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- research-articleNovember 2024
Heterogeneous Views and Spatial Structure Enhancement for triple error detection
Expert Systems with Applications: An International Journal (EXWA), Volume 256, Issue Chttps://doi.org/10.1016/j.eswa.2024.124938AbstractKnowledge graph error detection is to identify erroneous triples in knowledge graphs that are inconsistent with objective facts in the real world. In practice, the quality of knowledge graphs is an indispensable foundation for the widespread and ...
Highlights- Heterogeneous views with spatial structure enhancement are proposed.
- Positive and negative triple views on head/tail entity co-occurrence are constructed.
- Graph-Spatial-Transformer encoder is designed to capture spatial global ...
- research-articleNovember 2024
KGroot: A knowledge graph-enhanced method for root cause analysis
Expert Systems with Applications: An International Journal (EXWA), Volume 255, Issue PChttps://doi.org/10.1016/j.eswa.2024.124679AbstractFault localization in online microservices is a challenging task due to the vast amount of monitoring data, diversity of types and events, and complex interdependencies among services and components. Fault events in services are propagative and ...
- research-articleNovember 2024
Billion-scale pre-trained knowledge graph model for conversational chatbot
AbstractConversational Chatbot is a critical technology for managing customer service on E-commerce platforms. However, most Chatbots struggle with data scarcity and sparsity. To tackle these issues, a novel two-step (pretrain and fine-tune) click-...
- ArticleNovember 2024
GSEA: Global Structure-Aware Graph Neural Networks for Entity Alignment
Natural Language Processing and Chinese ComputingPages 187–199https://doi.org/10.1007/978-981-97-9434-8_15AbstractEntity alignment (EA) aims to identify the same real-world entities presented in different knowledge graphs (KGs), which is the most crucial step in integrating multi-source KGs. Existing entity alignment methods based on Graph Neural Networks ...
- research-articleOctober 2024
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- ArticleOctober 2024
GRASPER: Leveraging Knowledge Graphs for Predictive Supply Chain Analytics
AbstractSupply chain disruptions in manufacturing are increasingly prevalent due to the complexity and lack of transparency beyond direct suppliers. Early-stage disruptions often remain undetected, propagate in the network and pose significant challenges ...
- research-articleOctober 2024
- ArticleNovember 2024
MRGN: Multiscale Relation-Gated Graph Network for Entity Alignment
AbstractEntity alignment, which aims to identify equivalent entities from various Knowledge Graphs (KGs), is a fundamental and critical task in knowledge graph fusion. Current entity alignment methods usually use relationship triples to represent the ...
- research-articleOctober 2024
Prediction and optimization of pure electric vehicle tire/road structure-borne noise based on knowledge graph and multi-task ResNet
Expert Systems with Applications: An International Journal (EXWA), Volume 255, Issue PBhttps://doi.org/10.1016/j.eswa.2024.124536Highlights- A bidirectional knowledge graph of the vehicle tire/road structure-borne noise is developed.
- A Task-Conditional Gate Control (TCGC) approach is proposed for multi-task learning.
- A novel method that combines knowledge graph and ...
Pure electric vehicles (PEVs) offer a significant advantage in their lower interior noise levels compared to traditional combustion engines, making them increasingly popular among consumers. However, the absence of engine noise has brought ...
- research-articleNovember 2024
Meta-learning on dynamic node clustering knowledge graph for cold-start recommendation
AbstractMeta-learning has been introduced in the recommendation domain, and a possible direction to extend graph-based meta-learning is how to exploit higher-order information between nodes. The current studies primarily rely on pre-embedding methods to ...
- research-articleNovember 2024
Improving semantic similarity computation via subgraph feature fusion based on semantic awareness
Engineering Applications of Artificial Intelligence (EAAI), Volume 136, Issue PBhttps://doi.org/10.1016/j.engappai.2024.108947AbstractSemantic similarity is a critical aspect of natural language processing, as it evaluates the degree of similarity within a knowledge graph. Various computational methods, including distance-based and feature-based approaches, have been proposed ...
- research-articleNovember 2024
LLM-TIKG: Threat intelligence knowledge graph construction utilizing large language model
AbstractOpen-source threat intelligence is often unstructured and cannot be directly applied to the next detection and defense. By constructing a knowledge graph through open-source threat intelligence, we can better apply this information to intrusion ...
- research-articleNovember 2024
Causal knowledge extraction from long text maintenance documents
AbstractLarge numbers of maintenance Work Request Notification (WRN) records are created by industry as part of standard business work flows. These digital records hold invaluable insights crucial to best practice in asset management. Of particular ...
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Highlights- Free-text maintenance work request documents hold valuable causal information on asset failures.
- Work requests are multi-sentence and contain information extraneous to causal analysis so a novel sentence-level noise removal method is ...
- research-articleOctober 2024
CL-AP2: A composite learning approach to attack prediction via attack portraying
Journal of Network and Computer Applications (JNCA), Volume 230, Issue Chttps://doi.org/10.1016/j.jnca.2024.103963AbstractThe capabilities of accurate prediction of cyberattacks have long been desired as detection methods cannot avoid the damages caused by occurrences of cyberattack. Attack prediction still remains an open issue especially to specify the upcoming ...
- research-articleSeptember 2024
Triple confidence measurement in knowledge graph with multiple heterogeneous evidences
AbstractKnowledge graph (KG) is a representative technique of knowledge engineering, and it is often used in various intelligence applications, which assume that all triples in knowledge graphs (KGs) are correct. However, due to the noise brought by ...
- research-articleNovember 2024
Enhancing text-based knowledge graph completion with zero-shot large language models: A focus on semantic enhancement
AbstractThe design and development of text-based knowledge graph completion (KGC) methods leveraging textual entity descriptions are at the forefront of research. These methods involve advanced optimization techniques such as soft prompts and contrastive ...
- research-articleOctober 2024
Multi-aspect Knowledge-enhanced Hypergraph Attention Network for Conversational Recommendation Systems
AbstractConversational recommendation systems (CRS) aim to proactively elicit user preferences through multi-turn conversations for item recommendations. However, most existing works focus solely on user’s current conversation information, which fails to ...
- research-articleOctober 2024
An attention mechanism and residual network based knowledge graph-enhanced recommender system
AbstractRecommender systems enhanced by a knowledge graph (KG) have attained widespread popularity and attention in recent years. However, traditional KG-based recommender systems encounter the challenge of gradient explosion as the network depth ...
- research-articleOctober 2024
MDGRL: Multi-dimensional graph rule learning
Engineering Applications of Artificial Intelligence (EAAI), Volume 135, Issue Chttps://doi.org/10.1016/j.engappai.2024.108818AbstractKnowledge graph completion is an advanced artificial intelligence (AI) methodology that enables the systematic organization and structuring of data. It can significantly enhance the digital economy by facilitating more accurate and appropriate ...
- research-articleOctober 2024
Joint relational triple extraction with enhanced representation and binary tagging framework in cybersecurity
AbstractThe cyber threat intelligence (CTI) knowledge graph is a valuable tool for aiding security practitioners in the identification and analysis of cyberattacks. These graphs are constructed from CTI data, organized into relational triples, where each ...
Highlights- Proposed a novel model for relation extraction in cybersecurity.
- Developed methods for attack identification and profiling using massive cyber threat intelligence.
- Combined relation embeddings with word embeddings to capture ...