29 papers · ranked by Valyu relevance
Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d’Amato + 14 more
'Gerard de Melo' 'Claudio Gutiérrez' 'Sabrina Kirrane' 'José Emilio Labra Gayo' 'Roberto Navigli' 'Sebastian Neumaier' 'Axel-Cyrille Ngonga Ngomo' 'Axel Polleres' 'Sabbir M. Rashid' 'Anisa Rula' 'Lukas Schmelzeisen' 'Juan Sequeda' 'Steffen Staab' 'Antoine Zimmermann'] AIDAN HOGAN, IMFD, DCC, Universidad de Chile, Chile…
Agnieszka Lawrynowicz
Knowledge can take different forms. We distinguish between declarative knowledge (knowing something) or procedural knowledge (knowing how, know-how), sensorimotor knowledge (riding a bicycle), and affective knowledge (deep understanding). The classic definition of knowledge derived from philosophy defines knowledge as…
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…
Federico Bianchi, Gaetano Rossiello, Luca Costabello, Matteo Palmonari + 1 more
'Matteo Palmonari' 'Pasquale Minervini'] Abstract. Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces. In this chapter, we introduce the reader to the concept of knowledge graph embeddings by explaining what they…
Davide Riva, Cristina Rossetti
Trends and Methods Authors: ['Davide Riva' 'Cristina Rossetti'] In this essay we discuss the recent trends in visual analysis and exploration of Knowledge Graphs, particularly in conjunction with Knowledge Graph Embedding techniques. We present an overview of the current state of visualization techniques and frameworks…
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…
Qizheng Wang, Fan Yang, Lijie Quan, Mengjie Fu + 2 more
'Ju Wang'] Neurological disorders (e.g., Alzheimer’s disease and Parkinson’s disease) and mental disorders (e.g., depression and anxiety), pose huge challenges to global public health. The pathogenesis of these diseases can usually be attributed to many factors, such as genetic, environmental and socioeconomic status…
Zhisheng Huang, Qing Hu, Mingqun Liao, Cong Miao + 2 more
Kawasaki Disease is a vasculitis syndrome that is extremely harmful to children. Kawasaki Disease can cause severe symptoms of ischemic heart disease or develop into ischemic heart disease, leading to death in children. Researchers and clinicians need to analyze various knowledge and data resources to explore aspects…
Yichun Feng, Lu Zhou, Yikai Zheng, Ruikun He + 2 more
In recent years, Large Language Models (LLMs) have shown promise in various domains, notably in biomedical sciences. However, their real-world application is often limited by issues like erroneous outputs and hallucinatory responses. We developed the Knowledge Graph-based Thought (KGT) framework, an innovative solution…
Valentina Janev, Damien Graux, Hajira Jabeen, Emanuel Sallinger + 3 more
'Luigi Bellomarini' 'Emanuel Sallinger' 'Sahar Vahdati'] Knowledge Graphs (KGs) are one of the key trends among the next wave of technologies. Many definitions exist of what a Knowledge Graph is, and in this chapter, we are going to take the position that precisely in the multitude of definitions lies one of the…
Jianqiang Dong, Junwu Liu, Yifan Wei, Peilin Huang + 1 more
In biomedical research, the utilization of Knowledge Graph (KG) has proven valuable in gaining deep understanding of various processes. In this study, we constructed a comprehensive biomedical KG, named as MegaKG, by integrating a total of 23 primary data sources, which finally consisted of 188, 844 nodes/entities and…
Ali Hur, Naeem Khalid Janjua, Mohiuddin Ahmed
-- Global datasphere is increasing fast, and it is expected to reach 175 Zettabytes by 20251 . However, most of the content is unstructured and is not understandable by machines. Structuring this data into a knowledge graph enables multitudes of intelligent applications such as deep question answering, recommendation…
Amit Sheth, Swati Padhee, Amélie Gyrard
Knowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities. However, for dynamic real-world applications such as social networks, recommender systems, computational biology, relational knowledge representation has emerged as a challenging research…
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…
Wei Tang, Xueying Zhang, Deen Feng, Yipeng Wang + 3 more
'Hanhua Qu' 'Umutcan Simsek'] Alpine skiing, as an outdoor winter sport, is particularly vulnerable to the variation of meteorological conditions. Scattered and multi-source big data cannot be fully utilized to conduct effective decision analyses by conventional data analysis methods. Presently, knowledge graphs are…
Meije Mathé, Guillaume Laisney, Olivier Filangi, Franck Giacomoni + 4 more
Knowledge graphs (KGs) are a robust formalism for structuring biomedical knowledge, but large-scale KGs often require complex queries, are difficult for non-experts to explore, and lack real-world context (such as experimental data, clinical conditions, patients symptoms). This limits their usability for addressing…
Tiffany J. Callahan, Ignacio J. Tripodi, Lawrence E. Hunter, William A. Baumgartner
Although knowledge graphs (KGs) are used extensively in biomedical research to model complex phenomena, many KG construction methods remain largely unable to account for the use of different standardized terminologies or vocabularies, are often difficult to use, and perform poorly as the size of the KG increases in…
Favour James, Christopher Churas, Dexter Pratt, Augustin Luna
Knowledge graphs (KGs) are powerful tools for structuring and analyzing biological information due to their ability to represent data and improve queries across heterogeneous datasets. However, constructing KGs from unstructured literature remains challenging due to the cost and expertise required for manual curation.…
Lihui Liu, Zihao Wang, Hanghang Tong
Perspective Authors: ['Lihui Liu' 'Zihao Wang' 'Hanghang Tong'] Knowledge graph reasoning is pivotal in various domains such as data mining, artificial intelligence, the Web, and social sciences. These knowledge graphs function as comprehensive repositories of human knowledge, facilitating the inference of new…
David Geleta, Andriy Nikolov, Gavin Edwards, Anna Gogleva + 10 more
The use of knowledge graphs as a data source for machine learning methods to solve complex problems in life sciences has rapidly become popular in recent years. Our Biological Insights Knowledge Graph (BIKG) combines relevant data for drug development from public as well as internal data sources to provide insights for…
Chenwei Yan, Xinyue Fang, Xiaotong Huang, Chenyi Guo + 1 more
The knowledge graph is one of the essential infrastructures of artificial intelligence. It is a challenge for knowledge engineering to construct a high-quality domain knowledge graph for multi-source heterogeneous data. We propose a complete process framework for constructing a knowledge graph that combines structured…
Frédéric Burdet, Pierre-Marie Allard, Louis-Felix Nothias, Olivier Kirchhoffer + 16 more
Plants have a complex chemo-diversity and represent a reservoir of potential new therapeutic agents. Within a Swiss research project, six scientific research groups from different disciplines are collaborating to investigate a collection of more than 17’000 unique dried plant extracts. It aims to find new bioactive…
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…
Gergely Zahoránszky-Kőhalmi, Brandon Walker, Nathan Miller, Brett Yang + 11 more
The recent SmartGraph platform facilitates the execution of complex drug-discovery workflows with ease in the network-pharmacology paradigm. However, at the time of its publication, we identified the need for the development of an Application Programming Interface (API) that could promote biomedical data integration…
Authors not listed
Molecular property prediction has become essential in accelerating advancements in drug discovery and materials science. Graph Neural Networks have recently demonstrated remarkable success in molecular representation learning; however, their broader adoption is impeded by two significant challenges: (1) data scarcity…
Charlotte Neidiger, Tarek Saier, Kai Kühn, Victor Larignon + 12 more
In this work, a concept for an open chemistry knowledge base was developed to integrate chemical research results into a collaboratively usable platform. To achieve this, we enhanced Semantic MediaWiki (SMW) to support the collection and structured summary of chemical data contained in publications. We implemented…
Authors not listed
Computational methods for predictive modeling have been increasingly utilized in the early stages of drug discovery to supplement high-throughput screening. The advent of highly efficient and complex machine learning architectures necessitates new methods of collating the plethora of topological, geometrical, and…
Ryan Miller, Sebastian Muraru, Ana Belén Malpartida, Josh Low + 7 more
Within the next twenty years, the number of cancer patients is expected to rise by 70%. Current cancer treatments still face several limitations, such as severe side effects and a high incidence of disease recurrence. Drug combination therapies are a promising strategy to achieve higher therapeutic effects while…
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…