In recent years, with the rapid development of Internet technology and applications, the scale of Internet data has exploded, which contains a significant amount of valuable knowledge. The best methods for the organization, expression, calculation, a...
Knowledge graph (KG) reasoning improves the perception ability of graph structure features, improving model accuracy and enhancing model learning and reasoning capabilities. This paper proposes a new GraphDIVA model based on the variational reasoning...
Islands with both land and sea characteristics provide the foundation for protecting the marine environment, preserving the ecological balance of the ocean, and fostering sustainable economic and social growth. Advanced monitoring technologies have b...
Knowledge graphs’ reasoning is of great significance for the further development of artificial intelligence and information retrieval, especially for reasoning over temporal knowledge graphs. The rotation-based method has been shown to be effec...
In recent years, the emergence of large-scale language models, such as ChatGPT, has presented significant challenges to research on knowledge graphs and knowledge-based reasoning. As a result, the direction of research on knowledge reasoning has shif...
The Geographic Knowledge Graph (GeoKG) serves as an effective method for organizing geographic knowledge, playing a crucial role in facilitating semantic interoperability across heterogeneous data sources. However, existing GeoKGs are limited by a la...
Knowledge graph reasoning can deduce new facts and relationships, which is an important research direction of knowledge graphs. Most of the existing methods are based on end-to-end reasoning which cannot effectively use the knowledge graph, so conseq...
Knowledge acquisition and reasoning are essential in intelligent welding decisions. However, the challenges of unstructured knowledge acquisition and weak knowledge linkage across phases limit the development of welding intelligence, especially in th...
The integration of multi-source transportation data is complex and insufficient in most of the big cities, which made it difficult for researchers to conduct in-depth data mining to improve the policy or the management. In order to solve this problem...
To address the heavy reliance on experiential knowledge, fragmented multi-source information, and limited intelligence in decision-making for aircraft skin spraying processes, this paper proposes a knowledge reasoning method based on a knowledge grap...
The vitality of areas around tourist attractions plays a crucial role in promoting the sustainable development of both tourism and the regional economy. However, there is a lack of comprehensive studies on the methods for mining vitality around attra...
Temporal knowledge graphs (TKGs) incorporate temporal information into traditional triplets, enhancing the dynamic representation of real-world events. Temporal knowledge graph reasoning aims to infer unknown quadruples at future timestamps through d...
Human beings have the ability to quickly recognize novel concepts with the help of scene semantics. This kind of ability is meaningful and full of challenge for the field of machine learning. At present, object recognition methods based on deep learn...
To address the limitations in capturing complex semantic features between entities and the incomplete acquisition of entity and relationship information by existing patent knowledge graph reasoning algorithms, we propose a reasoning method that integ...
Visual commonsense reasoning is an intelligent task performed to decide the most appropriate answer to a question while providing the rationale or reason for the answer when an image, a natural language question, and candidate responses are given. Fo...
With its powerful expressive capability and intuitive presentation, the knowledge graph has emerged as one of the primary forms of knowledge representation and management. However, the presence of biases in our cognitive and construction processes of...
The judicious configuration of predicates is a crucial but often overlooked aspect in the field of knowledge graphs. While previous research has primarily focused on the precision of triples in assessing knowledge graph quality, the rationality of pr...
The knowledge graph link prediction task currently faces challenges such as insufficient semantic fusion of structured knowledge and unstructured text, limited representation learning of long-tailed entities, and insufficient interpretability of the...
Knowledge graphs are extensively utilized in diverse fields such as search engines, recommendation systems, and dialogue systems, and knowledge graph reasoning plays an important role in the aforementioned domains. Graph neural networks demonstrate t...
We explored the mediating role of prior knowledge on the relation between intelligence and learning proportional reasoning. What students gain from formal instruction may depend on their intelligence, as well as on prior encounters with proportional...
Table-based question answering (TableQA) has emerged as an important task in natural language processing, yet existing models face challenges in handling complex reasoning and mitigating hallucinations, especially when dealing with diverse table stru...
Artificial intelligence is set to transform the next generation of intelligent buildings through the application of information and semantic data models and machine learning algorithms. Semantic data models enable the understanding of real-world data...
Complex fact verification (FV) requires fusing scattered sequences and performing multi-hop reasoning over these composed sequences. Recently, by employing some FV models, knowledge is obtained from context to support the reasoning process based on p...
Nowadays, there are different kinds of public knowledge bases for cyber security vulnerability and threat intelligence which can be used for IoT security threat analysis. However, the heterogeneity of these knowledge bases and the complexity of the I...
Representing domain knowledge extracted from unstructured texts using knowledge graphs supports knowledge reasoning, enabling the extraction of accurate factual information and the generation of interpretable results. However, reasoning with knowledg...
In the field of question answering (QA), the methods of large language models (LLMs) cannot learn vertical domain knowledge during the pre-training stage, leading to low accuracy in domain QA. Conversely, knowledge base question answering (KBQA) can...
For the task of answering complex logical queries on large-scale incomplete knowledge graphs, the promising approach is to embed the knowledge graph and complex logical queries into a low-dimensional space and perform iterative reasoning to find the...
The Retrieval-Augmented Generation (RAG) framework enhances Large Language Models (LLMs) by retrieving relevant knowledge to broaden their knowledge boundaries and mitigate factual hallucinations stemming from knowledge gaps. However, the RAG Framewo...
Financial report question answering (FRQA) presents unique challenges due to the need for precise numerical reasoning, complex table structures, and multi-table associations. Existing approaches often overlook the domain-specific complexities of fina...
Predicting future events is crucial for temporal reasoning, providing valuable insights for decision-making across diverse domains. However, the intricate global interactions and temporal–causal relationships at the document level event present...
Reasoning over Temporal Knowledge Graphs (TKGs) aims to forecast future events based on historical ones. Existing approaches typically enforce strict temporal order constraints among past events; however, such rigidity limits the effective exploitati...
To address the difficulty in detecting workers’ violation behaviors in electric power construction scenarios, this paper proposes an innovative method that integrates knowledge reasoning and progressive multi-level distillation techniques. Firs...
Understanding and knowledge of scientific reasoning skills is a key ability of pre-service teachers. In a written survey (open response format), biology and chemistry pre-service teachers (n = 51) from two German universities claimed central decision...
Vision-and-Language Navigation (VLN) is a task designed to enable embodied agents carry out natural language instructions in realistic environments. Most VLN tasks, however, are guided by an elaborate set of instructions that is depicted step-by-step...
Path reasoning in knowledge graphs is a pivotal task for uncovering complex relational patterns and facilitating advanced inference processes. It also holds significant potential in domains such as power electronics, where real-time reasoning over dy...
A dynamic uncertain causality graph (DUCG) is a probabilistic graphical model for knowledge representation and reasoning, which has been widely used in many areas, such as probabilistic safety assessment, medical diagnosis, and fault diagnosis. Howev...
The application of high-bandwidth networks and cloud computing in manufacturing systems will be followed by mass data. Industrial data analysis plays important roles in condition monitoring, performance optimization, flexibility, and transparency of...
Non-axiomatic logic (NAL) is a term-based, non-monotonic, multi-valued logic with evidence-based formal semantics. All those characteristics position NAL as an excellent candidate for modeling natural language expressions and supporting artificial ag...
Due to the access of distributed energy and a new flexible load, the electrical characteristics of a new distribution network are significantly different from those of a traditional distribution network, which poses a new challenge to the original to...
Knowledge Graph Reasoning (KGR) aims to deduce missing or novel knowledge by learning structured information and semantic relationships within Knowledge Graphs (KGs). Despite significant advances achieved by deep neural networks in recent years, exis...
Multi-relational graph neural networks (GNNs) have found widespread application in tasks involving enhancing knowledge representation and knowledge graph (KG) reasoning. However, existing multi-relational GNNs still face limitations in modeling the e...
This paper proposes an ancillary decision-making method for hydropower station failure handling based on knowledge graph and case-based reasoning. The proposed method assists the power station dispatcher to carry out accurate and timely failure handl...
Temporal knowledge graphs (TKGs) effectively represent dynamic facts by incorporating a temporal dimension, yet they frequently encounter data incompleteness issues that constrain downstream applications. Concurrently, TKG prediction tasks, which ena...
Knowledge graph (KG) reasoning aims to obtain new knowledge based on existing data. Utilizing large language models (LLMs) through in-context learning for KG reasoning has become a significant direction. However, existing methods mainly extract in-co...
This study was conducted to address the demand for interpretable intelligent recognition of fruit tree diseases in smart horticultural environments. A KAD-Former framework integrating an agricultural knowledge graph with a visual Transformer was prop...
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...
Scene Knowledge-guided Visual Grounding (SK-VG) is a multi-modal detection task built upon conventional visual grounding (VG) for human–computer interaction scenarios. It utilizes an additional passage of scene knowledge apart from the image an...
In this study, a new risk assessment model is developed and the evidence reasoning (ER) approach is applied to assess failure risk of knowledge-intensive services (KIS) corporates in the UK. General quantitative financial indicators alone (e.g., oper...
Taking methanol production as an example, the concept of “ontology” is introduced to construct a safety knowledge ontology, and a safety information knowledge base is created with the help of the Protégé software. These can...
The use of advanced adaptive algorithms in Augmented Reality (AR) systems works to advance spatial skills with valuable relevance in many professional spheres by providing personalized feedback in an immersive environment. This study combines Bayesia...
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