Improving Scientific Document Retrieval with Academic Concept Index

2026-01-02

arXiv.org

AI Summary

Scientific document retrieval faces significant challenges due to the scarcity of domain-specific labeled data and the highly specialized nature of technical terminology, which often leads existing methods to suffer from conceptual redundancy or insufficient coverage. To address these limitations, this work proposes an academic concept indexing framework that integrates a structured scholarly taxonomy with large language models to extract and organize key concepts. The framework introduces two novel mechanisms: Concept-Coverage-aware Query Generation (CCQGen) and Concept-Focused Context Expansion (CCExpand), which jointly enhance the retrieval system’s capacity to understand and match scientific semantics. Experimental results demonstrate that the proposed approach substantially improves query quality, concept alignment, and overall retrieval effectiveness, outperforming current state-of-the-art methods on scientific document retrieval benchmarks.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Natural Language Processing: Information Extraction

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Adapting general-domain retrievers to scientific domains is challenging due to the scarcity of large-scale domain-specific relevance annotations and the substantial mismatch in vocabulary and information needs. Recent approaches address these issues through two independent directions that leverage large language models (LLMs): (1) generating synthetic queries for fine-tuning, and (2) generating auxiliary contexts to support relevance matching. However, both directions overlook the diverse academic concepts embedded within scientific documents, often producing redundant or conceptually narrow queries and contexts. To address this limitation, we introduce an academic concept index, which extracts key concepts from papers and organizes them guided by an academic taxonomy. This structured index serves as a foundation for improving both directions. First, we enhance the synthetic query generation with concept coverage-based generation (CCQGen), which adaptively conditions LLMs on uncovered concepts to generate complementary queries with broader concept coverage. Second, we strengthen the context augmentation with concept-focused auxiliary contexts (CCExpand), which leverages a set of document snippets that serve as concise responses to the concept-aware CCQGen queries. Extensive experiments show that incorporating the academic concept index into both query generation and context augmentation leads to higher-quality queries, better conceptual alignment, and improved retrieval performance.

Problem

• scientific document retrieval
• academic concept
• synthetic query generation
• context augmentation
• domain adaptation

Innovation

• academic concept index
• synthetic query generation
• context augmentation
• concept coverage
• scientific document retrieval