21 papers · ranked by Valyu relevance
Ciyuan Peng, Feng Xia, Mehdi Naseriparsa, Francesco Osborne
With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As graph data, knowledge graphs accumulate and convey knowledge of the real world. It has been well-recognized that knowledge graphs…
Xintao Wang, Qianwen Yang, Yongting Qiu, Jiaqing Liang + 4 more
'Zhouhong Gu' 'Yanghua Xiao' 'Wei Wang'] Large language models (LLMs) have demonstrated impressive impact in the field of natural language processing, but they still struggle with several issues regarding, such as completeness, timeliness, faithfulness and adaptability. While recent efforts have focuses on connecting…
Jian Yang, Gang Xiao, Yu-Long Shen, Wei Jiang + 3 more
'Ying Zhang' 'Jinghui Peng'] Pre-trained language models learn informative word representations on a large-scale text corpus through selfsupervised learning, which has achieved promising performance in fields of natural language processing (NLP) after fine-tuning. These models, however, suffer from poor robustness and…
Matt Beton, Simran Chana, Simran Chana
The mammalian brain has developed a solution to a problem that current AI architectures are yet to solve: storing specific episodic facts without corrupting general semantic knowledge. Neuroscience explains this capacity through Complementary Learning Systems theory, which posits that biological memory requires two…
Authors not listed
Large language models (LLMs) have garnered increasing attention owing to their potential as collaborative assistants in scientific studies. However, adapting an LLM to specialized domains remains challenging because of the difficulty in incorporating domain-specific knowledge. In the present study, we propose a…
Yuan Zhang, Feng Pan, Xin Sui, Kaixian Yu + 15 more
To cope with the rapid growth of scientific publications and data in biomedical research, knowledge graphs (KGs) have emerged as a powerful data structure for integrating large volumes of heterogeneous data to facilitate accurate and efficient information retrieval and automated knowledge discovery (AKD). However…
Shaked Launer-Wachs, Hillel Taub-Tabib, Yoav Goldberg, Yosi Shamay
As knowledgebases become increasingly important for structuring vast amounts of scientific knowledge and making it accessible to researchers, their construction entails expensive multi-year projects involving teams of bio-curators, computer scientists, or both. This restricts the coverage of existing knowledgebases to…
Authors not listed
The interdisciplinary nature of redox flow batteries (RFBs), spanning chemistry, materials, and engineering, has led to a vast and fragmented body of research, hindering the efficient synthesis of knowledge. An intelligent question-answering system is there-fore essential to organize this dispersed knowledge, enhance…
Amy Xin, Jinxin Liu, Zijun Yao, Z. -Y. Lee + 3 more
Knowledge Reasoning Authors: ['Amy Xin' 'Jinxin Liu' 'Zijun Yao' 'Z. -Y. Lee' 'Shulin Cao' 'Lei Hou' 'Juanzi Li'] Recent advancements in large language models (LLMs) have led to significant improvements in various natural language processing tasks, but it is still challenging for LLMs to perform knowledgeintensive…
Gerhard Weikum, Xin Dong, Simon Razniewski, Fabian M. Suchanek
Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for…
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
Guangrong Qin, Kamileh Narsinh, Qi Wei, Jared C. Roach + 15 more
As large clinical and multiomics datasets and knowledge resources accumulate, they need to be transformed into computable and actionable information to support automated reasoning. These datasets range from laboratory experiment results to electronic health records (EHRs). Barriers to accessibility and sharing of such…
Authors not listed
We have developed Aitomia – a platform powered by AI to assist in performing AI-driven atomistic and quantum chemical (QC) simulations. This evolving intelligent assistant platform is equipped with chatbots and AI agents to help experts and guide non-experts in setting up and running atomistic simulations, monitoring…
Ruud M.W.J. Berkers, Marieke van der Linden, David A. Neville, Marlieke T.R. van Kesteren + 3 more
Knowledge is acquired by generalization and integration across learning experiences, which can then be applied to future instances. This study provides novel insights into how linguistic associative knowledge is acquired by systematically tracking schematic knowledge formation while participants were learning an…
Khalid Rasheed Memon, Bilqees Ghani, Syed Irfan Hyder, Heesup Han + 3 more
'Muhammad Zada' 'Antonio Ariza-Montes' 'Marcelo Arraño-Muñoz'] The fourth industrial revolution will be ushered in by future high technology, and as a result, the world will face new difficulties relating to people, the environment, and profitability. Accordingly, the competitive edge and long-term viability of…
Mayla R. Boguslav, Nourah M. Salem, Elizabeth K. White, Katherine J. Sullivan + 5 more
Research progresses through the process of making the unknown unknowns into known unknowns, then into knowns. Many knowledge-bases exist to capture the knowns and can be used by graduate students to explore a topic or help researchers contextualize experimental results using concept enrichment methods. The unknowns…
Philip R. O. Payne, Fran Lewitter, Maricel Kann
The modern biomedical research and healthcare delivery domains have seen an unparalleled increase in the rate of innovation and novel technologies over the past several decades. Catalyzed by paradigm-shifting public and private programs focusing upon the formation and delivery of genomic and personalized medicine, the…
Authors not listed
Step-by-step thinking is essential in all domains of chemical sciences and engineering. While machine learning tools are broadly used, algorithms that automate reasoning are far less common. We elaborate on seven categories of human reasoning activities and connect each to applications in chemical science and…
Stefan V Pantazi, José F Arocha, Jochen R Moehr
Background The "applied" nature distinguishes applied sciences from theoretical sciences. To emphasize this distinction, we begin with a general, meta-level overview of the scientific endeavor. We introduce the knowledge spectrum and four interconnected modalities of knowledge. In addition to the traditional…
Barbara Kump, Johannes Moskaliuk, Ulrike Cress, Joachim Kimmerle
Contemporary research into socio-cognitive foundations of organizational learning tends to disregard the distinction between declarative and non-declarative knowledge. By reviewing the literature from organizational learning research and cognitive psychology we explain that this distinction is crucial. We describe the…
David G. Nagy, Balázs Török, Gergő Orbán
It has extensively been documented that human memory exhibits a wide range of systematic distortions, which have been associated with resource constraints. Resource constraints on memory can be formalised in the normative framework of lossy compression, however traditional lossy compression algorithms result in…