@inproceedings{duran-silva-accuosto-2025-dynamic,
title = "Dynamic Reference Extraction and Linking across Multiple Scholarly Knowledge Graphs",
author = "Duran-Silva, Nicolau and
Accuosto, Pablo",
editor = "Accomazzi, Alberto and
Ghosal, Tirthankar and
Grezes, Felix and
Lockhart, Kelly",
booktitle = "Proceedings of the Third Workshop for Artificial Intelligence for Scientific Publications",
month = dec,
year = "2025",
address = "Mumbai, India and virtual",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.wasp-main.9/",
pages = "80--86",
ISBN = "979-8-89176-310-4",
abstract = "References are an important feature of scientific literature; however, they are unstructured, heterogeneous, noisy, and often multilingual. We present a modular pipeline that leverages fine-tuned transformer models for reference location, classification, parsing, retrieval, and re-ranking across multiple scholarly knowledge graphs, with a focus on multilingual and non-traditional sources such as patents and policy documents. Our main contributions are: a unified pipeline for reference extraction and linking across diverse document types, openly released annotated datasets, fine-tuned models for each subtask, and evaluations across multiple scholarly knowledge graphs, enabling richer, more inclusive infrastructures for open research information."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="duran-silva-accuosto-2025-dynamic">
<titleInfo>
<title>Dynamic Reference Extraction and Linking across Multiple Scholarly Knowledge Graphs</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nicolau</namePart>
<namePart type="family">Duran-Silva</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pablo</namePart>
<namePart type="family">Accuosto</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Third Workshop for Artificial Intelligence for Scientific Publications</title>
</titleInfo>
<name type="personal">
<namePart type="given">Alberto</namePart>
<namePart type="family">Accomazzi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tirthankar</namePart>
<namePart type="family">Ghosal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Felix</namePart>
<namePart type="family">Grezes</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kelly</namePart>
<namePart type="family">Lockhart</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Mumbai, India and virtual</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-310-4</identifier>
</relatedItem>
<abstract>References are an important feature of scientific literature; however, they are unstructured, heterogeneous, noisy, and often multilingual. We present a modular pipeline that leverages fine-tuned transformer models for reference location, classification, parsing, retrieval, and re-ranking across multiple scholarly knowledge graphs, with a focus on multilingual and non-traditional sources such as patents and policy documents. Our main contributions are: a unified pipeline for reference extraction and linking across diverse document types, openly released annotated datasets, fine-tuned models for each subtask, and evaluations across multiple scholarly knowledge graphs, enabling richer, more inclusive infrastructures for open research information.</abstract>
<identifier type="citekey">duran-silva-accuosto-2025-dynamic</identifier>
<location>
<url>https://aclanthology.org/2025.wasp-main.9/</url>
</location>
<part>
<date>2025-12</date>
<extent unit="page">
<start>80</start>
<end>86</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Dynamic Reference Extraction and Linking across Multiple Scholarly Knowledge Graphs
%A Duran-Silva, Nicolau
%A Accuosto, Pablo
%Y Accomazzi, Alberto
%Y Ghosal, Tirthankar
%Y Grezes, Felix
%Y Lockhart, Kelly
%S Proceedings of the Third Workshop for Artificial Intelligence for Scientific Publications
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India and virtual
%@ 979-8-89176-310-4
%F duran-silva-accuosto-2025-dynamic
%X References are an important feature of scientific literature; however, they are unstructured, heterogeneous, noisy, and often multilingual. We present a modular pipeline that leverages fine-tuned transformer models for reference location, classification, parsing, retrieval, and re-ranking across multiple scholarly knowledge graphs, with a focus on multilingual and non-traditional sources such as patents and policy documents. Our main contributions are: a unified pipeline for reference extraction and linking across diverse document types, openly released annotated datasets, fine-tuned models for each subtask, and evaluations across multiple scholarly knowledge graphs, enabling richer, more inclusive infrastructures for open research information.
%U https://aclanthology.org/2025.wasp-main.9/
%P 80-86
Markdown (Informal)
[Dynamic Reference Extraction and Linking across Multiple Scholarly Knowledge Graphs](https://aclanthology.org/2025.wasp-main.9/) (Duran-Silva & Accuosto, WASP 2025)
ACL