KnowCoder-V2: Deep Knowledge Analysis (2506.06881v1)

Abstract: Deep knowledge analysis tasks always involve the systematic extraction and association of knowledge from large volumes of data, followed by logical reasoning to discover insights. However, to solve such complex tasks, existing deep research frameworks face three major challenges: 1) They lack systematic organization and management of knowledge; 2) They operate purely online, making it inefficient for tasks that rely on shared and large-scale knowledge; 3) They cannot perform complex knowledge computation, limiting their abilities to produce insightful analytical results. Motivated by these, in this paper, we propose a \textbf{K}nowledgeable \textbf{D}eep \textbf{R}esearch (\textbf{KDR}) framework that empowers deep research with deep knowledge analysis capability. Specifically, it introduces an independent knowledge organization phase to preprocess large-scale, domain-relevant data into systematic knowledge offline. Based on this knowledge, it extends deep research with an additional kind of reasoning steps that perform complex knowledge computation in an online manner. To enhance the abilities of LLMs to solve knowledge analysis tasks in the above framework, we further introduce \textbf{\KCII}, an LLM that bridges knowledge organization and reasoning via unified code generation. For knowledge organization, it generates instantiation code for predefined classes, transforming data into knowledge objects. For knowledge computation, it generates analysis code and executes on the above knowledge objects to obtain deep analysis results. Experimental results on more than thirty datasets across six knowledge analysis tasks demonstrate the effectiveness of \KCII. Moreover, when integrated into the KDR framework, \KCII can generate high-quality reports with insightful analytical results compared to the mainstream deep research framework.

• The paper introduces the KDR framework, separating knowledge organization and reasoning to efficiently manage and process large datasets.
• The model leverages unified code generation to convert raw text into structured objects, enabling advanced logical deduction and statistical inference.
• Experimental results show that KnowCoder-V2 excels in ontology expansion, multilingual extraction, and KBQA, demonstrating superior robustness and scalability.

Essay on KnowCoder-V2: Deep Knowledge Analysis

The paper "KnowCoder-V2: Deep Knowledge Analysis" contributes to the domain of deep knowledge analysis by addressing critical challenges related to knowledge management, operation efficiency, and computation complexity. The authors propose a Knowledgeable Deep Research (KDR) framework that integrates an offline phase for knowledge organization with an online phase for knowledge reasoning, leveraging the LLM KnowCoder-V2 to facilitate these processes through code generation. This essay provides an expert overview of the methodologies, experimental findings, and implications of this research.

Knowledgeable Deep Research Framework

The KDR framework presented by Li et al. aims to overcome limitations in existing frameworks regarding knowledge management, operational inefficiency, and shallow computation. The key innovation lies in separating the knowledge organization phase from the reasoning phase, allowing for structured and efficient management of large-scale domain-specific data.

1. Knowledge Organization: The framework employs an ontology-based approach where data is preprocessed into structured formats according to predefined classes. This phase involves generating instantiation code for knowledge objects, ensuring comprehensive alignment with existing ontologies, and updating knowledge bases dynamically.
2. Knowledge Reasoning: The reasoning phase adopts an online approach, leveraging structured knowledge for complex computations. This is facilitated through code generation that enables sophisticated operations such as logical deduction, statistical inference, and dynamic querying.

KnowCoder-V2 Model

KnowCoder-V2 emerges as the pivotal LLM within the KDR framework, designed to seamlessly bridge the gap between knowledge organization and reasoning. It employs a unified code generation strategy, which significantly enhances its ability to perform intricate computational tasks.

• Organizational Tasks: KnowCoder-V2 generates Python classes to represent concepts and instances, transforming raw textual data into structured knowledge objects. This internalization within the model parameters enables efficient management of variable prompt lengths, overcoming common limitations seen in other LLMs.
• Computational Tasks: The model generates analysis code that executes on the structured objects, providing deep insights. The iterative error-checking cycle further ensures robustness and accuracy, allowing extensive manipulation of complex datasets.

Experimental Evaluation

The paper reports significant experimental results across a diverse range of tasks, including ontology expansion, knowledge extraction, and knowledge base question answering (KBQA):

• Ontology Expansion: KnowCoder-V2 demonstrates superior performance in identifying semantic relations within ontologies, outperforming self-supervised and one-shot baseline models across evaluated datasets.
• Knowledge Extraction: The model exhibits strong multilingual and multi-event extraction capabilities, surpassing state-of-the-art models in specialized domain benchmarks such as BC2GM and SCIERC. Its efficiency in handling extensive schema scenarios with considerably shortened prompts is noteworthy.
• Robustness Evaluation: KnowCoder-V2 maintains robust performance across varied perturbations, ranking highest among evaluated models, highlighting its resilience to complex and extended texts.
• KBQA and Report Generation: The KDR framework, empowered by KnowCoder-V2, delivers accurate analysis and high-quality reports with substantial insights. Furthermore, it surpasses both open-source and closed-source deep research systems in report generation, particularly in coherence and completeness.

Implications and Future Directions

The KnowCoder-V2 model and the KDR framework exemplify progress in advancing the computational capabilities of LLMs for deep knowledge analysis, promising practical and theoretical implications. The structured management and reasoning approaches could be broadly applicable in areas such as automated scientific research, intelligent data processing, and strategic decision-making.

Future research could explore extending KnowCoder-V2's capabilities further, by incorporating reinforcement learning mechanisms for adaptive query generation during reasoning processes, or by enhancing its ontology alignment methodologies. The scalability of the KDR framework within real-time data environments also remains an intriguing avenue for exploration, potentially broadening its applicability across diverse industrial domains.

In conclusion, this paper provides valuable insights into leveraging LLMs for sophisticated knowledge analysis tasks, establishing a structured pathway through the KDR framework for realizing complex reasoning and management objectives. Through such innovations, the authors set a promising precedent for future advancements in this field.