AI4SC @ AAAI 2025

First AAAI Bridge on Artificial Intelligence for Scholarly Communication

FEBRUARY 25 – 26, 2025 | PHILADELPHIA, PENNSYLVANIA, USA

AI4SC @ AAAI 2025

Importants dates

Submission

Program

Recording

The scientific community of today faces the problem of scientific papers overload in their respective domains. To solve this problem, different scientific communities are being working on scientific knowledge extraction and organization so as to provide a reliable and living scientific knowledge base that empowers researchers to query, synthesize and analyse the vast body of scholarly knowledge. However, these communities have operated more or less independently, with limited exchange of ideas, methodologies, methods, theories, etc. Although many tools for assisting researchers during scientific knowledge extraction and organization exist, it has been reported that most researchers continue to depend on manual methods. The first edition of this bridge program at AAAI aims to bring together a broad audience of people composed of students, researchers, and AI-experts actively using or not AI-technologies during their research to identify common problems, facilitate collaborations and define future research directions.

This bridge aims to bring together AI and scientific knowledge extraction and organization leveraging connectionist AI, symbolic AI and neuro-symbolic AI models. To this end, the authors can consider the following research questions:

How to acquire scientific knowledge from research papers? The aim of this question is to document methodologies, methods, and tools being used for scientific knowledge acquisition. Participants will be invited to describe machine learning (ML) models used during the scientific knowledge acquisition process and datasets used to train these models.

How to organise scientific knowledge? Once extracted, scientific knowledge should be organized in such a way that fellow researchers can benefit from it. Participants will describe AI-tools used for scientific knowledge organization.

How to improve the usability of tools for knowledge extraction and organization? Tools for knowledge extraction and organization are used in diverse research disciplines including social sciences, engineering and technology, education, environmental sciences, and business and management. Participants will be invited to describe how user interfaces are used for making easy access to scientific knowledge. Thereafter, a panel discussion will be held to discuss how to make these tools more accessible to researchers in all domains.

Given that LLMs is the state-of-the-art in several NLP tasks, a particular attention will be oriented towards the use of LLMs for scientific knowledge extraction and organization.

Event Goals

Challenges

Address common challenges in the organization of scientific knowledge.

Collaboration

Encourage interdisciplinary collaboration between AI experts and scientific researchers.

Research directions

Define the future research directions for the organization of scientific knowledge.

Key Highlights:

AI techniques like Connectionist AI, Symbolic AI, and Neuro-Symbolic AI.
Practical applications of LLMs and Knowledge Graphs for extracting and organizing scientific knowledge.

KEYNOTES

Christoph Schuhmann

Advance and Democratize Artificial Intelligence through Open Source and Open Science

ORGANIZERS

Dr. Azanzi Jiomekong

Dr. Azanzi Jiomekong is Assistant Professor in Computer Science at the University of Yaounde I in Cameroon and Guest Researcher at TIB. Since 2012, by contributing to national (e.g., EPICAM), regional (e.g., MABO), and international (e.g., ORKG) projects, he has significantly advanced the application of Artificial Intelligence in the food and health domains in Africa, impacting Sustainable Development Goals 1, 2, 3, 4, 8, 9, 10, and 17. He received the second and third ORKG curation grants in 2022 and 2023, and was selected for the AAAI'23 New Faculty Highlights program in 2023. He is currently working on the development of an AI toolkit composed of datasets, connectionist and symbolic models to address the multi-modal nature of food information. This work aims to empower Africa with FAIR data on food so as to impact the population’s health. Dr. Jiomekong is also contributing as a PC member to several AI conferences such as ICBO, NeSy, ECAI, WSDM. He is reviewer of several AI journals such as EAAI, SWJ, Frontiers in AI.

Prof. Sören Auer

Prof. Dr. Sören Auer is Professor of Data Science and Digital Libraries at Leibniz Universität Hannover - one of the leading Technical Universities in Germany (German TU9), and Director of the TIB - Leibniz Information Center for Science and Technology, a member of the Leibniz Association. His research interests are data science, artificial intelligence, knowledge representation, information systems, databases, software engineering, and digital libraries, with a special focus on semantic data interlinking for artificial intelligence. Prof. Auer has made important contributions to semantic technologies, knowledge engineering, and information systems. He is the author or co-author of over 200 peer-reviewed scientific publications, which attracted more than 30.000 citations. He has received several awards, including an ERC Consolidator Grant from the European Research Council, a SWSA ten-year award, the ESWC 7-year Best Paper Award, and the OpenCourseware Innovation Award. He has led several large collaborative research projects, such as the EU H2020 flagship project BigDataEurope. He is co-founder of high potential research and community projects such as the Wikipedia semantification project DBpedia, the Open Research Knowledge Graph, and the innovative technology start-up Eccenca. Prof. Auer was the founding director of the Big Data Value Association, led the semantic data representation in the Industrial/International Data Space, is an expert for industry, European Commission, W3C, the German National Research Data Infrastructure (NFDI), and the European Open Science Cloud (EOSC).

BRIDGE Committee

⚪ Robin Champieux, Oregon Health & Science University, US

⚪ Harald Sack, FIZ Karlsruhe – Leibniz-Institute for Information Infrastructure GmbH

⚪ Oktie Hassanzadeh, IBM Research, IBM Research - Yorktown Heights, New York, USA

⚪ Petr Knoth, Knowledge Media Institute, UK

⚪ Angelo Salatino, KMi, The Open University, Milton Keynes, UK

⚪ Nandana Mihindukulasooriya, IBM Research, New York, USA

⚪ Gollam Rabby, L3S Research Center, Hanover, Germany

⚪ Allard Oelen, affiliation, TIB – Leibniz Information Centre for Science and Technology, Hannover, Germany

⚪ Norbert TSOPZE, University of yaounde I - Department of Computer Science, Sorbonne University, IRD, UMMISCO, F-93143, Yaounde, Cameroon

⚪ Hamed Babaei, TIB – Leibniz Information Centre for Science and Technology, Hannover, Germany

⚪ Soumya Smruti Mishra’s, Amazon Web Services, Santa Clara, US

⚪ Lars Vogt, TIB Leibniz Information Centre for Science and Technology, Hanover, Germany

⚪ Paulin Melatagia Yonta, Department of Computer Science, University of Yaounde I, Yaounde, Cameroon - Sorbonne Université - IRD - UMMISCO - F-93143, Bondy, France

⚪ Gaoussou Camara, Alioune Diop University of Bambey, Bambey, Senegal

⚪ Sanju Tiwari, Sharda University, Greater Noida, Delhi NCR, India

⚪ Francisco Bolanos, KMi, The Open University, Milton Keynes, UK

⚪ Francesco Osborne, KMi, The Open University, Milton Keynes, UK

⚪ Enrico Motta

Topics and Research Questions

How to Acquire Scientific Knowledge?

We will explore AI methodologies, tools, and models like SVMs and Neural Networks, along with datasets used for training.

How to Organize Scientific Knowledge?

AI tools such as knowledge graphs and ontologies will be highlighted for structuring and organizing extracted data.

How to Improve Tools for Knowledge Extraction?

We will examine usability challenges and focus on improving access to knowledge in different fields.

We would be delighted to have you with us for this event.

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