Knowledge graphs for the life sciences: Recent developments, challenges and opportunities

J Chen, H Dong, J Hastings, E Jiménez-Ruiz… - arxiv preprint arxiv …, 2023‏ - arxiv.org
The term life sciences refers to the disciplines that study living organisms and life processes,
and include chemistry, biology, medicine, and a range of other related disciplines. Research …

The sciqa scientific question answering benchmark for scholarly knowledge

S Auer, DAC Barone, C Bartz, EG Cortes… - Scientific Reports, 2023‏ - nature.com
Abstract Knowledge graphs have gained increasing popularity in the last decade in science
and technology. However, knowledge graphs are currently relatively simple to moderate …

Unsupervised machine learning approaches for test suite reduction

A Sebastian, H Naseem, C Catal - Applied Artificial Intelligence, 2024‏ - Taylor & Francis
Ensuring quality and reliability mandates thorough software testing at every stage of the
development cycle. As software systems grow in size, complexity, and functionality, the …

Knowledge engineering using large language models

BP Allen, L Stork, P Groth - arxiv preprint arxiv:2310.00637, 2023‏ - arxiv.org
Knowledge engineering is a discipline that focuses on the creation and maintenance of
processes that generate and apply knowledge. Traditionally, knowledge engineering …

[HTML][HTML] Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings

D Rincon-Yanez, C Ounoughi, B Sellami… - Journal of King Saud …, 2023‏ - Elsevier
Abstract Knowledge representation (KR) is vital in designing symbolic notations to represent
real-world facts and facilitate automated decision-making tasks. Knowledge graphs (KGs) …

MLSea: a semantic layer for discoverable machine learning

I Dasoulas, D Yang, A Dimou - European Semantic Web Conference, 2024‏ - Springer
Abstract With the Machine Learning (ML) field rapidly evolving, ML pipelines continuously
grow in numbers, complexity and components. Online platforms (eg, OpenML, Kaggle) aim …

Trust, accountability, and autonomy in knowledge graph-based AI for self-determination

LD Ibáñez, J Domingue, S Kirrane… - arxiv preprint arxiv …, 2023‏ - arxiv.org
Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent
decision-making and a wide range of Artificial Intelligence (AI) services across major …

Describing and organizing semantic web and machine learning systems in the SWeMLS-KG

FJ Ekaputra, M Llugiqi, M Sabou, A Ekelhart… - European Semantic …, 2023‏ - Springer
The overall AI trend of creating neuro-symbolic systems is reflected in the Semantic Web
community with an increased interest in the development of systems that rely on both …

Large process models: A vision for business process management in the age of generative ai

T Kampik, C Warmuth, A Rebmann, R Agam… - KI-Künstliche …, 2024‏ - Springer
The continued success of Large Language Models (LLMs) and other generative artificial
intelligence approaches highlights the advantages that large information corpora can have …

On the benefits of OWL-based knowledge graphs for neural-symbolic systems

D Herron, E Jiménez-Ruiz… - Proceedings of the 17th …, 2023‏ - openaccess.city.ac.uk
Knowledge graphs, as understood within the Semantic Web and Knowledge Representation
communities, are more than just graph data. OWL-based knowledge graphs offer the …