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Knowledge graphs for the life sciences: Recent developments, challenges and opportunities
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 …
and include chemistry, biology, medicine, and a range of other related disciplines. Research …
The sciqa scientific question answering benchmark for scholarly knowledge
Abstract Knowledge graphs have gained increasing popularity in the last decade in science
and technology. However, knowledge graphs are currently relatively simple to moderate …
and technology. However, knowledge graphs are currently relatively simple to moderate …
Unsupervised machine learning approaches for test suite reduction
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 …
development cycle. As software systems grow in size, complexity, and functionality, the …
Knowledge engineering using large language models
Knowledge engineering is a discipline that focuses on the creation and maintenance of
processes that generate and apply knowledge. Traditionally, knowledge engineering …
processes that generate and apply knowledge. Traditionally, knowledge engineering …
[HTML][HTML] Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings
Abstract Knowledge representation (KR) is vital in designing symbolic notations to represent
real-world facts and facilitate automated decision-making tasks. Knowledge graphs (KGs) …
real-world facts and facilitate automated decision-making tasks. Knowledge graphs (KGs) …
MLSea: a semantic layer for discoverable machine learning
Abstract With the Machine Learning (ML) field rapidly evolving, ML pipelines continuously
grow in numbers, complexity and components. Online platforms (eg, OpenML, Kaggle) aim …
grow in numbers, complexity and components. Online platforms (eg, OpenML, Kaggle) aim …
Trust, accountability, and autonomy in knowledge graph-based AI for self-determination
Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent
decision-making and a wide range of Artificial Intelligence (AI) services across major …
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
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 …
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
The continued success of Large Language Models (LLMs) and other generative artificial
intelligence approaches highlights the advantages that large information corpora can have …
intelligence approaches highlights the advantages that large information corpora can have …
On the benefits of OWL-based knowledge graphs for neural-symbolic systems
Knowledge graphs, as understood within the Semantic Web and Knowledge Representation
communities, are more than just graph data. OWL-based knowledge graphs offer the …
communities, are more than just graph data. OWL-based knowledge graphs offer the …