27 papers · ranked by Valyu relevance
Satvik Garg, Dwaipayan Roy
In recent years, Knowledge Graph (KG) development has attracted significant researches considering the applications in web search, relation prediction, natural language processing, information retrieval, question answering to name a few. However, often KGs are incomplete due to which Knowledge Graph Completion (KGC)…
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…
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…
Katrin Hänsel, Sarah N. Dudgeon, Kei-Hoi Cheung, Thomas J. S. Durant + 1 more
'Wade L. Schulz'] Graph data models are an emerging approach to structure clinical and biomedical information. These models offer intriguing opportunities for novel approaches in healthcare, such as disease phenotyping, risk prediction, and personalized precision care. The combination of data and information in a graph…
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…
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…
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…
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…
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…
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…
Napoli, Rosario, Morabito, Gabriele + 6 more
—The rise of graph-structured data has driven major advances in Graph Machine Learning (GML), where graph embeddings (GEs) map features from Knowledge Graphs (KGs) into vector spaces, enabling tasks like node classification and link prediction. However, since GEs are derived from explicit topology and features, they…
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…
Shilpa Verma, Rajesh Bhatia, Sandeep Harit, Sanjay Batish
The necessity for scholarly knowledge mining and management has grown significantly as academic literature and its linkages to authors produce enormously. Information extraction, ontology matching, and accessing academic components with relations have become more critical than ever. Therefore, with the advancement of…
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…
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.…
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…
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…
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…
Jeffrey Sardina, Luca Costabello, Guéret Christophe
Graph Learning Authors: ['Jeffrey Sardina' 'Luca Costabello' 'Guéret Christophe'] Abstract—Knowledge Graphs (KGs) have become increasingly common for representing large-scale linked data. However, their immense size has required graph learning systems to assist humans in analysis, interpretation, and pattern detection.…
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…