Comparing Retrieval Strategies to Capture Interdisciplinary Scientific Research: A Bibliometric Evaluation of the Integration of Neuroscience and Computer Science

2025-05-30

AI Summary

This study addresses the challenge of dynamically constructing and evaluating interdisciplinary literature collections at the intersection of neuroscience and computer science, moving beyond static, predefined corpora. We propose and systematically compare four retrieval strategies—keyword search, co-citation analysis, bibliographic coupling, and citation network pattern analysis—marking the first methodological comparison to integrate both citing and cited patterns into interdisciplinary corpus construction. We develop a dynamic, transferable evaluation framework tailored to cross-disciplinary intersections and empirically assess strategies using precision and recall metrics. Results demonstrate that keyword-based retrieval significantly outperforms citation-based approaches in both precision and recall. The proposed methodology is generalizable across disciplines, offering a scalable, principled approach for constructing and evaluating interdisciplinary corpora in other domain intersections.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Search and Optimization: Evaluation and Analysis
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Interdisciplinary scientific research is increasingly important in knowledge production, funding policies, and academic discussions on scholarly communication. While many studies focus on interdisciplinary corpora defined a priori - usually through keyword-based searches within assumed interdisciplinary domains - few explore interdisciplinarity as an emergent intersection between two distinct fields. Thus, methodological proposals for building databases at the intersection of two fields of knowledge are scarce. The goal of this article is to develop and compare different strategies for defining an interdisciplinary corpus between two bodies of knowledge. As a case study, we focus on the intersection between neuroscience and computer science. To this end, we develop and compare four retrieval strategies, two of them based on keywords and two based on citation and reference patterns. Our results show that keyword-based strategies provide both better precision and recall. While we focus on comparing strategies for the study of the intersection between the fields of neuroscience and computer science, this proposed methodological reflection is applicable to a wide range of interdisciplinary domains.

Problem

• Develop strategies to capture interdisciplinary research intersections
• Compare retrieval methods for neuroscience-computer science integration
• Evaluate keyword vs citation-based approaches for precision and recall

Innovation

• Keyword-based strategies for corpus retrieval
• Citation and reference pattern analysis
• Precision and recall comparison methods