27 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…
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…
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…
Muhammad Haris, Sören Auer, Markus Stocker
Digital research artifacts (articles, datasets, software, etc.) are the basis for scientific research. Due to the continuous growth and complexity (e.g., due to formats) of such artifacts, ensuring their organization and long-term availability for research is becoming increasingly challenging. It is essential to manage…
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…
Saber Soleymani, Nathan Gravel, Krzysztof Kochut, Natarajan Kannan
The integration of large language models (LLMs) with knowledge graphs (KGs) holds significant potential for simplifying the process of querying graph databases, especially for non-technical users. KGs provide a structured representation of domain-specific data, enabling rich and precise information retrieval. However…
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…
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…
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.…
Phillip Schneider, Tim Schopf, Juraj Vladika, Florian Matthes
Processing Authors: ['Phillip Schneider' 'Tim Schopf' 'Juraj Vladika' 'Florian Matthes'] Knowledge management is a critical challenge for enterprises in today's digital world, as the volume and complexity of data being generated and collected continue to grow incessantly. Knowledge graphs (KG) emerged as a promising…
Ying Liu, Peng Wang, Di Yang, Ningjia Qiu + 1 more
The purpose of knowledge embedding is to extract entities and relations from the knowledge graph into low-dimensional dense vectors, in order to be applied to downstream tasks, such as connection prediction and intelligent classification. Existing knowledge embedding methods still have many limitations, such as the…
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…
Thomas Gebhart, Jakob Hansen, Paul Schrater
Knowledge graph embedding involves learning representations of entities—the vertices of the graph—and relations—the edges of the graph such that the resulting representations encode the known factual information represented by the knowledge graph and can be used in the inference of new relations. We show that knowledge…
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…
Michael Statt, Brian Rohr, Dan Guevarra, Ja'Nya Breeden + 2 more
Materials knowledge is inherently hierarchical. While high-level descriptors such as composition and structure are valuable for contextualizing materials data, the data must ultimately be considered in the context of its low-level acquisition details. Graph databases offer an opportunity to represent hierarchical…
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…
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…
Arnaud Gaudry, Marco Pagni, Florence Mehl, Sébastien Moretti + 10 more
Modern natural products (NPs) research relies on untargeted liquid chromatography coupled with mass spectrometry metabolomics. Together with cutting-edge processing and computational annotation strategies, such approaches can yield extensive spectral and structural information. However, current processing workflows…
Authors not listed
Identifying synthesis routes from knowledge graphs poses challenges beyond retrosynthesis, including path–finding artifacts and data issues. We introduce “SynGPS”, a novel algorithm that overcomes these limitations by identifying viable routes even with common artifacts. SynGPS can resolve nonsensical cycles…
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…
Authors not listed
Today, machine learning models are employed extensively to predict the physicochemical and biological properties of molecules. Their performance is typically evaluated on in-distribution (ID) data, i.e., data originating from the same distribution as the training data. However, the real-world applications of such…