Cosine Similarity Research Articles

A novel semi-supervised learning rolling bearing fault diagnosis method based on SNNGAN

In practical industrial environments, rotating machinery typically operates under normal conditions. As a result, the signals collected are primarily normal signals. This imbalance in the sample data diminishes the effectiveness of fault diagnosis. To address this issue, this paper produces a novel semi-supervised fault diagnosis approach based on a Siamese neural network combined with a generative adversarial network (SNNGAN) to enhance classification accuracy. Firstly, vibration signals collected are subjected to continuous wavelet transformation to obtain time–frequency representations, which are utilized for pre-training convolutional encoders in the generator and discriminator. Subsequently, a cosine similarity algorithm is employed to ensure the quality of generated samples. For generated data, set a similarity threshold. Those surpassing the threshold are assigned their corresponding labels and added to the original sample set. Otherwise, those falling below the threshold are transformed back into vibration vectors through an inverse transform and then serve as input to create new samples. Finally, fault diagnosis experiments are conducted on the newly balanced data set. In four imbalanced data experiments, the results demonstrate that SNNGAN outperforms other methods in average accuracy, G-mean, and F1 score, with average accuracy values of 0.919, 0.948, 0.927, and 0.953 for the respective datasets. Therefore, SNNGAN exhibits outstanding fault diagnosis performance under conditions of data imbalance.

Vibration Signal Noise-Reduction Method of Slewing Bearings Based on the Hybrid Reinforcement Chameleon Swarm Algorithm, Variate Mode Decomposition, and Wavelet Threshold (HRCSA-VMD-WT) Integrated Model.

To enhance fault detection in slewing bearing vibration signals, an advanced noise-reduction model, HRCSA-VMD-WT, is designed for effective signal noise elimination. This model innovates by refining the Chameleon Swarm Algorithm (CSA) into a more potent Hybrid Reinforcement CSA (HRCSA), incorporating strategies from Chaotic Reverse Learning (CRL), the Whale Optimization Algorithm's (WOA) bubble-net hunting, and the greedy strategy with the Cauchy mutation to diversify the initial population, accelerate convergence, and prevent local optimum entrapment. Furthermore, by optimizing Variate Mode Decomposition (VMD) input parameters with HRCSA, Intrinsic Mode Function (IMF) components are extracted and categorized into noisy and pure signals using cosine similarity. Subsequently, the Wavelet Threshold (WT) denoising targets the noisy IMFs before reconstructing the vibration signal from purified IMFs, achieving significant noise reduction. Comparative experiments demonstrate HRCSA's superiority over Particle Swarm Optimization (PSO), WOA, and Gray Wolf Optimization (GWO) regarding convergence speed and precision. Notably, HRCSA-VMD-WT increases the Signal-to-Noise Ratio (SNR) by a minimum of 74.9% and reduces the Root Mean Square Error (RMSE) by at least 41.2% when compared to both CSA-VMD-WT and Empirical Mode Decomposition with Wavelet Transform (EMD-WT). This study improves fault detection accuracy and efficiency in vibration signals and offers a dependable and effective diagnostic solution for slewing bearing maintenance.

Optimizing in silico drug discovery: simulation of connected differential expression signatures and applications to benchmarking.

We present a novel simulation method for generating connected differential expression signatures. Traditional methods have struggled with the lack of reliable benchmarking data and biases in drug-disease pair labeling, limiting the rigorous benchmarking of connectivity-based approaches. Our aim is to develop a simulation method based on a statistical framework that allows for adjustable levels of parametrization, especially the connectivity, to generate a pair of interconnected differential signatures. This could help to address the issue of benchmarking data availability for connectivity-based drug repurposing approaches. We first detailed the simulation process and how it reflected real biological variability and the interconnectedness of gene expression signatures. Then, we generated several datasets to enable the evaluation of different existing algorithms that compare differential expression signatures, providing insights into their performance and limitations. Our findings demonstrate the ability of our simulation to produce realistic data, as evidenced by correlation analyses and the log2 fold-change distribution of deregulated genes. Benchmarking reveals that methods like extreme cosine similarity and Pearson correlation outperform others in identifying connected signatures. Overall, our method provides a reliable tool for simulating differential expression signatures. The data simulated by our tool encompass a wide spectrum of possibilities to challenge and evaluate existing methods to estimate connectivity scores. This may represent a critical gap in connectivity-based drug repurposing research because reliable benchmarking data are essential for assessing and advancing in the development of new algorithms. The simulation tool is available as a R package (General Public License (GPL) license) at https://github.com/cgonzalez-gomez/cosimu.

Robust Spherical Laplacian Embedding.

Laplacian embedding (LE) aims to project high-dimensional input data samples, which often contain nonlinear structures, into a low-dimensional space. However, existing distance functions used in the embedding space fail to provide discriminative representations for real-world datasets, especially those related to text analysis or image processing. Cosine similarity measurements are advantageous in dealing with sparse data but are fragile to the impact of outliers and noise from samples or data features. In this work, we propose robust spherical LE (RS-LE), a powerful method that builds the LE on a novel metric that unifies the Euclidean distance and cosine similarity in a spherical space using a robust lp,p -norm. We introduce an efficient iterative algorithm, the proximal alternating linearized minimization (ALM) method, to solve the difficult optimization problem that arises from the new metric, which is neither convex nor smooth. This algorithm allows the solution to achieve both global and objective convergence. Our findings are presented in rigorous theoretical analysis and validated in experiments.

A method to find temporal structure of neuronal coactivity patterns with across-trial correlations

BackgroundThe across-trial correlation of neurons' coactivity patterns emerges to be important for information coding, but methods for finding their temporal structures remain largely unexplored. New methodIn the present study, we propose a method to find time clusters in which coactivity patterns of neurons are correlated across trials. We transform the multidimensional neural activity at each timing into a coactivity pattern of binary states, and predict the coactivity patterns at different timings. We devise a method suitable for these coactivity pattern predictions, call general event prediction. Cross-temporal prediction accuracy is then used to estimate across-trial correlations between coactivity patterns at two timings. We extract time clusters from the cross-temporal prediction accuracy by a modified k-means algorithm. ResultsThe feasibility of the proposed method is verified through simulations based on ground truth. We apply the proposed method to a calcium imaging dataset recorded from the motor cortex of mice, and demonstrate time clusters of motor cortical coactivity patterns during a motor task. Comparison with existing methodsWhile the existing cosine similarity method, which does not account for across-trial correlation, shows temporal structures only for contralateral neural responses, the proposed method reveals those for both contralateral and ipsilateral neural responses, demonstrating the effect of across-trial correlations. ConclusionsThis study introduces a novel method for measuring the temporal structure of neuronal ensemble activity.

Selecting Pedal Load for Lower-Limb Rehabilitation Based on the Combination of Muscle Synergy and Fourier Series

This paper introduces a new lower-limb rehabilitation machine that meets the rehabilitation needs of hemiplegic patients. First, a left–right independent rotary pedal mechanism was selected to facilitate rehabilitation and adapt to the user’s physical condition. Then, a half model of the lower-limb rehabilitation machine is designed and manufactured with ergonomics in mind. As analytical tools, we combine non-negative matrix factorization and non-negative double singular value decomposition to calculate muscle synergy of the walking muscle surface electromyography (sEMG) signal, and use cosine similarity to evaluate the similarity between walking and pedaling activities. By comparing the results of the walking and pedaling experiments, the effectiveness of pedaling in gait rehabilitation is revealed. To further improve the similarity between walking and pedaling, double integration of the sEMG signal is introduced, and the relationship between load input and rotation angle is described for the first time using Fourier series. The results of the experiment confirmed that more than half of the 10 subjects performed pedaling exercises similar to walking using Fourier series loading compared to pedaling exercises with normal constant loading. This loading parameter may have the potential to improve rehabilitation efficiency for many subjects compared to the usual exercise.

Multi-label feature selection via similarity constraints with non-negative matrix factorization

Feature selection plays a key role in preprocessing, effectively addressing the curse of dimensionality in multi-label learning. While current approaches commonly utilize feature or label similarity to construct the weight matrix, typically through manifold learning regularization, there has been a dearth of progress in developing similarity-constrained regularization terms for multi-label feature selection. To address this gap, this paper conducts a comprehensive investigation and proposes a novel similarity constraint leveraging non-negative matrix factorization techniques. Subsequently, a new algorithm is introduced termed Multi-label Feature Selection via Similarity Constraints with Non-negative Matrix Factorization (SCNMF). Initially, the Gaussian similarity matrix among features is computed and factorized into a weight matrix using non-negative matrix factorization. Subsequently, the Cosine distance matrix among labels is computed to constrain the weight matrix. Finally, an objective function is formulated based on the aforementioned constraint mechanisms and iteratively optimized. Extensive experiments conducted across more than 10 multi-label datasets demonstrate the superior performance of our proposed approach. The source code is accessible at the URL: https://github.com/BIGOatMNNU/SCNMF.

Reliability of the Brief Assessment of Transactional Success in Communication in Aphasia

ABSTRACT Background While many measures exist for assessing discourse in aphasia, manual transcription, editing, and scoring are prohibitively labor intensive, a major obstacle to their widespread use by clinicians (Bryant et al. 2017; Cruice et al. 2020). Many tools also lack rigorous psychometric evidence of reliability and validity (Azios et al. 2022; Carragher et al. 2023). Establishing test reliability is the first step in our long-term goal of automating the Brief Assessment of Transactional Success in aphasia (BATS; Kurland et al. 2021) and making it accessible to clinicians and clinical researchers. Aims We evaluated multiple aspects of test reliability of the BATS by examining correlations between human/machine and human/human interrater edited transcripts, raw vs. edited transcripts, interrater scoring of main concepts, and test-retest performance. We hypothesized that automated methods of transcription and discourse analysis would demonstrate sufficient reliability to move forward with test development. Methods & Procedures We examined 576 story retelling narratives from a sample of 24 persons with aphasia and familiar and unfamiliar conversation partners (CP). Participants with aphasia (PWA) retold stories immediately after watching/listening to short video/audio clips. CP retold stories after six-minute topic-constrained conversations with a PWA in which the dyad co-constructed the stories. We utilized two macrostructural measures to analyze the automated speech-to-text transcripts of story retells: 1) a modified version of a semi-automated tool for measuring main concepts (mainConcept: Cavanaugh et al. 2021); and 2) an automated natural language processing “pipeline” to assess topic similarity. Outcomes & Results Correlations between raw and edited scores were excellent, interrater reliability on transcripts and main concept scoring were acceptable. Test-retest on repeated stimuli was acceptable. This was especially true of aphasic story retellings where there were actual within subject repeated stimuli. Conclusions Results suggest that automated speech-to-text was generally sufficient in most cases to avoid the time-consuming, labor intensive step of transcribing and editing discourse. Overall, our study results suggest that natural language processing automated methods such as text vectorization and cosine similarity are a fast, efficient way to obtain a measure of topic similarity between two discourse samples. Although test-retest reliability for the semi-automated mainConcept method was generally higher than for automated methods of measuring topic similarity, we found no evidence of a difference between machine automated and human-reliant scoring.

A landslide susceptibility assessment method considering the similarity of geographic environments based on graph neural network

Landslide susceptibility assessment (LSA) is vital for landslide mitigation and management. Existing LSA methods only consider local environmental characteristics associated with landslides, while neglecting the characteristics of remote yet interconnected geographic environments. In addition, the datasets for landslide samples do not consider the equilibrium among various geographic environmental characteristics. Ultimately, the landslide susceptibility mapping (LSM) generated is unreliable. We propose an LSA method based on the similarity of geographic environments and the equilibrium of landslide samples. We selected the Bailong River Basin (BRB) as the study area. Our proposed LSA method has four steps: (1) We constructed slope units using the curvature watershed method (CWM) based on the digital elevation model (DEM) to generate geographic nodes and selected 12 landslide influencing factors (LIFs) through multicollinearity analysis to extract geographic node features. (2) We constructed an association graph of geographic environment similarity constraints based on geographic node features using the cosine similarity method to correlate landslide and geographical environments. (3) We constructed sample datasets for an equal number of landslide and non-landslides based on various geographic environments. (4) We constructed a graph neural network (GNN) model based on the association graph to predict the susceptibility of unknown geographic nodes, obtaining a reliable LSM. Compared with random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) models, our proposed model framework outperforms in the LSA. The area under the receiver operating characteristic curve (AUC) values improved by 6.2%, 5.7%, and 1.8%, respectively. Specifically, our model can predict landslides across space in areas with few samples. Our proposed model offers an effective approach and essential technical support for landslide disaster investigation and prevention.

Exploring Reddit Community Structure: Bridges, Gateways and Highways

Multiple research directions have been proposed to study the information structure of Reddit. One of them is to model inter-subreddit relations but modeling user interactions in the form of a graph. Building upon prior work centered on political subreddits using pre-2020 data, we expand this investigation to include a more extensive dataset spanning 2022 and encompassing diverse topic areas. Employing NLP techniques such as text embeddings, we model subreddit content directly and construct a subreddit graph network based on cosine similarity. Community detection using the Louvain method reveals distinct subreddits and allows the analysis of inter-community connections via previous works’ concepts of “bridges” and “gateways”. Surprisingly, our findings indicate redundancy between bridges and gateways in the utilized dataset. Therefore, we introduce a new concept, “highways”. Highways, representing the most traversed paths between subreddits, unveil insights not captured by previous analyses, underscoring the significance of novel conceptual frameworks in uncovering latent knowledge within Reddit’s online community structures.

Enhancing Event Exploration and Engagement: A Social Events Networking Platform Leveraging Cosine Similarity Recommendations and Google Maps Integration

This project introduces a Social Events Networking Platform, strategically engineered to streamline event exploration and engagement through the effective utilization of user preferences. With a comprehensive range of functionalities, users can seamlessly navigate and participate in diverse events, while event planners gain the ability to create and manage their own events. At its core, the platform employs cosine similarity recommendation algorithms to analyze user preferences, providing personalized event suggestions tailored to individual likes and interests. This approach significantly heightens the probability of user attendance and interaction. In tandem with the recommendation feature, the platform seamlessly integrates Google Maps API, offering a map functionality that enables users to visualize event locations alongside pertinent details. This feature empowers users to efficiently plan their attendance by considering factors such as proximity and accessibility. The overarching goal is to cultivate a vibrant, interactive community by connecting event enthusiasts with organizers. The platform's reliance on Firebase Cloud Function APIs for CRUD operations, such as post creation, reading, updating, and deletion, ensures robust support. Additionally, the recommendation function, based on cosine similarity, is deployed as an API to Firebase Cloud Function. These APIs empower administrators to perform CRUD operations on events, while users can access events based on location and receive recommendations rooted in their favorite events. By seamlessly integrating event recommendations and location-based information, the platform elevates the overall event discovery and participation experience, rendering it more engaging and personalized for users.

A Construction Method for a Dynamic Weighted Protein Network Using Multi-Level Embedding

The rapid development of high-throughput technology has generated a large amount of protein–protein interaction (PPI) data, which provide a large amount of data support for constructing dynamic protein–protein interaction networks (PPINs). Constructing dynamic PPINs and applying them to recognize protein complexes has become a hot research topic. Most existing methods for complex recognition cannot fully mine the information of PPINs. To address this problem, we propose a construction method of dynamic weighted protein network by multi-level embedding (DWPNMLE). It can reflect the protein network’s dynamics and the protein network’s higher-order proximity. Firstly, the protein active period is calculated to divide the protein subnetworks at different time points. Then, the connection probability is used for the proteins possessing the same time points to judge whether there is an interaction relationship between them. Then, the corresponding protein subnetworks (multiple adjacency matrices) are constructed. Secondly, the multiple feature matrices are constructed using one-hot coding with the gene ontology (GO) information. Next, the first embedding is performed using variational graph auto-encoders (VGAEs) to aggregate features efficiently, followed by the second embedding using deep attributed network embedding (DANE) to strengthen the node representations learned in the first embedding and to maintain the first-order and higher-order proximity of the original network; finally, we compute the cosine similarity to obtain the final dynamic weighted PPIN. To evaluate the effectiveness of DWPNMLE, we apply four classical protein-complex-recognition algorithms on the DWPNMLE and compare them with two other dynamic protein network construction methods. The experimental results demonstrate that DWPNMLE significantly enhances the accuracy of complex recognition with high robustness, and the algorithms’ efficiency is also within a reasonable range.

Interval-Valued Intuitionistic Fuzzy Cosine Similarity Measures for Real World Problem Solving

Similarity measures (SMs) are fundamental in various applications, including identifying patterns within medical data and aiding pattern recognition (PR) by quantifying the likeness between different patterns. Moreover, they play a crucial role in real-world problems such as Multiple Criteria Decision Making (MCDM), where decision-makers assess and compare alternatives based on multiple criteria simultaneously. Moreover, Cosine similarity is a measurement that quantifies the similarity between two or more objects. This study presents a comprehensive exploration of Interval-Valued Intuitionistic Fuzzy Cosine Similarity Measures (IV IF CSMs) as a novel technique for assessing the degree of association between objects in realworld applications. By extending traditional cosine similarity measures (CSM) to interval-valued intuitionistic fuzzy sets (IV IFS), the proposed IV IF CSMs offer an effective framework for handling uncertainty, ambiguity, and imprecision in decision-making processes. The research demonstrates the practical utility of IV IF CSMs in addressing complex issues in PR, medical diagnosis (MD), and MCDM. In contrast to established methods like Singh’s, Xu’s, and Luo’s measures, our approach consistently generates higher similarity values, encompassing both membership (MF) and non-membership (NMF) with interval values.

Time dimension feature extraction and classification of high-dimensional large data streams based on unsupervised learning

In order to solve the problem of low accuracy of time dimension feature extraction and classification of high-dimensional large data streams, this paper proposes a time dimension feature extraction and classification algorithm of high-dimensional large data streams based on unsupervised learning. Analyze the trend of high-dimensional data flow changes under machine learning, and achieve dimensionality reduction of high-dimensional large traffic time dimensional data through local save projection. Analyze the spatial relationship between feature attributes and feature space, segment and fit high-dimensional big data streams and time dimensional feature data streams, further segment time dimensional sequences using sliding windows, and complete feature extraction through discrete dyadic wavelet transform. According to the clustering algorithm, cluster the time dimension feature data stream, calculate the cosine similarity of the feature data, model the time dimension feature stream of training samples, use the feature classification function to minimize the classification loss, and use unsupervised learning to achieve the final classification task. The test results show that this method can improve the temporal feature extraction and classification accuracy streams.

Light-Adaptive Human Body Key Point Detection Algorithm Based on Multi-Source Information Fusion.

The identification of key points in the human body is vital for sports rehabilitation, medical diagnosis, human-computer interaction, and related fields. Currently, depth cameras provide more precise depth information on these crucial points. However, human motion can lead to variations in the positions of these key points. While the Mediapipe algorithm demonstrates effective anti-shake capabilities for these points, its accuracy can be easily affected by changes in lighting conditions. To address these challenges, this study proposes an illumination-adaptive algorithm for detecting human key points through the fusion of multi-source information. By integrating key point data from the depth camera and Mediapipe, an illumination change model is established to simulate environmental lighting variations. Subsequently, the fitting function of the relationship between lighting conditions and adaptive weights is solved to achieve lighting adaptation for human key point detection. Experimental verification and similarity analysis with benchmark data yielded R2 results of 0.96 and 0.93, and cosine similarity results of 0.92 and 0.90. With a threshold range of 8, the joint accuracy rates for the two rehabilitation actions were found to be 89% and 88%. The experimental results demonstrate the stability of the proposed method in detecting key points in the human body under changing illumination conditions, its anti-shake ability for human movement, and its high detection accuracy. This method shows promise for applications in human-computer interaction, sports rehabilitation, and virtual reality.

Enhanced Freelance Matching: Integrated Data Analysis and Machine Learning Techniques

The objective of this research is to devise a personalized recommendation system for a freelancing platform to optimize the freelancer project matching process. This enhancement is intended to improve user experience and increase the success rate of projects. The system will recommend projects to freelancers based on their skills and preferences by employing data analysis and machine learning methodologies. The research methodology adheres to the Cross Industry Standard Process for Data Mining (CRISP-DM), incorporating six stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The proposed project employs a hybrid recommendation strategy, integrating Content-Based Filtering through KNearest Neighbors (K-NN) and Cosine Similarity, Collaborative Filtering via Singular Value Decomposition (SVD), and recommendations derived from Word2vec. Evaluation metrics such as precision, recall, F1 score, MAP, and MRR are utilized to assess model performance. The results, including precision scores of 0.80 for KNN and 0.728 for SVD, recall scores of 0.60 for KNN and 0.623 for SVD, and F1 scores of 0.69 for KNN and 0.671 for SVD, as well as a MAP of 0.75 and MRR of 0.80 for Word2vec, demonstrate the efficacy of the hybrid recommendation system in delivering accurate and varied project suggestions to freelancers, with a weighted average ensemble learning model emerging as the most effective solution.

Study on the pricing and replenishment of vegetable commodities based on the optimization model

Based on the problems of short shelf life, inventory control and pricing management, marketing, economics, operation research, and the control optimal theory, this paper establishes the regression prediction analysis model; establish the optimization model, formulate the total amount of daily replenishment; analyze the deficiencies of the model. Finally, the combination of the research results gives the replenishment and pricing decision to improve the revenue of the supermarket. In this study, the correlation degree between the two variables of each vegetable category and the single product sales volume was measured, and the time series analysis was used to identify the sales trend. Then according to the correlation analysis of descriptive statistics, using cosine similarity formula, analyze the data units, the total sales dimension normalization, using the method of standardize the correlation between item and category, using pearson correlation coefficient and Spearman correlation coefficient to study the relationship between category and sales, establish Spearman correlation coefficient to determine the final correlation coefficient, cluster analysis of each category, analysis of the correlation. By processing the relevant data, establish the ARIMA time series model, forecast on July 1,2023-July 7, the relevant data, using the adftest function time series stability test, fit the ARIMA time series model, using differential sequence to time series analysis, calculate vegetable sales and sales price of elastic demand function, finally find the optimal solution through the particle swarm algorithm.

Modern Approach in Pattern Recognition Using Circular Fermatean Fuzzy Similarity Measure for Decision Making with Practical Applications

The circular Fermatean fuzzy (CFF) set is an advancement of the Fermatean fuzzy (FF) set and the interval-valued Fermatean fuzzy (IVFF) set which deals with uncertainty. The CFF set is represented as a circle of radius ranging from 0 to 2 with the center at the degree of association (DA) and degree of nonassociation (DNA). If multiple people are involved in making decisions, the CFF set, as an alternative to the FF and IVFF sets, can deal with ambiguity more effectively by encircling the decision values within a circle rather than taking an average. Using algorithms, a pattern can be observed computationally or visually. Machine learning algorithm utilizes pattern recognition as an instrument for identifying patterns and also similarity measure (SM) is a beneficial pattern recognition tool used to classify items, discover variations, and make future predictions for decision making. In this work, we introduce the CFF cosine and Dice similarity measures (CFFDMs and CFFSMs), and their properties are studied. Unlike traditional approaches of decision making, which emphasize a single number, the proposed CFFSMs observe the pattern over the circular region to help in dealing with uncertainty more effectively. We introduce an innovative decision-making method in the FF setting. Available bank loans and applicants’ eligibility levels are represented as CFF set using their FF criteria and are taken as loan patterns and customer eligibility patterns. The loan is allocated to the applicant by measuring the CFFCSM and CFFDSM between the two patterns. Also, laptops are suggested to the customers by measuring the similarity between specification pattern and requirement pattern. The correctness and consistency of the proposed models are ensured by comparison analysis and graphical simulations of the input and similarity CFFNs.

Intelligent Packet Priority Module for a Network of Unmanned Aerial Vehicles Using Manhattan Long Short-Term Memory

Unmanned aerial vehicles (UAVs) are becoming more common in wireless communication networks. Using UAVs can lead to network problems. An issue arises when the UAVs function in a network-access-limited environment with nodes causing interference. This issue could potentially hinder UAV network connectivity. This paper introduces an intelligent packet priority module (IPPM) to minimize network latency. This study analyzed Network Simulator–3 (NS-3) network modules utilizing Manhattan long short-term memory (MaLSTM) for packet classification of critical UAV, ground control station (GCS), or interfering nodes. To minimize network latency and packet delivery ratio (PDR) issues caused by interfering nodes, packets from prioritized nodes are transmitted first. Simulation results and evaluation show that our proposed intelligent packet priority module (IPPM) method outperformed previous approaches. The proposed IPPM based on MaLSTM implementation for the priority packet module led to a lower network delay and a higher packet delivery ratio. The performance of the IPPM averaged 62.2 ms network delay and 0.97 packet delivery ratio (PDR). The MaLSTM peaked at 97.5% accuracy. Upon further evaluation, the stability of LSTM Siamese models was observed to be consistent across diverse similarity functions, including cosine and Euclidean distances.

CosUKG: A Representation Learning Framework for Uncertain Knowledge Graphs

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 relationship facts. The existing research lacks effective representation of such uncertain information. In this regard, we propose a novel representation learning framework called CosUKG, which is specifically designed for uncertain knowledge graphs. This framework models uncertain information by measuring the cosine similarity between transformed vectors and actual target vectors, effectively integrating uncertainty into the embedding process of the knowledge graph while preserving its structural information. Through multiple experiments on three public datasets, the superiority of the CosUKG framework in representing uncertain knowledge graphs is demonstrated. It achieves improved representation accuracy of uncertain information without increasing model complexity or weakening structural information.