Comparing Retrieval Strategies to Capture Interdisciplinary Scientific Research: A Bibliometric Evaluation of the Integration of Neuroscience and Computer Science
- URL: http://arxiv.org/abs/2506.03187v1
- Date: Fri, 30 May 2025 19:29:18 GMT
- Title: Comparing Retrieval Strategies to Capture Interdisciplinary Scientific Research: A Bibliometric Evaluation of the Integration of Neuroscience and Computer Science
- Authors: Malena Mendez Isla, Agustin Mauro, Diego Kozlowski,
- Abstract summary: Interdisciplinary scientific research is increasingly important in knowledge production, funding policies, and academic discussions.<n>We develop and compare different strategies for defining an interdisciplinary corpus between two bodies of knowledge.<n>Our results show that keyword-based strategies provide both better precision and recall.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- A Hybrid AI Methodology for Generating Ontologies of Research Topics from Scientific Paper Corpora [6.384357773998868]
Sci-OG is a semi-auto-mated methodology for generating research topic.<n>This paper presents Sci-OG, a semi-auto-mated methodology for generating research topic.<n>We evaluate this approach against a range of alternative solutions using a dataset of 21,649 manually annotated semantic triples.
arXiv Detail & Related papers (2025-08-06T08:48:14Z) - The Incomplete Bridge: How AI Research (Mis)Engages with Psychology [30.36064725942852]
Social sciences have accumulated a rich body of theories and methodologies for investigating the human mind and behaviors.<n> Focusing on psychology as a prominent case, this study explores the interdisciplinary synergy between AI and the field.<n>We identify key patterns of interdisciplinary integration, locate the psychology domains most frequently referenced, and highlight areas that remain underexplored.
arXiv Detail & Related papers (2025-07-30T17:03:59Z) - The Blind Men and the Elephant: Mapping Interdisciplinarity in Research on Decentralized Autonomous Organizations [0.0]
Decentralized Autonomous Organizations (DAOs) are attracting interdisciplinary interest, particularly in business, economics, and computer science.<n>Research remains fragmented across disciplines, limiting a comprehensive understanding of their potential.<n>Current research remains predominantly applied and case-driven, with limited theoretical integration.
arXiv Detail & Related papers (2025-02-14T07:06:43Z) - Trajectories of Change: Approaches for Tracking Knowledge Evolution [0.0]
We explore local vs. global evolution of knowledge systems through the framework of socio-epistemic networks (SEN)<n>We first use information-theoretic measures based on relative entropy to detect semantic shifts, assess their significance, and identify key driving features.<n>Second, variations in document embedding reveal changes in semantic neighbourhoods, tracking how concentration of similar documents increase, remain stable, or disperse.
arXiv Detail & Related papers (2024-12-31T11:09:37Z) - Delineating Feminist Studies through bibliometric analysis [1.1060425537315088]
This paper proposes a novel approach for identifying gender/sex related publications scattered across diverse scientific disciplines.<n>We employ bibliometric techniques, natural language processing (NLP) and manual curation to compile a dataset of scientific publications.<n>The resulting dataset comprises over 1.9 million scientific documents published between 1668 and 2023, spanning four languages.
arXiv Detail & Related papers (2024-11-27T12:52:51Z) - Argumentation and Machine Learning [4.064849471241967]
This chapter provides an overview of research works that present approaches with some degree of cross-fertilisation between Computational Argumentation and Machine Learning.
Two broad themes representing the purpose of the interaction between these two areas were identified.
We evaluate the spectrum of works across various dimensions, including the type of learning and the form of argumentation framework used.
arXiv Detail & Related papers (2024-10-31T08:19:58Z) - Re-mine, Learn and Reason: Exploring the Cross-modal Semantic
Correlations for Language-guided HOI detection [57.13665112065285]
Human-Object Interaction (HOI) detection is a challenging computer vision task.
We present a framework that enhances HOI detection by incorporating structured text knowledge.
arXiv Detail & Related papers (2023-07-25T14:20:52Z) - A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and
Why? [84.46288849132634]
We propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques.
We define three variables to encompass diverse facets of the evolution of research topics within NLP.
We utilize a causal discovery algorithm to unveil the causal connections among these variables using observational data.
arXiv Detail & Related papers (2023-05-22T11:08:00Z) - Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph [99.28342534985146]
Existing literature and knowledge platforms on COVID-19 only focus on collecting papers on biology and medicine.
We propose Covidia, COVID-19 interdisciplinary academic knowledge graph to bridge the gap between knowledge of COVID-19 on different domains.
arXiv Detail & Related papers (2023-04-14T16:45:38Z) - Deep Learning for Human Parsing: A Survey [54.812353922568995]
We provide an analysis of state-of-the-art human parsing methods, covering a broad spectrum of pioneering works for semantic human parsing.
We introduce five insightful categories: (1) structure-driven architectures exploit the relationship of different human parts and the inherent hierarchical structure of a human body, (2) graph-based networks capture the global information to achieve an efficient and complete human body analysis, (3) context-aware networks explore useful contexts across all pixel to characterize a pixel of the corresponding class, and (4) LSTM-based methods can combine short-distance and long-distance spatial dependencies to better exploit abundant local and global contexts.
arXiv Detail & Related papers (2023-01-29T10:54:56Z) - Foundations and Recent Trends in Multimodal Machine Learning:
Principles, Challenges, and Open Questions [68.6358773622615]
This paper provides an overview of the computational and theoretical foundations of multimodal machine learning.
We propose a taxonomy of 6 core technical challenges: representation, alignment, reasoning, generation, transference, and quantification.
Recent technical achievements will be presented through the lens of this taxonomy, allowing researchers to understand the similarities and differences across new approaches.
arXiv Detail & Related papers (2022-09-07T19:21:19Z) - Revise and Resubmit: An Intertextual Model of Text-based Collaboration
in Peer Review [52.359007622096684]
Peer review is a key component of the publishing process in most fields of science.
Existing NLP studies focus on the analysis of individual texts.
editorial assistance often requires modeling interactions between pairs of texts.
arXiv Detail & Related papers (2022-04-22T16:39:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.