Human beings tend to incrementally learn from the rapidly changing environment without comprising or forgetting the already learned representations. Although deep learning also has the potential to mimic such human behaviors to some extent, it suffer...
Relations in knowledge graphs have rich relational structures and various binary relational patterns. Various relation modelling strategies are proposed for embedding knowledge graphs, but they fail to fully capture both features of relations, rich r...
Complex-valued knowledge graph embedding (KGE) models like ComplEx effectively capture asymmetric relations but are fundamentally constrained by integer-order transformations. This restriction limits their ability to model multi-scale interactions, h...
Complexity science studies systems in which properties and behaviors emerge at meso- and macroscales that are difficult to predict and model by observing the properties and behaviors exhibited by the system’s components at smaller scales. The s...
Visual activity recognition plays a fundamental role in several research fields as a way to extract semantic meaning of images and videos. Prior work has mostly focused on classification tasks, where a label is given for a video clip. However, real l...
Knowledge graph (KG) technology is a newly emerged knowledge representation method in the field of artificial intelligence. Knowledge graphs can form logical mappings from cluttered data and establish triadic relationships between entities. Accurate...
Knowledge representation in autonomous robots with social roles has steadily gained importance through their supportive task assistance in domestic, hospital, and industrial activities. For active assistance, these robots must process semantic knowle...
Knowledge graphs have been extensively studied and applied, but most of these studies assume that the relationship facts in the knowledge graph are correct and deterministic. However, in the objective world, there inevitably exist uncertain relations...
The evolution of knowledge acquisition and representation in manufacturing technologies is vital for translating complex manufacturing data into actionable insights and advancing a comprehensive knowledge framework. This framework is pivotal in drivi...
Effective vertical seismic profiling (VSP) of upgoing and downgoing wave separation is essential for high-quality imaging. However, VSP wavefield separation is particularly challenging under complex geological conditions. Existing solutions encompass...
The purpose of representation learning is to encode the entities and relations in a knowledge graph as low-dimensional and real-valued vectors through machine learning technology. Traditional representation learning methods like TransE, a method whic...
The multi-granularity temporal knowledge graph question-answering model consists of two core tasks: question information extraction and knowledge graph embedding representation. Existing studies typically compute the relevance score between the quest...
Knowledge Graphs (KGs), a structural way to model human knowledge, have been a critical component of many artificial intelligence applications. Many KG-based tasks are built using knowledge representation learning, which embeds KG entities and relati...
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...
Knowledge graphs are foundational to structured knowledge representation and drive the evolution of artificial intelligence from data-driven to knowledge-enhanced paradigms. Hyper-relational knowledge graphs employ hyperedge structures to overcome th...
The mesoscopic structure significantly affects the properties of polycrystalline materials. Current artificial-based microstructure-performance analyses are expensive and require rich expert knowledge. Recently, some machine learning models have been...
Considering the complexity of entity pair relations and the information contained in the target neighborhood in few-shot knowledge graphs (KG), existing few-shot KG completion methods generally suffer from insufficient relation representation learnin...
Knowledge graphs as external information has become one of the mainstream directions of current recommendation systems. Various knowledge-graph-representation methods have been proposed to promote the development of knowledge graphs in related fields...
The present study investigates how the quality of knowledge representations contributes to rule transfer in a problem-solving context and how working memory capacity (WMC) might contribute to the subsequent failure or success in transferring the rele...
Autonomous robots are playing an important role to solve the Simultaneous Localization and Mapping (SLAM) problem in different domains. To generate flexible, intelligent, and interoperable solutions for SLAM, it is a must to model the complex knowled...
Distribution grids are complex networks containing multiple pieces of equipment. These components are interconnected, and each of them is described by various attributes. A knowledge graph is an interesting data format that represents pieces of infor...
We present a new method of self-supervised learning and knowledge distillation based on multi-views and multi-representations (MV–MR). MV–MR is based on the maximization of dependence between learnable embeddings from augmented and non-au...
Cooperative path planning in complex coastal mountainous environments near the shore is a knowledge-intensive engineering task crucial for demanding multi-UAV operations such as disaster rescue, environmental monitoring, and coastal emergency respons...
Digital twin technology is the talking point of academia and industry. When defining a digital twin, new modeling paradigms and computational methods are needed. Developments in the Internet of Things and advanced simulation and modeling techniques h...
With the progressive improvements in the power, effectiveness, and reliability of AI solutions, more and more critical human problems are being handled by automated AI-based tools and systems. For more complex or particularly critical applications, t...
Indoor fire is a sudden and frequent disaster that severely threatens the safety of indoor people worldwide. Indoor fire emergency evacuation is crucial to reducing losses involving various objects and complex relations. However, traditional studies...
Construction safety requirements (SRs), which serve as critical information encapsulating a wide range of safety-related issues, constitute a fundamental basis for effective construction safety management. The constraints of the complex information c...
Represention and modeling the dynamic landslide scenes is essential for gaining a comprehensive understanding and managing them effectively. Existing models, which focus on a single scale make it difficult to fully express the complex, multi-scale sp...
Forest fires destroy the ecological environment and cause large property loss. There is much research in the field of geographic information that revolves around forest fires. The traditional forest fire prediction methods hardly consider multi-sourc...
The new event cameras are now widely used in many computer vision applications. Their high raw data bitrate levels require a more efficient fixed-length representation for low-bandwidth transmission from the event sensor to the processing chip. A nov...
Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully rep...
The use of knowledge graphs has grown significantly in recent years. However, entities and relationships must be transformed into forms that can be processed by computers before the construction and application of a knowledge graph. Due to its simpli...
Complex Question Answering over Knowledge Graph (C-KGQA) seeks to solve complex questions using knowledge graphs. Currently, KGQA systems achieve great success in answering simple questions, while complex questions still present challenging issues. A...
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...
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...
Geohazards such as landslides, earthquakes, debris flows, and floods are governed by complex interactions among geological, hydrological, and human processes. Traditional data-driven models have improved hazard prediction but often lack interpretabil...
Large-scale knowledge graphs not only store entities and relations but also provide ontology-based information about them. Type constraints that exist in this information are of great importance for link prediction. In this paper, we proposed a novel...
Service robotics involves the design of robots that work in a dynamic and very open environment, usually shared with people. In this scenario, it is very difficult for decision-making processes to be completely closed at design time, and it is necess...
Knowledge graph-based recommendation methods are a hot research topic in the field of recommender systems in recent years. As a mainstream knowledge graph-based recommendation method, the propagation-based recommendation method captures users’...
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...
Generative modeling and representation learning of tandem mass spectrometry data aim to learn an interpretable and instrument-agnostic digital representation of metabolites directly from MS/MS spectra. Interpretable and instrument-agnostic digital re...
To address the problems of complex process knowledge sources, heterogeneous representations, dispersed semantic associations, and limited reusability in the domain of machining distortion of thin-walled parts, this study proposes a knowledge graph co...
There is a growing trend in robotics for implementing behavioural mechanisms based on human psychology, such as the processes associated with thinking. Semantic knowledge has opened new paths in robot navigation, allowing a higher level of abstractio...
Meanings and values of built heritage vary from factual and explicit meanings which are relatively easy to present, to more tacit knowledge, which is typically more challenging to communicate due to its implicit and often abstract character. In this...
Hyperspectral anomaly detection (HAD) aims to identify Earth’s surface anomalies without prior knowledge. However, most existing unsupervised HAD methods rely on training data roughly partitioned into background and anomalies, suffering from se...
In open-ended continuous environments, robots need to learn multiple parameterised control tasks in hierarchical reinforcement learning. We hypothesise that the most complex tasks can be learned more easily by transferring knowledge from simpler task...
Nature is composed of elements at various spatial scales, ranging from the atomic to the astronomical level. In general, human sensory experience is limited to the mid-range of these spatial scales, in that the scales which represent the world of the...
Imaging problems, including image reconstruction, restoration, enhancement, segmentation,
classification, object detection, and secure image transmission, have traditionally
been addressed using analytical methods derived from mathematical models and d...
As the complexity of mission planning increases, relying on the subjective experience of planners is no longer sufficient to meet the needs of modern mission planning. Knowledge mapping, as a structured knowledge management technique, provides an eff...
Whether learners can correctly complete exercises is influenced by multiple factors, including their mastery of relevant knowledge concepts and the interdependencies among these concepts. To investigate how the structure of the knowledge space—...
of 15