Graphs are used in school, many occupations, and daily life, yet many people struggle to interpret them accurately. To help identify sources of difficulty in graph comprehension, we propose the Pictorial–Semantic–Task Framework. In it, we...
In recent years, pre-trained language models, represented by the bidirectional encoder representations from transformers (BERT) model, have achieved remarkable success in machine reading comprehension (MRC). However, limited by the structure of BERT-...
Multi-hop machine reading comprehension is a challenging task in natural language processing as it requires more reasoning ability across multiple documents. Spectral models based on graph convolutional networks have shown good inferring abilities an...
In recent years, with the advancement of natural language processing techniques and the release of models like ChatGPT, how language models understand questions has become a hot topic. In handling complex logical reasoning with pre-trained models, it...
6 October 2015
Graphs are commonly believed to facilitate users’ comprehension. We explored the effect of graphs on comprehension compared to text, manipulating content complexity (single bar vs. double bar graphs) and question type (point-locating vs. comparison q...
An experimental investigation into interaction between language and information graphics in multimodal documents served as the basis for this study. More specifically, our purpose was to investigate the role of linguistic annotations in graph-text do...
Research on graph comprehension suggests that point differences are easier to read in bar graphs, while trends are easier to read in line graphs. But are graph readers able to detect and use the most suited graph type for a given task? In this study,...
Graphs are essential tools in introductory STEM courses, yet students often read them as pictures rather than quantitative representations of changing quantities. This study examined how 18 first-semester general chemistry students interpreted two st...
Chinese open-domain reading comprehension question answering is a task in the field of natural language processing. Traditional neural network-based methods lack interpretability in answer reasoning when addressing open-domain reading comprehension q...
Graph kernels are of vital importance in the field of graph comparison and classification. However, how to compare and evaluate graph kernels and how to choose an optimal kernel for a practical classification problem remain open problems. In this pap...
13 July 2021
Agricultural activities are highly related to the reduction of the availability of water resources due to the consumption of freshwater for crop irrigation, the use of fertilizers and pesticides. In this study, the water quality of the Adolfo López M...
The scientific and reasonable division of geographical units of cultivated land reserve resource areas is an important foundation for comprehensive land consolidation (CLC). Saline–alkali land is an important cultivated land reserve resource, a...
Graph theory (GT) concepts are potentially applicable in the field of computer science (CS) for many purposes. The unique applications of GT in the CS field such as clustering of web documents, cryptography, and analyzing an algorithm’s executi...
Sudden tailings dam breaches trigger large-scale heavy metal compound pollution in coupled surface water–groundwater systems, requiring systematic full-cycle ecological damage quantification tools applicable to diverse contamination types. This...
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...
Automatic key concept extraction from text is the main challenging task in information extraction, information retrieval and digital libraries, ontology learning, and text analysis. The statistical frequency and topical graph-based ranking are the tw...
In this work, we construct a homotopy theory for a class of intersection graphs arising from topological monoids. We introduce the M-intersection graph of a
Traffic flow prediction is a key component of Intelligent Transportation Systems (ITS), crucial for alleviating urban congestion, optimizing traffic management, and improving the overall efficiency of road networks. With the rapid growth in vehicle n...
Molecular odor prediction represents a fundamental challenge in computational chemistry with significant applications in fragrance design, food science, and chemical safety assessment. While traditional Quantitative Structure–Activity Relations...
In today’s world, where information keeps growing rapidly and changing constantly, language models play a crucial role in making our lives easier across different fields. However, it is tough to keep these models updated with all the new data w...
The research on the optimization and transformation of rural “production-living-ecological” functions (PLEFs) is of great significance for rural revitalization and sustainable development. Existing studies predominantly evaluate rural PLE...
Deep learning models contributed to reaching unprecedented results in prediction and classification tasks of Artificial Intelligence (AI) systems. However, alongside this notable progress, they do not provide human-understandable insights on how a sp...
This paper introduces the concept of proper 2-dominating sets in graphs, providing a comprehensive characterisation of graphs that possess such sets. We give the necessary and sufficient conditions for a graph to have a proper 2-dominating set. Graph...
Accurate traffic prediction is crucial to the construction of intelligent transportation systems. This task remains challenging because of the complicated and dynamic spatiotemporal dependency in traffic networks. While various graph-based spatiotemp...
This paper introduces a prospective study of the potential of spatio-temporal graphs (ST-graphs) and knowledge graphs (K-graphs) for the modelling of geographical phenomena. While the integration of time within GIS has long been a domain of major int...
Graph anomaly detection aims at identifying rare, unusual entities in attributed networks with respect to their patterns or structures that deviate significantly from the majority within a graph. Over the years, extensive efforts in this field have b...
Graph machine-learning (ML) methods have recently attracted great attention and have made significant progress in graph applications. To date, most graph ML approaches have been evaluated on social networks, but they have not been comprehensively rev...
The geographic knowledge graph offers a structured framework for mining and discovering spatiotemporal knowledge, which is of great significance for understanding geographic dynamics. However, existing geographic knowledge graphs still encounter sign...
Rotating machinery serves as a critical component in various engineering systems, making accurate prediction of its Remaining Useful Life (RUL) essential for ensuring operational stability. To address the technical limitations of mainstream RUL predi...
This paper presents a comprehensive survey of knowledge graphs in education. It covers the patterns and prospects of research in this area. A total of 48 relevant publications between 2011 and 2023 were collected from the Web of Science, Scopus, and...
Real-world problems often exhibit complex relationships and dependencies, which can be effectively captured by graph learning systems. Graph attention networks (GATs) have emerged as a powerful and versatile framework in this direction, inspiring num...
Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as in information retrieval, natural language processing, recommendation systems, etc. However, the open nature of KGs often implies that they are incomplete,...
Haros graphs are a graph-theoretical representation of real numbers in the unit interval. The degree distribution of the Haros graphs provides information regarding the topological structure and the associated real number. This article provides a com...
Situational Graphs (S-Graphs) merge geometric models of the environment generated by Simultaneous Localization and Mapping (SLAM) approaches with 3D scene graphs into a multi-layered jointly optimizable factor graph. As an advantage, S-Graphs not onl...
Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks u...
Air traffic management (ATM) relies on the running condition of the air traffic control sector (ATCS), and assessing whether it is overloaded is crucial for efficiency and safety for the entire aviation industry. Previous approaches to evaluating air...
Simple questions are the most common type of questions used for evaluating a knowledge graph question answering (KGQA). A simple question is a question whose answer can be captured by a factoid statement with one relation or predicate. Knowledge grap...
Graph neural networks (GNNs) are effective for structured data analysis but face reduced learning accuracy due to noisy connections and the necessity for explicit graph structures and labels. This requirement constrains their usability in diverse gra...
Graphs are data structures that effectively represent relational data in the real world. Graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc. Graph...
Graphs are powerful tools to model manufacturing systems and scheduling problems. The complexity of these systems and their scheduling problems has been substantially increased by the ongoing technological development. Thus, it is essential to genera...
The rapid expansion and interdisciplinary nature of Physical Internet (PI) research have resulted in fragmented knowledge, limiting the ability of stakeholders to identify emerging trends, actionable insights and genuine research gaps. This study int...
Graph neural networks (GNNs) have demonstrated significant potential in the field of computer vision in recent years, particularly in handling non-Euclidean data and capturing complex spatial and semantic relationships. This paper provides a comprehe...
In computer science and graph theory, prism and antiprism graphs are crucial for network modeling, optimization, and network connectivity comprehension. Applications such as social network analysis, fault-tolerant circuit design, and parallel and dis...
Spreading digitalization, flexibility, and autonomy of technological processes in cyber-physical systems entails high security risks corresponding to negative consequences of the destructive actions of adversaries. The paper proposes a comprehensive...
The jsGraph library is a versatile javascript library that allows advanced charting to be rendered interactively in web browsers without relying on server-side image processing. jsGraph is released under the MIT license and is free of charge. While b...
This paper presents a comprehensive study of the linguistic Z-graph, which is a novel framework designed to analyze linguistic structures within social networks. By integrating concepts from graph theory and linguistics, the linguistic Z-graph provid...
This paper presents a comprehensive review of the literature on the original concept of metric location, along with its various adaptations and extensions that have been developed over time. Given that determining a minimum location set is generally...
Over time, the renowned Kyoto Encyclopedia of Genes and Genomes (KEGG) has grown to become one of the most comprehensive online databases for biological procedures. The majority of the data are stored in the form of pathways, which are graphs that de...
As an essential part of artificial intelligence, a knowledge graph describes the real-world entities, concepts and their various semantic relationships in a structured way and has been gradually popularized in a variety practical scenarios. The major...
Training Graph Neural Networks (GNNs) on large-scale graphs in the deep learning era can be expensive. While graph condensation has recently emerged as a promising approach through which to reduce training cost by compressing large graphs into smalle...
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