With the continuous development and popularization of remote sensing technology, remote sensing images have been widely used in the field of land cover classification. Since remote sensing images have complex spatial structure and texture features, i...
Deep neural network-based deep clustering has achieved remarkable success by unifying representation learning and clustering. However, conventional representation modules are typically not tailored for clustering, resulting in conflicting objectives...
Metabolomics-based genome-wide association studies (mGWAS) are key to understanding the genetic regulations of metabolites in complex phenotypes. We previously developed mGWAS-Explorer 1.0 to link single-nucleotide polymorphisms (SNPs), metabolites,...
In this paper, we establish several expansion formulas for products of the Jacobi theta functions. As applications, we derive some expressions of the powers of
We consider the concept of fuzzy
The development of knowledge graphs about water resources as a tool for studying the sustainable development of a region is currently an urgent task, because the growing deterioration of the state of water bodies affects the ecology, economy, and hea...
Today’s technological improvements have made ubiquitous healthcare systems that converge into smart healthcare applications in order to solve patients’ problems, to communicate effectively with patients, and to improve healthcare service quality. The...
Few-shot knowledge graph completion (FKGC) tasks involve determining the authenticity of triple candidates using a small number of reference triples with a given relation. Intuitively, the expression of relation features contributes to the close corr...
As Advanced Persistent Threats (APTs) continue to evolve, constructing a dynamic cybersecurity knowledge graph requires precise extraction of entity–relationship triples from unstructured threat intelligence. Existing approaches, however, face...
This paper proposes a simple knowledge base enrichment based on parse tree patterns with a semantic filter. Parse tree patterns are superior to lexical patterns used commonly in many previous studies in that they can manage long distance dependencies...
Emergency scenarios in the Internet of Vehicles (IoV) face significant challenges due to the stringent requirements for ultra-reliable and low-latency communication under high-mobility conditions. This paper proposes a cooperative transmission framew...
The purpose of knowledge representation learning is to learn the vector representation of research objects projected by a matrix in low-dimensional vector space and explore the relationship between embedded objects in low-dimensional space. However,...
The effectiveness of hashing methods in big data retrieval has been proved due to their merit in computational and storage efficiency. Recently, encouraged by the strong discriminant capability of deep learning in image representation, various deep h...
Explainable Artificial Intelligence (XAI) has primarily focused on explaining model predictions, yet a critical gap remains in explaining semantic structure discovery within knowledge graphs derived from concept maps (CMs). This study extends the OBO...
Integrating heterogeneous and multilingual geoscience texts into coherent knowledge graphs is challenged by semantic inconsistencies from terminology variations, diverse expressions, and data heterogeneity, hindering the construction of reliable mine...
Due to the fast retrieval speed and low storage cost, cross-modal hashing has become the primary method for cross-modal retrieval. Since the emergence of deep cross-modal hashing methods, cross-modal retrieval significantly improved. However, the exi...
The aim of Medical Knowledge Graph Completion is to automatically predict one of three parts (head entity, relationship, and tail entity) in RDF triples from medical data, mainly text data. Following their introduction, the use of pretrained language...
Large-language-model (LLM) APIs demonstrate impressive reasoning capabilities, but their size, cost, and closed weights limit the deployment of knowledge-aware AI within biomedical research groups. At the other extreme, standard attention-based neura...
Natural heritage digitization has evolved beyond simple 3D representation. Contemporary approaches require transparent documentation integrating biological, heritage, and digitization standards, yet existing frameworks operate in isolated domains wit...
Entity and relation extraction (ERE) is a core task in information extraction. This task has always faced the overlap problem. It was found that heterogeneous graph attention networks could enhance semantic analysis and fusion between entities and re...
Interoperability has become a major challenge for the development of integrated healthcare applications. This is mainly because of the reason that data is collected, processed, and managed using heterogeneous protocols, different data formats, and di...
The Singapore Government has embarked on a project to establish a three-dimensional city model and collaborative data platform for Singapore. The research herein contributes to this endeavour by developing a methodology and algorithms to automate the...
The primary task of few-shot relation extraction is to quickly learn the features of relation classes from a few labelled instances and predict the semantic relations between entity pairs in new instances. Most existing few-shot relation extraction m...
The domain of Aspect Level Sentiment Classification, in which the sentiment toward a given aspect is analyzed, attracts much attention in NLP. Recently, the state-of-the-art Aspect Level Sentiment Classification methods are devised by using the Graph...
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...
This study focuses on the extraction and semantic analysis of data from tables, emphasizing the importance of understanding the semantics of tables to obtain useful information. The main goal was to develop a technology using the ontology for the sem...
Chatbots, also known as conversation agents, are programs that are able to simulate and reproduce an intelligent conversation with humans. Although this type of program is not new, the explosion of the available information and the rapid increase of...
Library organizations have enthusiastically undertaken semantic web initiatives and in particular the data publishing as linked data. Nevertheless, different surveys report the experimental nature of initiatives and the consumer difficulty in re-usin...
The cultural heritage of the Liao dynasty in Chifeng encompasses significant historical and cultural information that requires systematic digital preservation and management. However, heterogeneous data sources across museums, archives, and research...
Standardized approaches to relevance classification in information retrieval use generative statistical models to identify the presence or absence of certain topics that might make a document relevant to the searcher. These approaches have been used...
Skin lesion segmentation has achieved notable progress in recent years; however, accurate delineation remains challenging due to complex backgrounds, ambiguous boundaries, and low lesion-to-skin contrast. To address these issues, we propose the lesio...
Link prediction is a key technique for connecting entities and relationships in a graph reasoning field. It leverages known information about the graph structure data to predict missing factual information. Previous studies have either focused on the...
The main objective of Linked Data is linking and integration, and a major step for evaluating whether this target has been reached, is to find all the connections among the Linked Open Data (LOD) Cloud datasets. Connectivity among two or more dataset...
Few-shot knowledge graph (KG) completion is challenged by the dynamic and long-tail nature of real-world KGs, where only a handful of relation-specific triples are available for each new relation. Existing methods often over-rely on neighbor informat...
The digitisation of law-enforcement archives is examined with the aim of moving from static analogue records to interoperable semantic information systems. A step-by-step framework for optimal digitisation is proposed, grounded in archival best pract...
Querying Linked Data is one of the most important issues for the semantic web community today because it requires the user to understand the structure and vocabularies used in various data sources. Furthermore, users must be familiar with the syntax...
In the field of question answering-based knowledge graphs, due to the complexity of the construction of knowledge graphs, a domain-specific knowledge graph often cannot contain some common-sense knowledge, which makes it impossible to answer question...
Cyber Threat Intelligence (CTI) plays a crucial role in cybersecurity. However, traditional information extraction has low accuracy due to the specialization of CTI and the concealment of relations. To improve the performance of CTI relation extracti...
Knowledge representation learning is representing entities and relations in a knowledge graph as dense low-dimensional vectors in the continuous space, which explores the features and properties of the graph. Such a technique can facilitate the compu...
Knowledge graph link prediction is crucial for constructing triples in knowledge graphs, which aim to infer whether there is a relation between the entities. Recently, graph neural networks and contrastive learning have demonstrated superior performa...
Knowledge graph completion (KGC), the process of predicting missing knowledge through known triples, is a primary focus of research in the field of knowledge graphs. As an important graph representation technique in deep learning, graph neural networ...
This paper develops an approach to perform binary semantic segmentation on Arabidopsis thaliana root images for plant root phenotyping using a conditional generative adversarial network (cGAN) to address pixel-wise class imbalance. Specifically, we u...
The digital transformation of Tibetan cultural tourism is hindered by high manual costs, weak semantic adaptability, and cultural security risks. To address these, this paper proposes RLT2C, a “Rule+LLM-Verify” approach to automated and c...
Relation extraction, a fundamental task in natural language processing, aims to extract entity triples from unstructured data. These triples can then be used to build a knowledge graph. Recently, pre-training models that have learned prior semantic a...
Significance and popularity of Role-Based Access Control (RBAC) is inevitable; however, its application is highly challenging in multi-domain collaborative smart city environments. The reason is its limitations in adapting the dynamically changing in...
Earth Observation data possess tremendous potential in understanding the dynamics of our planet. We propose the Semantics-driven Remote Sensing Scene Understanding (Sem-RSSU) framework for rendering comprehensive grounded spatio-contextual scene desc...
The Internet is not just used for communication, transactions, and cloud storage; it also serves as a massive knowledge store where both people and machines can create, analyze, and use data and information. The Semantic Web was designed to enable ma...
13 April 2020
Constructed emergency response scenarios provide a basis for decision makers to make management decisions, and the development of such scenarios considers earlier historical cases. Over the decades, the development of emergency response scenarios has...
Since data are gradually enriched over time, knowledge graphs are inherently imperfect. Thus, knowledge graph completion is proposed to perfect knowledge graph by completing triples. Currently, a family of translation models has become the most effec...
Knowledge graph completion can make knowledge graphs more complete, which is a meaningful research topic. However, the existing methods do not make full use of entity semantic information. Another challenge is that a deep model requires large-scale m...
of 3