Distantly supervised relation extraction (DSRE) utilizes an external knowledge base to automatically label a corpus, which inevitably leads to the problem of mislabeling. Existing approaches utilize BERT to provide instances and relation embeddings t...
Knowledge Graph Embedding (KGE) is a powerful way to express Knowledge Graphs (KGs), which can help machines learn patterns hidden in the KGs. Relation patterns are useful hidden patterns, and they usually assist machines to predict unseen facts. Man...
As a crucial component of many natural language processing tasks, extracting entities and relations transforms unstructured text information into structured data, providing essential support for constructing knowledge graphs (KGs). However, current e...
In industrial vision, the lack of defect samples is one of the key constraints of depth vision quality inspection. This paper mainly studies defect detection under a small training set, trying to reduce the dependence of the model on defect samples b...
Relationship extraction is a crucial step in the construction of a knowledge graph. In this research, the grid field entity relationship extraction was performed via a labeling approach that used span representation. The subject entity and object ent...
Chemical-induced disease (CID) relation extraction plays an important role for disease treatment, drug repositioning, and drug discovery; thus, researchers employ natural language processing techniques to extract such relationships from the biomedica...
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...
As a fundamental task in the field of remote sensing, scene classification is increasingly attracting attention. The most popular way to solve scene classification is to train a deep neural network with a large-scale remote sensing dataset. However,...
Since the facts in the knowledge graph (KG) cannot be updated automatically over time, some facts have temporal conflicts. To discover and eliminate the temporal conflicts in the KG, this paper proposes a novel temporal conflict resolution method bas...
A knowledge graph is a structured semantic network designed to describe physical entities and relations in the world. A comprehensive and accurate knowledge graph is essential for tasks such as knowledge inference and recommendation systems, making l...
As global economic competition intensifies, collaborative innovation in strategic emerging industries has become a key factor in promoting economic growth and business development, attracting widespread attention. To delve into the mechanisms of coll...
Protein embedding is a protein representation that carries along the information derived from filtering large volumes of sequences stored in large archives. Routinely, the protein is represented by a matrix in which each residue is a context-specific...
Chinese knowledge base question answering (KBQA) is designed to answer the questions with the facts contained in a knowledge base. This task can be divided into two subtasks: topic entity extraction and relation selection. During the topic entity ext...
A Geographic Knowledge Graph (GeoKG) links geographic relation triplets into a large-scale semantic network utilizing the semantic of geo-entities and geo-relations. Unfortunately, the sparsity of geo-related information distribution on the web leads...
A knowledge graph (KG), also known as a knowledge base, is a particular kind of network structure in which the node indicates entity and the edge represent relation. However, with the explosion of network volume, the problem of data sparsity that cau...
Terrestrial, satellite, and internet High Definition (HD) and Ultra High Definition (UHD) broadcasting have experienced notable advancements in recent years, yet the potential within the DTV Essential Hidden Area (DEHA) in each frame remains largely...
This study introduces an unsupervised term extraction approach that combines non-negative matrix factorization (NMF) with word embeddings. Inspired by a pioneering semantic NMF method that employs regularization to jointly optimize document–wor...
A knowledge graph is a repository that represents a vast amount of information in the form of triplets. In the training process of completing the knowledge graph, the knowledge graph only contains positive examples, which makes reliable link predicti...
Anomaly detection plays a crucial role in data security and risk management across various domains, such as financial insurance security, medical image recognition, and Internet of Things (IoT) device management. Researchers rely on machine learning...
Network embedding (NE) is an important method to learn the representations of a network via a low-dimensional space. Conventional NE models focus on capturing the structural information and semantic information of vertices while neglecting such infor...
Recommender systems (RS) have been developed to make personalized suggestions and enrich users’ preferences in various online applications to address the information explosion problems. However, traditional recommender-based systems act as blac...
Advanced Persistent Threats (APTs) exhibit covert behaviors, long attack cycles, and fragmented intelligence, creating challenges for correlation analysis and attribution. This work proposes a unified knowledge-graph-based framework for multi-level A...
Related party transactions (RPTs) can serve as channels for the spread of credit risk events among blockchain firms. However, current credit risk-assessment models typically only consider a firm’s individual characteristics, overlooking the imp...
Background/Objectives: Microsatellite instability (MSI) is clinically important in colorectal cancer, but definitive assessment requires molecular or immunohistochemical testing. This study developed a leakage-safe, calibration-aware patient-level fr...
Relation classification tends to struggle when training data are limited or when it needs to adapt to unseen categories. In such challenging scenarios, recent approaches employ the metric-learning framework to measure similarities between query and s...
Relation Extraction is crucial for knowledge graph construction, but extracting complex relations in specialized domains like Satellite Navigation Countermeasures (SNCM) remains challenging due to long semantic spans and high relational density. Whil...
Software quality assurance is a critical aspect of software engineering, directly impacting maintainability, extensibility, and overall system performance. Traditional machine-learning techniques, such as gradient boosting and support vector machines...
The supervised model based on deep learning has made great achievements in the field of image classification after training with a large number of labeled samples. However, there are many categories without or only with a few labeled training samples...
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...
Relation extraction is one of the most important intelligent information extraction technologies, which can be used to construct and optimize services in intelligent communication systems (ICS). One issue with the existing relation extraction approac...
Understanding heterogeneity in farm development strategies is important in order to design effective policies and support programs to increase the sustainability of agriculture in relation to its socio-material context. Using a unique case study of 1...
Advances in language modeling have provided affordances for term extraction based on the capture of the lexical context and its semantics and encoding this in real-valued vectors (embeddings). Term importance is usually computed using quantitative me...
In an age overflowing with information, the task of converting unstructured data into structured data are a vital task of great need. Currently, most relation extraction modules are more focused on the extraction of local mention-level relations&mdas...
User alignment (UA), a central issue for social network analysis, aims to recognize the same natural persons across different social networks. Existing studies mainly focus on the positive effects of incorporating user attributes and network structur...
Deep learning-based methods have shown great promise for synthetic aperture radar (SAR) automatic target recognition (ATR) in recent years. These methods demonstrate superior performance compared to traditional approaches across various recognition t...
In real world applications, binary classification is often affected by imbalanced classes. In this paper, a new methodology to solve the class imbalance problem that occurs in image classification is proposed. A digital image is described through a n...
Promoting low-carbon, green development in the industrial sector is crucial for the sustainable development of the economy and society in China. As the micro-entity of industrial carbon reduction, the question of how to enhance the carbon reduction c...
GFRP bars will be damaged due to a series of irreversible hygroscopic chemical reactions under humid and hot curing environmental conditions. The multiple factors related to the moisture absorption model were established through the moisture absorpti...
Various tasks in natural language processing (NLP) suffer from lack of labelled training data, which deep neural networks are hungry for. In this paper, we relied upon features learned to generate relation triples from the open information extraction...
When dealing with complex entrepreneurial network problems, such as sustainable resource flows, the highly uncertainty in environment that brings cognitive bias in entrepreneurs’ decision-making means which entrepreneurs who are expert in using...
Embedding the program of elderly care into community-based service system seems to imply that China is reorganising capacities of neighbourhood governance. The program, created by transformation of neighbourhood governance, represented the state gove...
Distantly supervised relation extraction is the most popular technique for identifying semantic relation between two entities. Most prior models only focus on the supervision information present in training sentences. In addition to training sentence...
The intention of this research is to establish a relationship between dairy cattle diseases with various non-invasive sensors for the development of a health monitoring system. This paper expands on the conference paper titled “Sensor technology for...
10 March 2023
The conservation of avian diversity plays a critical role in maintaining ecological balance and ecosystem function, as well as having a profound impact on human survival and livelihood. With species’ continuous and rapid decline, information an...
Most of the dimensionality reduction algorithms assume that data are independent and identically distributed (i.i.d.). In real-world applications, however, sometimes there exist relationships between data. Some relational learning methods have been p...
This research addresses the need to enhance agricultural management due to rapid population growth in the 20th and 21st centuries. It focuses on integrating sensors and embedded systems to collect data on soil and air conditions, including temperatur...
In recent years, MKR has attracted increasing attention due to its ability to enhance the accuracy of recommendation systems through cooperation between the RS tasks and the KGE tasks, allowing for complementarity of the information. However, there a...
This article addresses the task of building personalized educational recommendations based on a heterogeneous knowledge graph that integrates data from university curricula, job vacancies, and online courses. To solve the problem of course recommenda...
Classifying social media (SM) messages into relevant or irrelevant categories is challenging due to data sparsity, imbalance, and ambiguity. This study aims to improve Intelligent Transport Systems (ITS) by enhancing short text classification of traf...
Knowledge graph-enhanced recommendation has gained increasing attention for its ability to provide structured semantic context. However, most existing approaches struggle with two critical challenges: the sparsity of long-tail relations in knowledge...
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