27 papers · ranked by Valyu relevance
Jiajun Tong, Zhixiao Wang, Xiaobin Rui
The open domain knowledge base is very important. It is usually extracted from encyclopedia websites and is widely used in knowledge retrieval systems, question answering systems, or recommendation systems. In practice, the key challenge is to maintain an up-to-date knowledge base. Different from Unwieldy fetching all…
Lianhong Ding, Mengxiao Li, Shengchang Gao, Juntao Li + 3 more
'Jianye Yu' 'Giovanni Angiulli'] Relational direction plays an important role in multi-relational knowledge graphs (KGs). Current knowledge graph completion (KGC) methods suffer from insufficient utilization of relation correlation information. To address this issue, this article proposes a novel KGC framework, namely…
Trinh Trung Duong Nguyen, Thuy Nguyen-Phuong, Quy-Hoai Nguyen, Amna Mumtaz Abbasi + 8 more
Biomedical knowledge evolves rapidly, yet most disease-centered knowledge graphs remain unchanged after publication. We present PrimeKG-Plus, an extension of PrimeKG that updates all 20 original data resources to releases available as of Dec 2025 and incorporates additional resources, including OpenTargets…
Savaş Takan, Claudio Ardagna
A knowledge graph is convenient for storing knowledge in artificial intelligence applications. On the other hand, it has some shortcomings that need to be improved. These shortcomings can be summarised as the inability to automatically update all the knowledge affecting a piece of knowledge when it changes, ambiguity…
Lingwen Deng, Yifei Han, Shijie Li, Yue Du + 1 more
Parameter-Preserving Knowledge Editing (PPKE) enables updating models with new or corrected information without retraining or parameter adjustment. Recent PPKE approaches based on knowledge graphs (KG) to extend knowledge editing (KE) capabilities to multi-hop question answering (MHQA). However, these methods often…
Keunwoo Peter Yu
In natural language processing, interactive text-based games serve as a test bed for interactive AI systems. Prior work has proposed to play text-based games by acting based on discrete knowledge graphs constructed by the Discrete Graph Updater (DGU) to represent the game state from the natural language description.…
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…
Yifei Li, Lingling Zhang, Hang Yan, Tianzhe Zhao + 3 more
Traditional knowledge graph (KG) embedding methods aim to represent entities and relations in a low-dimensional space, primarily focusing on static graphs. However, real-world KGs are dynamically evolving with the constant addition of entities, relations and facts. To address such dynamic nature of KGs, several…
Chunjuan Li, Hong Zheng, Gang Liu, Md Mehedi Hassan
Federated learning ensures that data can be trained globally across clients without leaving the local environment, making it suitable for fields involving privacy data such as healthcare and finance. The knowledge graph technology provides a way to express the knowledge of the Internet into a form more similar to the…
Shuangmei Wang, Fengjie Sun, Muhammad Nasir Khan
The role of knowledge graph encompasses the representation, organization, retrieval, reasoning, and application of knowledge, providing a rich and robust cognitive foundation for artificial intelligence systems and applications. When we learn new things, find out that some old information was wrong, see changes and…
Yanan Zhang, Li Jin, Xiaoyu Li, Honqi Wang
Multi-hop path reasoning over knowledge base aims at finding answer entities for an input question by walking along a path of triples from graph structure data, which is a crucial branch in the knowledge base question answering (KBQA) research field. Previous studies rely on deep neural networks to simulate the way…
Tong Zhou, Yubo Chen, Kang Liu, Jun Zhao
Knowledge Graph Authors: ['Tong Zhou' 'Yubo Chen' 'Kang Liu' 'Jun Zhao'] Large language models have become integral to question-answering applications despite their propensity for generating hallucinations and factually inaccurate content. Querying knowledge graphs to reduce hallucinations in LLM meets the challenge of…
Walid S. Saba
Knowledge graphs (KGs) have become the standard technology for the representation of factual information in applications such as recommendation engines, search, and question-answering systems. However, the continual updating of KGs, as well as the integration of KGs from different domains and KGs in different…
Mehwish Alam, Genet Asefa Gesese, Pierre-Henri Paris
Knowledge graphs (KGs) have recently been used for many tools and applications, making them rich resources in structured format. However, in the real world, KGs grow due to the additions of new knowledge in the form of entities and relations, making these KGs dynamic. This chapter formally defines several types of…
Payal Chandak, Kexin Huang, Marinka Zitnik
Developing personalized diagnostic strategies and targeted treatments requires a deep understanding of disease biology and the ability to dissect the relationship between molecular and genetic factors and their phenotypic consequences. However, such knowledge is fragmented across publications, non-standardized research…
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…
Yuanhao Huang, Zhaowei Han, Xin Luo, Xuteng Luo + 20 more
Knowledge graphs have recently emerged as a powerful data structure to organize biomedical knowledge with explicit representation of nodes and edges. The knowledge representation is in a machine-learning ready format and supports explainable AI models. However, PubMed, the largest and richest biomedical 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…
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…
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
Sharing knowledge on chemicals in the digital age has been the playground of databases such as the Chemical Abstract Services and PubChem. Wikipedia complements this field by providing context to chemicals aimed at a broad audience, but is not easily read by machines. Wikidata was started as a database service to…
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…
Authors not listed
The nanosafety domain has seen significant advancements in data generation and sharing, yet challenges remain in ensuring data interoperability and reuse. This article focuses on developing a semantic interoperability framework for nanosafety data to maximize the FAIRness (Findability, Accessibility, Interoperability…
Venkata Chandrasekhar Nainala, Kohulan Rajan, Sri Ram Sagar Kanakam, Nisha Sharma + 3 more
The COCONUT (COlleCtion of Open Natural prodUcTs) database was launched in 2021 as an aggregation of openly available natural product datasets and has been one of the biggest open natural product databases since. Apart from the chemical structures of natural products, COCONUT contains information about names and…
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…
Zachary Humphreys, Xenophon Evangelopoulos, Stavros Gerolymatos, Edward O. Pyzer-Knapp + 1 more
Graph neural networks have recently met huge success in various inference tasks including materials property prediction amongst many others. Nevertheless, having an inherently locally-based representation capacity as they do, global representation of materials' structures can only only be achieved by expanding the…
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…