Knowledge and Information Systems最新文献

The goal of aspect-based sentiment analysis is to recognize the aspect information in the text and the corresponding sentiment polarity. A variety of robust methods, including attention mechanisms and convolutional neural networks, have been extensively utilized to tackle this complex task. Better experimental results are obtained by using graph convolutional networks (GCN) based on semantic dependency trees in previous studies. Therefore, abundant methods begin to use sentence structure information to complete this task. However, only the loose connection between aspect words and contexts is realized in some practices due to sentences may contain complex relations. To solve this problem, Twain-Syntax graph convolutional network model is proposed, which can utilize multiple syntactic structure information simultaneously. Guided by the constituent tree and dependency tree, rich syntactic information is fully used in the model to build the sentiment-aware context for each aspect. In special, the multilayer attention mechanism and GCN are employed for learning to capture the correlation between words. By integrating syntactic information, this approach significantly refines the model’s technical performance. Extensive testing on four benchmark datasets shows that the model delineated in this paper exhibits high levels of efficiency, comparable to several cutting-edge models.

Convolutional neural networks (CNNs) have demonstrated impressive performance in fitting data distribution. However, due to the complexity in learning intricate features from data, networks usually experience overfitting during the training. To address this issue, many data augmentation techniques have been proposed to expand the representation of the training data, thereby improving the generalization ability of CNNs. Inspired by jigsaw puzzles, we propose PatchMix, a novel mixup-based augmentation method that applies mixup to patches within an image to extract abundant and varied information from it. At the input level of CNNs, PatchMix can generate a multitude of reliable training samples through an integrated and controllable approach that encompasses cropping, combining, blurring, and more. Additionally, we propose PatchMix-R to enhance the robustness of the model against perturbations by processing adjacent pixels. Easy to implement, our methods can be integrated with most CNN-based classification models and combined with varying data augmentation techniques. The experiments show that PatchMix and PatchMix-R consistently outperform other state-of-the-art methods in terms of accuracy and robustness. Class activation mappings of the trained model are also investigated to visualize the effectiveness of our approach.

The knowledge graph emerges as powerful data structures that provide a deep representation and understanding of the knowledge presented in networks. In the pursuit of representation learning of the knowledge graph, entities and relationships undergo an embedding process, where they are mapped onto a vector space with reduced dimensions. These embeddings are progressively used to extract their information for a multitude of tasks in machine learning. Nevertheless, the increase data in knowledge graph has introduced a challenge, especially as knowledge graph embedding now encompass millions of nodes and billions of edges, surpassing the capacities of existing knowledge representation learning systems. In response to these challenge, this paper presents DistKGE, a distributed learning approach of knowledge graph embedding based on a new partitioning technique. In our experimental evaluation, we illustrate that the proposed approach improves the scalability of distributed knowledge graph learning with respect to graph size compared to existing methods in terms of runtime performances in the link prediction task aimed at identifying new links between entities within the knowledge graph.

Online social networks (OSNs) are an indispensable part of social communication where people connect and share information. Spammers and other malicious actors use the OSN’s power to propagate spam content. In an OSN with mutual relations between nodes, two kinds of spammer detection methods can be employed: feature based and propagation based. However, both of these are incomplete in themselves. The feature-based methods cannot exploit mutual connections between nodes, and propagation-based methods cannot utilize the rich discriminating node features. We propose a hybrid model—Markov enhanced graph attention network (MEGAT)—using graph attention networks (GAT) and pairwise Markov random fields (pMRF) for the spammer detection task. It efficiently utilizes node features as well as propagation information. We experiment our GAT model with a smoother Swish activation function having non-monotonic derivatives, instead of the leakyReLU function. The experiments performed on a real-world Twitter Social Honeypot (TwitterSH) benchmark dataset and subsequent comparative analysis reveal that our proposed MEGAT model outperforms the state-of-the-art models in accuracy, precision–recall area under curve (PRAUC), and F1-score performance measures.

Distributional estimates in Bayesian approaches in structure learning have advantages compared to the ones performing point estimates when handling epistemic uncertainty. Differentiable methods for Bayesian structure learning have been developed to enhance the scalability of the inference process and are achieving optimistic outcomes. However, in the differentiable continuous setting, constraining the acyclicity of learned graphs emerges as another challenge. Various works utilize post-hoc penalization scores to impose this constraint which cannot assure acyclicity. The topological ordering of the variables is one type of prior knowledge that contains valuable information about the acyclicity of a directed graph. In this work, we propose a framework to guarantee the acyclicity of inferred graphs by integrating the information from the topological ordering into the inference process. Our integration framework does not interfere with the differentiable inference process while being able to strictly assure the acyclicity of learned graphs and reduce the inference complexity. Our extensive empirical experiments on both synthetic and real data have demonstrated the effectiveness of our approach with preferable results compared to related Bayesian approaches.

Multi-view learning consistently outperforms traditional single-view learning by leveraging multiple perspectives of data. However, the effectiveness of multi-view learning heavily relies on how the data are partitioned into feature sets. In many cases, different datasets may require different partitioning methods to capture their unique characteristics, making a single partitioning method insufficient. Finding an optimal feature set partitioning (FSP) for each dataset may be a time-consuming process, and the optimal FSP may still not be sufficient for all types of datasets. Therefore, the paper presents a novel approach called ensemble multi-view feature set partitioning (EMvFSP) to improve the performance of multi-view learning, a technique that uses multiple data sources to make predictions. The proposed EMvFSP method combines the different views produced by multiple partitioning methods to achieve better classification performance than any single partitioning method alone. The experiments were conducted on 15 structured datasets with varying ratios of samples, features, and labels, and the results showed that the proposed EMvFSP method effectively improved classification performance. The paper also includes statistical analyses using Friedman ranking and Holms procedure to demonstrate the effectiveness of the proposed method. This approach provides a robust solution for multi-view learning that can adapt to different types of datasets and partitioning methods, making it suitable for a wide range of applications.

Personalized search plays an important role in satisfying users’ information needs owing to its ability to build user profiles based on users’ search histories. Most of the existing personalized methods built dynamic user profiles by emphasizing query-related historical behaviors rather than treating each historical behavior equally. Sometimes, the ambiguity and short nature of the query make it difficult to understand the potential query intent exactly, and the query-centric user profiles built in these cases will be biased and inaccurate. In this work, we propose to leverage candidate documents, which contain richer information than the short query text, to help understand the query intent more accurately and improve the quality of user profiles afterward. Specifically, we intend to better understand the query intent through candidate documents, so that more relevant user behaviors from history can be selected to build more accurate user profiles. Moreover, by analyzing the differences between candidate documents, we can better control the degree of personalization on the ranking of results. This controlled personalization approach is also expected to further improve the stability of personalized search as blind personalization may harm the ranking results. We conduct extensive experiments on two datasets, and the results show that our model significantly outperforms competitive baselines, which confirms the benefit of utilizing candidate documents for personalized web search.

One of the challenges practitioners face when applying structure learning algorithms to their data involves determining a set of hyperparameters; otherwise, a set of hyperparameter defaults is assumed. The optimal hyperparameter configuration often depends on multiple factors, including the size and density of the usually unknown underlying true graph, the sample size of the input data, and the structure learning algorithm. We propose a novel hyperparameter tuning method, called the Out-of-sample Tuning for Structure Learning (OTSL), that employs out-of-sample and resampling strategies to estimate the optimal hyperparameter configuration for structure learning, given the input dataset and structure learning algorithm. Synthetic experiments show that employing OTSL to tune the hyperparameters of hybrid and score-based structure learning algorithms leads to improvements in graphical accuracy compared to the state-of-the-art. We also illustrate the applicability of this approach to real datasets from different disciplines.

Road safety remains a critical issue in contemporary society, where the sudden deterioration of road conditions due to weather-related natural phenomena poses significant risks. These abrupt changes can lead to severe safety hazards on the roads, making real-time monitoring and control essential for maintaining road safety. In this context, technological advancements, especially in sensor networks and intelligent systems, play a fundamental role in efficiently managing these challenges. This study introduces an innovative approach that leverages a sophisticated sensor platform coupled with a multi-agent system. This integration facilitates the collection, processing, and analysis of data to preemptively determine the appropriate chemical treatments for roads during severe winter conditions. By employing advanced data analysis and machine learning techniques within a multi-agent framework, the system can predict and respond to adverse weather effects swiftly and with a high degree of accuracy. The proposed system has undergone rigorous testing in a real-world environment, which has verified its operational effectiveness. The results from the deployment of the multi-agent architecture and its predictive capabilities are encouraging, suggesting that this approach could significantly enhance road safety in extreme weather conditions. Furthermore, the proposed architecture allows the system to evolve and scale over time. This paper details the design and implementation of the system, discusses the results of its field tests, and explores potential improvements.

To address the problem of sparse data and cold-start when facing new users and items in the single-domain recommendation, cross-domain recommendation has gradually become a hot topic in the recommendation system. This method enhances target domain recommendation performance by incorporating relevant information from an auxiliary domain. A critical aspect of cross-domain recommendation is the effective transfer of user preferences from the source to the target domain. This paper proposes a novel cross-domain recommendation framework, namely the Cross-domain Recommendation based on Aspect-level Sentiment extraction (CRAS). CRAS leverages user and item review texts in cross-domain recommendations to extract detailed user preferences. Specifically, the Biterm Topic Model (BTM) is utilized for the precise extraction of ’aspects’ from users and items, focusing on identifying characteristics that align with user interests and the positive attributes of items. These ’aspects’ represent distinct, influential features of the items. For example, a good service attitude can be regarded as a good aspect of a restaurant. Furthermore, this study employs an improved Cycle-Consistent Generative Adversarial Networks (CycleGAN), efficiently mapping user preferences from one domain to another, thereby enhancing the accuracy and personalization of the recommendations. Lastly, this paper compares the CRAS model with a series of state-of-the-art baseline methods in the Amazon review dataset, and experiment results show that the proposed model outperforms the baseline methods.