assessing scientific research papers with knowledge graphs

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Assessing Scientific Research Papers with Knowledge Graphs

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Related Papers

The Semantic Web

Rosaline de Haan

Bibliothek Forschung und Praxis

The transfer of knowledge has not changed fundamentally for many hundreds of years: It is usually document-based-formerly printed on paper as a classic essay and nowadays as PDF. With around 2.5 million new research contributions every year, researchers drown in a flood of pseudo-digitized PDF publications. As a result research is seriously weakened. In this article, we argue for representing scholarly contributions in a structured and semantic way as a knowledge graph. The advantage is that information represented in a knowledge graph is readable by machines and humans. As an example, we give an overview on the Open Research Knowledge Graph (ORKG), a service implementing this approach. For creating the knowledge graph representation, we rely on a mixture of manual (crowd/expert sourcing) and (semi-)automated techniques. Only with such a combination of human and machine intelligence, we can achieve the required quality of the representation to allow for novel exploration and assista...

Companion Proceedings of the Web Conference 2021

Harald Sack

Scientometrics

Diego Kozlowski , Jennifer Dusdal

Over the last century, we observe a steady and exponential growth of scientific publications globally. The overwhelming amount of available literature makes a holistic analysis of the research within a field and between fields based on manual inspection impossible. Automatic techniques to support the process of literature review are required to find the epistemic and social patterns that are embedded in scientific publications. In computer sciences, new tools have been developed to deal with large volumes of data. In particular, deep learning techniques open the possibility of automated end-to-end models to project observations to a new, low-dimensional space where the most relevant information of each observation is highlighted. Using deep learning to build new representations of scientific publications is a growing but still emerging field of research. The aim of this paper is to discuss the potential and limits of deep learning for gathering insights about scientific research articles. We focus on document-level embeddings based on the semantic and relational aspects of articles, using Natural Language Processing (NLP) and Graph Neural Networks (GNNs). We explore the different outcomes generated by those techniques. Our results show that using NLP we can encode a semantic space of articles, while GNN we enable us to build a relational space where the social practices of a research community are also encoded.

Scientific Data

Islam Ebeid

PubMed® is an essential resource for the medical domain, but useful concepts are either difficult to extract or are ambiguous, which has significantly hindered knowledge discovery. To address this issue, we constructed a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, collecting affiliation history and educational background of authors from ORCID®, and identifying fine-grained affiliation data from MapAffil. Through the integration of these credible multi-source data, we could create connections among the bio-entities, authors, articles, affiliations, and funding. Data validation revealed that the BioBERT deep learning method of bio-entity extraction significantly outperformed the state-of-the-art models based on the F1 score (by 0.51%), with the author name disambiguation (AND) achieving an F1 score of 98.09%. PKG can trigger broa...

International Journal of Advanced Trends in Computer Science and Engineering

WARSE The World Academy of Research in Science and Engineering , habib benlahmar , Sara Mifrah

The term "scientific publication" includes several types of scientific communications and digital broadcasts that scientific researchers make of their work towards their peers and an audience of specialists. These publications describe in detail the studies or experiments carried out and the conclusions drawn from them by the authors. They undergo an examination of the value of the results and the rigor of the scientific method used for the work carried out. In this paper we evaluated the quality of a scientific article on the subject (topic), based on its citations and where is it cited, we based on the Topic modeling theme with the choice of LDA algorithms applied to the corpus Nips (1987-2016) for detecting all subjects of each paper and there citations in a first step then on the citations of each article of the corpus and on the Sentiment Analysis using a lexical based approaches. Then we created a csv file containing the link of each paper with the other cited papers (relation cited-citing), and finally generated a semantic graph between these publications.

Serafeim Chatzopoulos

Rodney Kinney

We introduce the Semantic Scholar Graph of References in Context (GORC),1 a large contextual citation graph of 81.1M academic publications, including parsed full text for 8.1M open access papers, across broad domains of science. Each paper is represented with rich paper metadata (title, authors, abstract, etc.), and where available: cleaned full text, section headers, figure and table captions, and parsed bibliography entries. In-line citation mentions in full text are linked to their corresponding bibliography entries, which are in turn linked to in-corpus cited papers, forming the edges of a contextual citation graph. To our knowledge, this is the largest publicly available contextual citation graph; the full text alone is the largest parsed academic text corpus publicly available. We demonstrate the ability to identify similar papers using these citation contexts and propose several applications for language modeling and citation-related tasks.

Andreas Strotmann

Citation analysis is used in research evaluation exercises around the globe, directly affecting the lives of millions of researchers and the expenditure of billions of dollars. It is therefore crucial to seriously address the problems and limitations that plague it. Central amongst critiques of the common practice of citation analysis has long been that it treats all citations equally, be they crucial to the citing paper or perfunctory. Weighting citations by their value to the citing paper has long been proposed as a theoretically promising solution to this problem. Recitation analysis proposes to tune out the large percentage of perfunctory citations in a paper and tune in on crucial ones when performing citation analysis, by ignoring uni-citations (mentioned just once in a paper) and counting and analyzing only re-citations (used again and again in a citing paper). By focusing on core connections in knowledge networks, re-citation analysis can help research evaluation become more...

Dewey Murdick

We describe a strategy for identifying the universe of research publications relevant to the application and development of artificial intelligence. The approach leverages the arXiv corpus of scientific preprints, in which authors choose subject tags for their papers from a set defined by editors. We compose a functional definition of AI relevance by learning these subjects from paper metadata, and then inferring the arXiv-subject labels of papers in larger corpora: Clarivate Web of Science, Digital Science Dimensions, and Microsoft Academic Graph. This yields predictive classification $F_1$ scores between .75 and .86 for Natural Language Processing (cs.CL), Computer Vision (cs.CV), and Robotics (cs.RO). For a single model that learns these and four other AI-relevant subjects (cs.AI, cs.LG, stat.ML, and cs.MA), we see precision of .83 and recall of .85. We evaluate the out-of-domain performance of our classifiers against other sources of topic information and predictions from altern...

RELATED PAPERS

Jaime Teevan

Bhuvan Sharma

Roman Vainshtein

Ronald Denaux

Rajal Nivargi

Proceedings of the Symposium on Applied Computing - SAC '17

Ognjen Arandjelovic

Proceedings of the 6th International Workshop on Mining Scientific Publications - WOSP 2017

Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics

Maria-Esther Vidal

Daniel Amancio

Prabhat Bharti

Control Engineering and Applied Informatics

Ioan Dumitrache

Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

SHASHANK SHAILABH

Lecture Notes in Computer Science

Wolf-tilo Balke

kamal kaushik varanasi

Proceedings of the 10th International Conference on Data Science, Technology and Applications

Alessandro Vaccarino

Gangan Prathap

Procedia Computer Science

Professor Navonil Mustafee

sokiato gulo

Future Generation Computer Systems

Harun Jamil

Frontiers in Research Metrics and Analytics

Δημητρης Παππάς

Journal of Control Engineering and Applied Informatics

Dan Caragea

Proceedings of the 10th International Conference on Knowledge Capture

CHIMAN KWAN

Brian D Davison

Esteban Romero-Frías

2015 Tenth International Conference on Digital Information Management (ICDIM)

Natalie Pang

Jiaming Shen

Olga Mariño

Proceedings of the Thirteenth Language Resources and Evaluation Conference

Terry Ruas , Bela Gipp

Shaurya Rohatgi

Brett Buttliere

Journal of Data and Information Science

cihat ekici

2021 IEEE International Conference on Big Data (Big Data)

FRANCISCO JOSÉ ANDRADES

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2023

• My Account |
• StudentHome |
• TutorHome |
• IntranetHome |
• Contact the OU Contact the OU Contact the OU |
• Accessibility Accessibility

• Postgraduate
• News & media
• Business & apprenticeships

Open Research Online - ORO

Over 50,000 publications from the broad range of OU research

Assessing Scientific Conferences through Knowledge Graphs

Copy the page URI to the clipboard

Angioni, Simone; Salatino, Angelo ; Osborne, Francesco ; Birukou, Aliaksandr; Recupero, Diego Reforgiato and Motta, Enrico (2021). Assessing Scientific Conferences through Knowledge Graphs. In: International Semantic Web Conference (ISWC) 2021: Posters, Demos, and Industry Tracks , 2980.

URL: http://ceur-ws.org/Vol-2980/

Springer Nature is the main publisher of scientific conferences in Computer Science and produces several well-known series of proceedings books, such as LNCS. The editorial team needs to take critical decisions about which conferences to publish as well as actively scan the horizon for identifying emerging ones. In this short paper, we present the Conference Dashboard, a new web application based on a large knowledge graph of scholarly data (1.3B triples) for assessing scientific conferences and informing editorial decisions.

Viewing alternatives

Download history.

• Download Published Version ( PDF / 293kB)

Item Actions

This item URI

• Item ORO ID
• Conference or Workshop Item
• Scholarly Data; Knowledge Graphs; Scholarly Ontologies
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi) Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Copyright Holders
• © 2021 The Authors.
• Depositing User

Recommendations

The Open University

• Contact the OU Contact the OU
• Accessibility
• Conditions of use
• Privacy and cookies
• Modern Slavery Act (pdf 149kb)

OU Community

• OpenLearn: free learning

OU Students Community

• OU Students Association
• OU Students Shop (including exam papers)
• OU Students Forums
• OU Students on Facebook
• OU Students on Twitter
• OU Students Freshers

Find your personal contacts including your tutor and student support team:

For help and support relating to the University's computing resources:

For information, advice and guidance on using the library, referencing styles or finding journals, ebooks and articles for your assignments:

• Help Centre
• Study planning and funding
• Student Charter and policies

© . All rights reserved. The Open University is incorporated by Royal Charter (RC 000391), an exempt charity in England & Wales and a charity registered in Scotland (SC 038302). The Open University is authorised and regulated by the Financial Conduct Authority in relation to its secondary activity of credit broking.