Knowledge and Information Systems

Aspect-based sentiment analysis (ABSA) is a natural language processing technique that seeks to recognize and extract the sentiment connected to various qualities or aspects of a specific good, service, or entity. It entails dissecting a text into ...

Sentiment Summarization is an automated technology that extracts important features of sentences and then reorganizes selected words or sentences by their aspect class and sentiment polarity. This emerging research area wields considerable ...

Knowledge Discovery and Management (KDM) encompasses a comprehensive process and approach involving the creation, discovery, capture, organization, refinement, presentation, and provision of data, information, and knowledge with a specific goal in ...

Extracting named entities text forms the basis for many crucial tasks such as information retrieval and extraction, machine translation, opinion mining, sentiment analysis and question answering. This paper presents a survey of the research ...

For cardinality estimation in DBMS, building multiple local models instead of one global model can usually improve estimation accuracy as well as reducing the effort to label large amounts of training data. Unfortunately, the existing approach of ...

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 ...

Bankruptcy prediction is considered one of the most important research topics in the field of finance and accounting. The rapid increase of data science, artificial intelligence, and machine learning has led researchers to build an accurate ...

The formulation of analytical queries over Knowledge Graphs in RDF is a challenging task that presupposes familiarity with the syntax of the corresponding query languages and the contents of the graph. To alleviate this problem, we introduce a ...

Trial excessive duration is a common problem in Juridical systems worldwide, even if some countries seem to be more affected by it than others. The European Council has provided metrics and statistics to identify this problem and has pointed out ...

This paper introduces a new dissimilarity measure between two discrete and finite probability distributions. The followed approach is grounded jointly on mixtures of probability distributions and an optimization procedure. We discuss the clear ...

Class imbalance has been widely accepted as a significant factor that negatively impacts a machine learning classifier’s performance. One of the techniques to avoid this problem is to balance the data distribution by using sampling-based ...

The event sequences with spatiotemporal characteristics have been rapidly produced in various domains, such as earthquakes in seismology, electronic medical records in healthcare, and transactions in the financial market. These data often continue ...

Local rough sets are an efficient model to analyze large-scale datasets with finite labels because they are an essential development in classical rough sets. The objective of this paper, we put forth the idea of a local soft rough approximation ...

Location-based services are gaining popularity; prediction of future destinations and crowd movement patterns are crucial components of these services. This article presents an attention-based neural network regression model designed to forecast ...

Few-shot knowledge graph completion (FKGC) refers to the task of inferring missing facts in a knowledge graph by utilizing a limited number of reference entities. Most FKGC methods assume a single similarity metric, which leads to a single feature ...

Identification of the key influencers is one of the most important strategies for initiating any transmission process in a social network. However, many of the current studies on influence transmission concentrate primarily on the progressive ...

Applying deep learning, especially time series neural networks, to predict stock price, has become one of the important applications in quantitative finance. Recently, some GAN-based stock prediction models are proposed, where LSTM or GRU is used ...

In the cloud system, service providers supply a pool of resources in the form of a web service and the services are merged to provide the required composite services. Composing a quality-of-service aware web service is like the knapsack problem ...

User identity linkage (UIL) aims to link identities belonging to the same individual across various platforms. While numerous methods have been proposed for paired or multiple platforms, UIL is still a non-trivial task due to the following ...

Multi-Attribute Similarity Join represents an important task for a variety of applications. Due to a large amount of data, several techniques and approaches were proposed to avoid superfluous comparisons between entities. One of these techniques ...

In recent years, neural network search has been utilized in designing effective heterogeneous graph neural networks (HGNN) and has achieved remarkable performance beyond manually designed networks. Generally, there are two mainstream design ...

Recommendation system is used to filter the information according to the customer’s satisfaction. Based on consumer reviews, this approach discovers and compares product scores, ratings, and rankings. Here, the data are obtained from Amazon ...

Sports analytics (SA) incorporate machine learning (ML) techniques and models for performance prediction. Researchers have previously evaluated ML models applied on a variety of basketball statistics. This paper aims to benchmark the forecasting ...