and relevant information. By combining the interpretive power of LLMs with the rigor of structured parsing, systems can deliver not only answers, but also personalized and contextualized knowledge experiences, adapting to individual user needs and preferences. This synergy unlocks the potential for more intuitive, efficient, and ultimately, more valuable interactions with vast stores of structured data.

The progression of this research necessitates expansion beyond current limitations, with future efforts concentrating on the capacity to process exponentially larger knowledge graphs. While the framework demonstrates promising results, true conversational intelligence demands the integration of common-sense reasoning – the ability to infer information not explicitly stated. Researchers intend to explore novel techniques, potentially leveraging probabilistic logic and advanced inference engines, to equip the system with this crucial capability. Successfully scaling the framework and incorporating common sense will not only enhance the accuracy and relevance of responses but also unlock the potential for truly insightful and nuanced knowledge discovery, moving beyond simple fact retrieval towards genuine understanding.

This research demonstrates a notable advancement in the field of knowledge access, yielding systems with markedly improved capabilities in understanding and responding to intricate queries. Through a novel framework, the developed systems achieved a 22.6% improvement in F1 Score and a 22.1% improvement in Accuracy when contrasted with established baseline methods. These gains suggest a significant leap towards genuinely intelligent systems, capable of not merely retrieving information, but of processing and interpreting complex requests with both precision and efficiency – paving the way for more intuitive and powerful interactions with knowledge graphs and beyond.

The architecture detailed within this work emphasizes a holistic approach to knowledge graph interaction, mirroring the principle that structure dictates behavior. SEAL’s self-evolving mechanism, particularly its use of S-expression cores, builds a resilient system capable of adapting to the nuances of conversational question answering. This resonates with Andrey Kolmogorov’s observation: “The most important thing in science is not to be afraid of making mistakes.” The framework doesn’t aim for immediate perfection but for continuous refinement through agentic learning, embracing iterative improvement as a core tenet. By allowing the system to evolve and correct its own errors, it anticipates and mitigates potential weaknesses, aligning with the understanding that systems break along invisible boundaries – boundaries that are revealed and addressed through persistent self-assessment.

The Road Ahead

The architecture presented in SEAL-a framework built upon the interplay of semantic parsing and agentic learning-offers a compelling, if not entirely surprising, demonstration of emergent capability. One cannot simply graft large language models onto knowledge graphs and expect coherence; the system must internalize a structured method of reasoning. Yet, the current iteration feels less like a solved problem and more like a carefully balanced provisional state. The self-evolving mechanism, while promising, remains heavily reliant on the initial ‘seed’ of S-expressions. A truly robust system will require a means of generating these foundational structures autonomously, perhaps by learning directly from the graph’s inherent topology.

The conversational aspect, too, introduces complexities that extend beyond mere accuracy. Maintaining contextual consistency and adapting to nuanced user queries requires a deeper understanding of intent-a challenge that, predictably, exposes the limitations of relying solely on surface-level linguistic analysis. The bloodstream of information flows only as smoothly as the vessels allow; expanding the scope of reasoning necessitates a parallel expansion in the capacity for disambiguation.

Ultimately, the field appears poised to move beyond simply ‘answering’ questions and towards constructing systems capable of understanding them. The true measure of success will not be the number of correct responses, but the elegance with which the system navigates the inevitable ambiguities inherent in natural language-a task demanding not merely more data, but a more fundamental rethinking of the relationship between knowledge representation and reasoning.

Original article: https://arxiv.org/pdf/2512.04868.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/