24 papers · ranked by Valyu relevance
Chengcheng Sun, Jiayun Tian, Cheng Zhai, Zhixiao Wang + 4 more
Graph Neural Networks (GNNs) have emerged as a powerful paradigm in Knowledge Graphs (KGs) due to their intrinsic ability to model graph-structured data. However, there remains a lack of a systematic review about GNN-based methodologies across the entire knowledge graph technologies pipeline. To address this gap, we…
Mohamad Zamini, Hassan Reza, Minou Rabiei
Information extraction methods proved to be effective at triple extraction from structured or unstructured data. The organization of such triples in the form of (head entity, relation, tail entity) is called the construction of Knowledge Graphs (KGs). Most of the current knowledge graphs are incomplete. In order to use…
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…
Hao Tian, Xiaoxiong Zhang, Yuhan Wang, Daojian Zeng + 1 more
'Gholamreza Anbarjafari'] Knowledge graph completion is an important technology for supplementing knowledge graphs and improving data quality. However, the existing knowledge graph completion methods ignore the features of triple relations, and the introduced entity description texts are long and redundant. To address…
Peng He, Gang Zhou, Yao Yao, Zhe Wang + 1 more
Knowledge graphs (KGs) are of great importance to many artificial intelligence applications, but they usually suffer from the incomplete problem. Knowledge graph embedding (KGE), which aims to represent entities and relations in low-dimensional continuous vector spaces, has been proved to be a promising approach for KG…
Ruitong Liu, Lin, Boxu, Li + 6 more
Fusing Knowledge Graphs with Large Language Models is crucial for knowledge-intensive tasks like knowledge graph completion. The prevailing paradigm, prefix-tuning, simply concatenates knowledge embeddings with text inputs. However, this shallow fusion overlooks the rich relational semantics within KGs and imposes a…
Min Zhang, Guohua Geng, Sheng Zeng, Huaping Jia
Knowledge graph completion can make knowledge graphs more complete, which is a meaningful research topic. However, the existing methods do not make full use of entity semantic information. Another challenge is that a deep model requires large-scale manually labelled data, which greatly increases manual labour. In order…
Mohommad Esmaei Khani, Mahdieh Hasheminejad, Ali Taherkhani, Hossein Hajiabolhassan
Link prediction in knowledge graphs fundamentally depends on the quality of learned embeddings for entities and relations. However, most existing methods derive these embeddings by aggregating only the local neighborhood of each entity, neglecting the global structure of the knowledge graph. This limited view prevents…
Roger Tu, Meghamala Sinha, Carolina González, Eric Hu + 3 more
While link prediction methods in knowledge graphs have been increasingly utilized to locate potential associations between compounds and diseases, they suffer from lack of sufficient evidence to explain why a drug and a disease may be indicated. This is especially true for knowledge graph embedding (KGE) based methods…
Qingsong Li, You Lv, Xiaolong Wei, Chao Li + 4 more
Knowledge graph completion (KGC) is a fundamental task for improving downstream applications like semantic search and question answering. Effective KGC requires integrating structural and description information, allowing them to complement each other’s weaknesses (e.g., long-tail issues or overlooked structural…
Meghamala Sinha, Roger Tu, Carolina González, Andrew I. Su
This study introduces a weighted ensemble method called “WeightedKgBlend” for link prediction in knowledge graphs which combines the predictive capabilities of two types of Knowledge Graph completion methods: knowledge graph embedding and path based reasoning. By dynamically assigning weights based on individual model…
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…
Udari Madhushani Sehwag, Kassiani Papasotiriou, Jared Vann, Sumitra Ganesh
Completion Authors: ['Udari Madhushani Sehwag' 'Kassiani Papasotiriou' 'Jared Vann' 'Sumitra Ganesh'] Knowledge graphs (KGs) are crucial for representing and reasoning over structured information, supporting a wide range of applications such as information retrieval, question answering, and decision-making. However…
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…
Vignesh Prabhakar, Chau Vu, Jennifer Crawford, Joseph Waite + 1 more
Generating knowledge graph embeddings (KGEs) to represent entities (nodes) and relations (edges) in large scale knowledge graph datasets has been a challenging problem in representation learning. This is primarily because the embeddings / vector representations that are required to encode the full scope of data in a…
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…
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…
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…
Chonghuan Zhang, Adarsh Arun, Alexei Lapkin
Computer Aided Synthesis Planning (CASP) development of reaction routes requires understanding of complete reaction structures. However, most reactions in the current databases are missing reaction co-participants. Although reaction prediction and atom mapping tools can predict major reaction participants and trace…
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…
Authors not listed
As a significant task of pharmaceutical and chemical engineering, molecular retrosynthesis aims at predicting candidate reactants from predefined products. Treating this challenging task as a conditional generative modeling problem, we propose a hierarchical graph autoregression (HGAR) model and its…
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…
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…