Sepsis is a leading cause of death among intensive care unit patients. Early sepsis prediction, which primarily relies on advanced artificial intelligence technology, is an important phase in sepsis management. In previous sepsis prediction …

Although Knowledge Graphs (KGs) provide great value in many applications, they are often incomplete with many missing facts. KG Completion (KGC) is a popular technique for knowledge supplement. However, there are two fundamental challenges for …

Entity Resolution (ER) is a crucial process in the field of data management and integration. The primary goal of ER is to identify different profiles (or records) that refer to the same real-world entity across databases. The challenging problem …

Recommender systems are indispensable because they influence our day-to-day behavior and decisions by giving us personalized suggestions. Services like Kindle, YouTube, and Netflix depend heavily on the performance of their recommender systems to …

Nowadays, various applications across industries, healthcare, and security have begun adopting automatic sentiment analysis and emotion detection in short texts, such as posts from social media. Twitter stands out as one of the most popular online …

Commonsense Question Answering (CQA) aims to select the correct answers to common knowledge questions. Most existing approaches focus on integrating external knowledge graph (KG) representations with question context representations to facilitate …

Community detection in complex networks is an important task for discovering hidden information in network analysis. Neighborhood density between nodes is one of the fundamental indicators of community presence in the network. A community with a …

Recognizing crucial seed spreaders of complex networks is an open issue that studies the dynamic spreading process and analyzes the performance of networks. However, most of the findings design the hierarchical model based on nodes’ degree such as …

In this paper, we present a methodology for the early detection of fake news on emerging topics through the innovative application of weak supervision. Traditional techniques for fake news detection often rely on fact-checkers or supervised …

The accuracy of price forecasts is important for financial market trading strategies and portfolio management. Compared to traditional models such as ARIMA and other state-of-the-art deep learning techniques, temporal Transformers with similarity …

Producing a high-quality review translation is a multifaceted process. It goes beyond successful semantic transfer and requires conveying the original message’s tone and style in a way that resonates with the target audience, whether they are …

Multi-modal recommendation using multi-modal features (e.g., image and text features) has received significant attention and has been shown to have more effective recommendation. However, there are currently the following problems with multi-modal …

The formulation of structured queries over Knowledge Graphs is not an easy task. To alleviate this problem, we propose a novel interactive method for SPARQL query formulation, for enabling users (plain and advanced) to formulate gradually queries …

Question Answering Over Temporal Knowledge Graphs (TKGQA) is an important topic in question answering. TKGQA focuses on accurately understanding questions involving temporal constraints and retrieving accurate answers from knowledge graphs. In …

Medical question-answering systems require the ability to extract accurate, concise, and comprehensive answers. They will better comprehend the complex text and produce helpful answers if they can reason on the explicit constraints described in …

Detecting deviant traces in business process logs is crucial for modern organizations, given the harmful impact of deviant behaviours (e.g., attacks or faults). However, training a Deviance Prediction Model (DPM) by solely using supervised …

A Knowledge Graph-based Question Answering (KGQA) system attempts to answer a given natural language question using a knowledge graph (KG) rather than from text data. The current KGQA methods attempt to determine whether there is an explicit …

Knowledge Graphs (KGs) have already proven their strength as a source of high-quality information for different tasks such as data integration, search, text summarization, and personalization. Another prominent research field that has been …

This paper analyses Twitter data to detect the political lean of a profile by extracting and classifying sentiments expressed through tweets. The work utilizes natural language processing, augmented with sentiment analysis algorithms and machine …

This paper tackles the critical challenge of detecting and mitigating unintended political bias in offensive meme detection. Political memes are a powerful tool that can be used to influence public opinion and disrupt voters’ mindsets. However …