Article
Computer Science, Information Systems
Kun Ding, Shanshan Liu, Yuhao Zhang, Hui Zhang, Xiaoxiong Zhang, Tongtong Wu, Xiaolei Zhou
Summary: The joint extraction of entities and their relations from texts is crucial for natural language processing. A hybrid neural framework incorporating external knowledge improved performance in handling overlapping issues and relation prediction. Experiments on a Chinese military corpus validated the effectiveness of the proposed method in entity and relation extraction.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Article
Chemistry, Multidisciplinary
Haiyang Zhang, Guanqun Zhang, Yue Ma
Summary: The study proposes a framework that incorporates syntax knowledge and local focus mechanism into entity and relation extraction, significantly enhancing the model performance by fusing syntactic and semantic features as well as obtaining richer contextual features from local context.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Kang Zhao, Hua Xu, Yue Cheng, Xiaoteng Li, Kai Gao
Summary: This paper proposes a relation extraction model RIFRE based on heterogeneous graph neural networks. Through representation iterative fusion, it successfully establishes effective connections between entities and relations, improving the accuracy and efficiency of relation extraction. Empirical results on multiple datasets have demonstrated the superior performance of RIFRE.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Qian Wan, Luona Wei, Xinhai Chen, Jie Liu
Summary: This paper proposes a Region-based Hypergraph Network (RHGN) for joint entity and relation extraction, which introduces the concept of regional hypernodes and constructs a region-based relation hypergraph to improve performance. Experimental results demonstrate the superior performance of the model in both entity recognition and relation extraction.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Yongming Nian, Yanping Chen, Yongbin Qin, Ruizhang Huang, Ruixue Tang, Ying Hu
Summary: This paper presents a model for detecting entity boundaries and predicting entity candidates jointly. The model makes predictions based on gap representations between words, avoiding ambiguity when a token has multiple labels. Additionally, a neighborhood span proposal strategy is proposed to address the issue of data imbalance. The model achieves performance close to the state-of-the-art in ACE2005 and GENIA corpora.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Tiantian Chen, Lianke Zhou, Nianbin Wang, Xirui Chen
Summary: This study proposes a joint extraction model named PARE-Joint, which addresses the interaction between entities and relations as well as the overlapping triple problem using position-aware attention and relation embedding. The experimental results demonstrate that the proposed model outperforms other baselines on four public datasets.
APPLIED SOFT COMPUTING
(2022)
Article
Multidisciplinary Sciences
Yongbin Qin, Weizhe Yang, Kai Wang, Ruizhang Huang, Feng Tian, Shaolin Ao, Yanping Chen
Summary: This paper introduces task-related entity indicators to enhance the performance of relation extraction through a deep neural network. The experimental results show that this method has outperformed other methods in F1 score on different corpora.
Article
Computer Science, Artificial Intelligence
Qian Wan, Luona Wei, Shan Zhao, Jie Liu
Summary: In this study, a Span-based Multi-Modal Attention Network (SMAN) is proposed for joint entity and relation extraction. The network introduces a cloze mechanism to simultaneously extract the context and span position information, and jointly models the span and label in the relation extraction stage. Experimental results demonstrate that the proposed model consistently outperforms the state-of-the-art and other competing approaches in entity recognition and relation extraction tasks.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Yizhao Wu, Yanping Chen, Yongbin Qin, Ruizhang Huang, Ruixue Tang
Summary: Recognizing entities and extracting their relations are important tasks in information extraction. Recent works have focused on leveraging entity markers for better span representations and have achieved promising performance. However, existing works have two shortcomings: (1) previous markers are randomly embedded in distributed representations, ignoring semantic information relevant to the targeted tokens; (2) most works simply implant markers into a sentence, lacking the ability to encode the interrelation between multiple tokens. This work proposes a marker collaborating model for entity and relation extraction, consisting of two modules, and achieves state-of-the-art performance on three standard benchmarks (ACE04, ACE05, and SciERC).
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Multidisciplinary Sciences
Dongchen Han, Zhaoqian Zheng, Hui Zhao, Shanshan Feng, Haiting Pang
Summary: Extracting entities and relations from unstructured text has gained attention recently. Existing methods have achieved good results but struggle with entity overlap and exposure bias. To address these challenges, we propose a joint entity relation extraction model based on a span-level multi-head selection mechanism. Our model outperforms the baseline method on English dataset NYT and Chinese dataset DuIE 2.0, confirming its effectiveness.
Article
Computer Science, Artificial Intelligence
Bo Qiao, Zhuoyang Zou, Yu Huang, Kui Fang, Xinghui Zhu, Yiming Chen
Summary: The study introduces a construction technology for agricultural knowledge graphs, including a BERT-based entity relationship joint extraction model for extracting relationships between entities in the agricultural domain.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Chen Gao, Xuan Zhang, LinYu Li, JinHong Li, Rui Zhu, KunPeng Du, QiuYing Ma
Summary: Joint entity and relation extraction is a crucial task in knowledge graph construction. Existing methods have limitations such as unrelated relation prediction and lack of interaction between relation and entity. We propose a lightweight joint extraction model based on global entity matching strategy, consisting of three components: Relation Extraction Module, Relation Attention Based Entity Recognition Module, and Global Entity Pairing Module. Our model simplifies the structure and leverages relevant information by decomposing extraction into sub-tasks. We also introduce negative sampling to alleviate exposure bias, achieving effective triple overlap resolution, improved performance, and reduced memory usage.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Tingting Hang, Jun Feng, Yirui Wu, Le Yan, Yunfeng Wang
Summary: In this study, an end-to-end neural network model (BERT-JEORE) is proposed for joint extraction of entities and overlapping relations, with the use of parameter sharing and a source-target BERT model to improve labeled data quality. A three-step overlapping relations extraction model is designed to predict relations between all entity pairs. Experimental results demonstrate that BERT-JEORE outperforms baseline models and effectively captures different types of overlapping relational triplets in a sentence.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Minkyu Jeong, Hyowon Suh, Heejung Lee, Jae Hyun Lee
Summary: This paper presents a text-based research method to extract key information and knowledge from equipment maintenance documents, addressing issues related to semantically ambiguous expressions. By utilizing named entity recognition and dependency parsing methods, maintenance knowledge was effectively extracted.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Youwei Wang, Ying Wang, Zhongchuan Sun, Yinghao Li, Shizhe Hu, Yangdong Ye
Summary: The study proposes a novel and effective model for joint named entity recognition and relation extraction task, which can automatically capture purified task-specific features to improve classification performance. The experiment results show that the proposed model achieves promising results on different benchmarks.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Computer Science, Artificial Intelligence
Gilles Vandewiele, Isabelle Dehaene, Gyorgy Kovacs, Lucas Sterckx, Olivier Janssens, Femke Ongenae, Femke De Backere, Filip De Turck, Kristien Roelens, Johan Decruyenaere, Sofie Van Hoecke, Thomas Demeester
Summary: Although information extracted from electrohysterography recordings could provide valuable insights into estimating the risk of preterm birth, recent studies have reported overly optimistic results due to a methodological flaw. Specifically, applying over-sampling before partitioning the data could lead to biased results, impacting the predictive performance of related studies.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, Luc De Raedt
Summary: DeepProbLog is a neural probabilistic logic programming language that supports symbolic and subsymbolic representations and inference, program induction, probabilistic programming, and learning from examples. It integrates general-purpose neural networks and expressive probabilistic-logical modeling and reasoning, allowing end-to-end training based on examples.
ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Klim Zaporojets, Giannis Bekoulis, Johannes Deleu, Thomas Demeester, Chris Develder
Summary: This study proposes a method for solving arithmetic word problems in natural language processing systems, using Tree-RNN to score candidate solution equations. Experimental results show that this method outperforms previous algorithms in terms of accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Klim Zaporojets, Johannes Deleu, Chris Develder, Thomas Demeester
Summary: DWIE is a newly created multi-task dataset that combines four main Information Extraction (IE) annotation subtasks, focusing on entity-centric descriptions of interactions and properties of conceptual entities. Challenges in building and evaluating IE models for DWIE include the need for a new entity-driven metric to avoid dominance of frequently mentioned entities and the requirement for information transfer between entity mentions in different parts of the document and across different tasks. Incorporating neural graph propagation into a joint model showed significant improvement in F-1 percentage points, showcasing DWIE's potential to stimulate research in graph neural networks for multi-task IE.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Article
Energy & Fuels
Jonas Van Gompel, Domenico Spina, Chris Develder
Summary: Current techniques for PV fault diagnosis, while accurate, are costly and limit widespread adoption. This study proposes a machine learning-based approach that utilizes satellite weather data and low-frequency inverter measurements for precise fault diagnosis in PV systems.
Article
Computer Science, Artificial Intelligence
Amir Hadifar, Johannes Deleu, Chris Develder, Thomas Demeester
Summary: This paper introduces a new method for dynamic sparseness that combines sparsity with block-wise matrix-vector multiplications to improve efficiency. Unlike static sparseness, this method preserves the full network capabilities and outperforms static sparseness baselines in the task of language modeling.
PATTERN RECOGNITION LETTERS
(2021)
Article
Energy & Fuels
Gargya Gokhale, Bert Claessens, Chris Develder
Summary: This paper proposes a data-driven modeling approach using physics informed neural networks for control-oriented thermal modeling of buildings. By utilizing measured data and prior information, this approach accurately predicts the temperature, power consumption, and hidden state of building thermal mass, achieving higher prediction accuracy and data efficiency compared to conventional neural networks.
Article
Computer Science, Artificial Intelligence
Yiwei Jiang, Klim Zaporojets, Johannes Deleu, Thomas Demeester, Chris Develder
Summary: This work introduces a new dialog dataset, CookDial, for research on task-oriented dialog systems with procedural knowledge understanding. The dataset consists of 260 human-to-human task-oriented dialogs in which an agent guides the user to cook a dish based on a recipe document. CookDial dialogs exhibit procedural alignment and complex agent decision-making, and three challenging tasks are identified. Neural baseline models are developed for each task and evaluated on the CookDial dataset. The dataset is publicly released to stimulate further research on domain-specific document-grounded dialog systems.
APPLIED INTELLIGENCE
(2023)
Article
Automation & Control Systems
Manu Lahariya, Farzaneh Karami, Chris Develder, Guillaume Crevecoeur
Summary: This article discusses the importance of identifying and designing control for flexibility in the evaporative cooling process using machine learning methods. The integration of system dynamics into ML models such as PhyLSTMs and PhyNNs allows for better modeling of complex nonlinear behavior. The performance and optimization of these methods are analyzed in relation to training data size.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Lusine Abrahamyan, Yiming Chen, Giannis Bekoulis, Nikos Deligiannis
Summary: The study proposes a gradient compression method based on distributed learning, which improves compression efficiency by leveraging inter-node gradient correlations. Experimental results show significant compression effects across different datasets and deep learning models.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Xiangyu Yang, Giannis Bekoulis, Nikos Deligiannis
Summary: This study addresses the problem of detecting traffic events using Twitter and proposes two subtasks: a text classification subtask to identify whether a tweet is traffic-related or not, and a slot filling subtask to extract fine-grained information about the traffic event. Experimental results show that the proposed deep learning methods achieve high performance scores (95%+ F1 score) on the constructed datasets for both subtasks, even in a transfer learning scenario. The Dutch Traffic Twitter datasets from Belgium and the Brussels capital region, as well as the code, are available on GitHub.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Yoan A. Lopez, Hector R. Gonzalez Diez, Orlando Grabiel Toledano-Lopez, Yusniel Hidalgo-Delgado, Erik Mannens, Thomas Demeester
Summary: This research proposes an extended version of DLIME called DLIME-Graphs for explaining machine learning models on graphs. By reducing triple embeddings using UMAP and clustering with HDB-SCAN, DLIME-Graphs is able to provide explanations for 100% of the triples in the test dataset, enhancing the transparency and interpretability of the models.
KNOWLEDGE GRAPHS AND SEMANTIC WEB, KGSWC 2022
(2022)
Article
Computer Science, Information Systems
Tien Huu Do, Marc Berneman, Jasabanta Patro, Giannis Bekoulis, Nikos Deligiannis
Summary: The study proposes a generic model for identifying fake news that considers both the news content and the social context, using shallow and deep representations to examine different aspects of news content. Additionally, the utilization of graph convolutional neural networks and mean-field layers helps leverage the structural information of news articles and their social context for improved performance.
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)