Knowledge graph completion (KGC) is commonly studied under static settings, whereas real-world knowledge graphs evolve continuously with newly emerging entities, relations, and facts. In continual knowledge graph completion (CKGC), the model is requi...
A variety of cast product defects may occur in the continuous casting process. By establishing a Continuous Casting Quality Knowledge Graph (C2Q-KG) focusing on the causes of cast product defects, enterprises can systematically sort out and express t...
Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the...
The representation learning of the knowledge graph projects the entities and relationships in the triples into a low-dimensional continuous vector space. Early representation learning mostly focused on the information contained in the triplet itself...
With the development of technology, Earth Science has entered a new era. Continuous research has generated a large amount of Earth Science data, including a significant amount of semi-structured and unstructured data, which contain information about...
Knowledge graph (KG) representation learning aims to encode entities and relations into dense continuous vector spaces such that knowledge contained in a dataset could be consistently represented. Dense embeddings trained from KG datasets benefit a v...
Knowledge graphs (KGs) play a crucial role in many applications, such as question answering, but incompleteness is an urgent issue for their broad application. Much research in knowledge graph completion (KGC) has been performed to resolve this issue...
In knowledge graph representation learning, link prediction is among the most popular and influential tasks. Its surge in popularity has resulted in a panoply of orthogonal embedding-based methods projecting entities and relations into low-dimensiona...
Knowledge representation learning achieves the automatic completion of knowledge graphs (KGs) by embedding entities into continuous low-dimensional vector space. In knowledge graph completion (KGC) tasks, the inter-dependencies and hierarchical infor...
With the rapid advancement of educational digitalization and generative artificial intelligence, knowledge graphs and AI agents have emerged as key technologies supporting smart higher education. To address challenges in current AI-assisted teaching,...
Geographical knowledge graphs (GeoKGs) have long experienced several fundamental challenges in representing complex spatial relationships, such as limited dimensionality, insufficient quantification of relationship strength, and weak reasoning capabi...
Problems such as unreasonable processability or model defects generated in the design stage will lead to continuous rework during the manufacturing process, which greatly increases the manufacturing cost of the product. Through manufacturability anal...
Embedding learning on knowledge graphs (KGs) aims to encode all entities and relationships into a continuous vector space, which provides an effective and flexible method to implement downstream knowledge-driven artificial intelligence (AI) and natur...
Intelligent monitoring in grain-reserve warehouses increasingly combines fixed sensors, UAVs, quadruped robots, human review, and site information systems, yet the resulting operational records are typically stored separately. This fragmentation make...
A Geographic Knowledge Graph (GeoKG) links geographic relation triplets into a large-scale semantic network utilizing the semantic of geo-entities and geo-relations. Unfortunately, the sparsity of geo-related information distribution on the web leads...
Background/Objectives: Alzheimer’s disease (AD) drug development continues to suffer from high late-stage attrition and narrow mechanistic diversity, which makes the systematic repurposing of approved compounds an urgent priority. Existing comp...
The increasing heterogeneity of Internet of Things (IoT) devices continues to challenge semantic interoperability across heterogeneous platforms and application domains. Although numerous IoT ontologies and standards have been proposed, many emphasiz...
A special class of complex adaptive systems—biological and social—thrive not by passively accumulating patterns, but by engineering coherence, i.e., the deliberate alignment of prior knowledge, real-time updates, and teleonomic purposes....
Hybrid service function chains (SFCs) composed of containerized network functions (CNFs) and virtual machine-based network functions (VMNFs) underpin latency-critical services in edge computing environments, yet the order-of-magnitude difference in c...