Web Information Systems Engineering - WISE 2024

This five-volume set LNCS 15436 -15440 constitutes the proceedings of the 25th International Conference on Web Information Systems Engineering, WISE 2024, held in Doha, Qatar, in December 2024.

The 110 full papers and 55 short papers were presented in these proceedings were carefully reviewed and selected from 368 submissions. The papers have been organized in the following topical sections as follows:

Part I : Information Retrieval and Text Processing; Text and Sentiment Analysis; Data Analysis and Optimisation; Query Processing and Information Extraction; Knowledge and Data Management.

Part II: Social Media and News Analysis; Graph Machine Learning on Web and Social; Trustworthy Machine Learning; and Graph Data Management.

Part III: Recommendation Systems; Web Systems and Architectures; and Humans and Web Security.

Part IV: Learning and Optimization; Large Language Models and their Applications; and AI Applications.

Part V: Security, Privacy and Trust; Online Safety and Wellbeing through AI; and Web Technologies.a

Contenu

.- Information Retrieval and Text Processing.

.- Learning Contrastive Representations for Dense Passage Retrieval in Open-domain Conversational Question Answering.

.- Wikipedia Empowered Natural Language Interface for Web Search.

.- Mathematical Formulas-Based Scientific Literature Retrieval with Heterogeneous Network Semantic Enhancement.

.- MarkedDPR: Enhancing Dense Passage Retrieval with Exact Match Signals and Synthetic Data Augmentation.

.- Text and Sentiment Analysis.

.- Explaining a Deep Learning Model for Aspect-Based Sentiment Analysis Using SHAP.

.- Knowledge Injection from a Domain Sentiment Ontology in an Attention Neural Network for Aspect-Based Sentiment Classification.

.- Multilingual, Cross-lingual, and Unilingual Models for ABSC.

.- From Data to Insights: Constructing and Evaluating a Hospitality Dataset for Quadruple Aspect-Based Sentiment Analysis.

.- A Cross-Lingual Meta-Learning Method Based on Domain Adaptation for Speech Emotion Recognition.

.- Data Analysis and Optimisation.

.- Representation with Minimized Max-error in Optimal Piecewise Linear Approximation of Time Series Data.

.- A Big Data Drilling Method for Value Assessment of Leakage Data.

.- An Effective Tag Assignment Approach for Billboard Advertisement.

.- Early Discovery of Key Innovative Publications by Analyzing Emerging Topic Trends.

.- Two-Stage Trained Stacking Model for Univariate Time Series Forecasting.

.- ITRA: Incremental Task Replanning Algorithm for multi-UAV Based on Centralized-Distributed Negotiation.

.- Query Processing and Information Extraction.

.- RePair My Queries: Personalized Query Reformulation via Conditional Transformers.

.- DORA: A Reliability-Associated Query Optimization Framework for Plan Selection.

.- REXIO: Indexing for Low Write Amplification by Reducing Extra I/Os in Key-Value Store under Mixed Read/Write Workloads.

.- Enhancing Multi-Hop Complex Question Answering with LLM-based Pseudo-Document Generation and Adaptive Retrieval.

.- Enhancing RAG's Retrieval via Query Backtranslations.

.- Hierarchical Indexing for Interactive Zooming of Document Clusters.

.- Distributed k-hop Query Powered by an Asynchronous Framework.

.- Contract Clause Extraction Using Question- Answering Task.

.- Knowledge and Data Management .

.- Data- and Activity-centric Business Process Modeling: An Approach based on Business Units.

.- Periphoscape: Enhance Wikipedia Browsing by Presenting Diverse Aspects of Topics.

.- TERM: Tree Ensemble Models for Interpretable Rule Mining.

.- A high utility co-location pattern mining algorithm using multiple utility thresholds.

.- Talking Buildings: Interactive Human-Building Smart-Bot for Smart Buildings.

.- Concepts and Relations Features Are All You Need for Embedding-based Ontology Matching.

.- An Ontological Approach for Anomaly Detection in Supply Chain-based Business Processes.

.- Incorporating Taxonomic Reasoning and Regulatory Knowledge into Automated Privacy Question Answering.

.- AutoTable: Effective and Efficient Automated Feature Transformation for Tabular Data.