Web information systems and applications : 19th International Conference, WISA 2022, Dalian, China, September 16-18, 2022 : proceedings / Xiang Zhao, Shiyu Yang, Xin Wang, Jianxin Li (eds.).
Material type:
TextSeries: Lecture notes in computer science ; 13579.Publisher: Cham : Springer, [2022]Copyright date: ©2022Description: 1 online resource (xviii, 743 pages) : illustrations (chiefly color)Content type: - text
- computer
- online resource
- 9783031203091
- 3031203097
- WISA 2022
- 004.67/8 23/eng/20230103
- TK5105.888
| Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds | |
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eBook
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e-Library | eBook LNCS | Available |
Selected conference papers.
Includes author index.
This book constitutes the proceedings of the 19th International Conference on Web Information Systems and Applications, WISA 2022, held in Dalian, China, in September 2022. The 45 full papers and 19 short papers presented were carefully reviewed and selected from 212 submissions. The papers are grouped in topical sections on knowledge graph, natural language processing, world wide web, machine learning, query processing and algorithm, recommendation, data privacy and security, and blockchain.
Online resource; title from PDF title page (SpringerLink, viewed January 3, 2023).
Intro -- Preface -- Organization -- Contents -- Knowledge Graph -- Temporal Knowledge Graph Embedding for Link Prediction -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 Methodology -- 4.1 Structural Self-attention -- 4.2 Temporal Self-attention -- 4.3 Parameter Learning -- 4.4 Discussion -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Performance Comparison (RQ1) -- 5.3 Utility of Structural and Temporal Self-attention (RQ2) -- 5.4 Hyper-Parameter Studies (RQ3) -- 6 Conclusions -- References -- A Multi-modal Knowledge Graph Platform Based on Medical Data Lake
1 Introduction -- 2 Related Work -- 3 Architecture of MMKGP -- 4 Translation-Based Model Enhanced by Prior Knowledge -- 4.1 TransE Model -- 4.2 Constraint for Relations -- 5 Knowledge Graph Completion with Multi-modal Data -- 5.1 Dataset -- 5.2 Evaluation Criterion -- 5.3 Model Training and Result -- 6 Knowledge Graph-Based Clinical Decision Support System -- 6.1 Link Prediction & Correction -- 6.2 Recommendation and Q&A System -- 7 Conclusion -- References -- Fusion of Natural Language and Knowledge Graph for Multi-hop Reasoning -- 1 Introduction -- 2 Related Works -- 3 Model -- 3.1 Description
3.2 Framework -- 3.3 Subgraph Retrieval -- 3.4 Structural Fusion -- 3.5 Relation Reasoning -- 4 Experiments -- 4.1 Experiment Setup -- 4.2 Model Comparison -- 5 Conclusion -- References -- Commonsense Knowledge Construction with Concept and Pretrained Model -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Framework of CG&BF -- 3.2 Concept-Based Generator -- 3.3 BERT-Based Filter -- 4 Experiments -- 4.1 Experiment Setup -- 4.2 Model Comparison -- 5 Conclusion -- References -- Simplifying Knowledge-Aware Aggregation for Knowledge Graph Collaborative Filtering -- 1 Introduction
2 Related Work -- 3 Task Formulation -- 4 Methodology -- 4.1 Personalized Knowledge Aggregation -- 4.2 User Aggregation -- 4.3 Prediction Layer -- 5 Experiments -- 5.1 Datasets -- 5.2 Baselines -- 5.3 Experimental Settings -- 5.4 Performance Comparison (RQ1) -- 5.5 Ablation Studies (RQ2) -- 6 Conclusion -- References -- Bi-Directional Neighborhood-Aware Network for Entity Alignment in Knowledge Graphs -- 1 Introduction -- 2 Related Work -- 2.1 Embedding-Based Methods -- 2.2 Phenomenon of Long-Tail -- 3 Problem Formalization -- 4 Methodology -- 4.1 Neighborhood Embedding
4.2 Entity Name Embedding -- 4.3 Feature Fusion with Bi-attention -- 4.4 Alignment and Training -- 5 Experiments -- 5.1 Experiment Setting -- 5.2 Main Result -- 5.3 Ablation Study -- 5.4 Evaluation by Degrees Interval -- 5.5 Robustness on Datasets -- 6 Conclusion -- References -- SAREM: Semi-supervised Active Heterogeneous Entity Matching Framework -- 1 Introduction -- 2 Related Work -- 2.1 Entity Matching -- 2.2 EM Based on Active Learning -- 3 Problem Statement and Definition -- 4 The Framework: Sarem -- 4.1 Data Augmentation -- 4.2 Feature Extraction -- 4.3 Example Selection