Yizhou Sun – författare

This book constitutes the proceedings of the 20th International Conference on Web Information Systems Engineering, WISE 2019, held in Hong Kong, China, in November 2019. Due to the problems/protests in Hong Kong, WISE 2019 was postponed from November 26-30, 2019 until January 19-22, 2020. The 50 full papers presented were carefully reviewed and selected from 211 submissions. The papers are organized in the following topical sections:  blockchain and crowdsourcing; machine learning; deep learning; recommender systems, data mining; web-based applications; entity linkage and disambiguation; graph learning; knowledge graphs; graph mining; and text mining.

This book constitutes the proceedings of the 20th International Conference on Web Information Systems Engineering, WISE 2019, held in Hong Kong, China, in November 2019. Due to the problems/protests in Hong Kong, WISE 2019 was postponed from November 26-30, 2019 until January 19-22, 2020. 

The 50 full papers presented were carefully reviewed and selected from 211 submissions. The papers are organized in the following topical sections:  blockchain and crowdsourcing; machine learning; deep learning; recommender systems, data mining; web-based applications; entity linkage and disambiguation; graph learning; knowledge graphs; graph mining; and text mining.

Real-world physical and abstract data objects are interconnected, forming gigantic, interconnected networks. By structuring these data objects and interactions between these objects into multiple types, such networks become semi-structured heterogeneous information networks. Most real-world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks. Therefore, effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge. In this book, we investigate the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, our semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network. This semi-structured heterogeneous network modeling leads to a series of new principles and powerful methodologies for mining interconnected data, including: (1) rank-based clustering and classification; (2) meta-path-based similarity search and mining; (3) relation strength-aware mining, and many other potential developments. This book introduces this new research frontier and points out some promising research directions. Table of Contents: Introduction / Ranking-Based Clustering / Classification of Heterogeneous Information Networks / Meta-Path-Based Similarity Search / Meta-Path-Based Relationship Prediction / Relation Strength-Aware Clustering with Incomplete Attributes / User-Guided Clustering via Meta-Path Selection / Research Frontiers

Real-world physical and abstract data objects are interconnected, forming gigantic, interconnected networks. By structuring these data objects and interactions between these objects into multiple types, such networks become semi-structured heterogeneous information networks. Most real-world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks. Therefore, effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge. In this book, we investigate the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, our semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network. This semi-structured heterogeneous network modeling leads to a series of new principles and powerful methodologies for mining interconnected data, including: (1) rank-based clustering and classification; (2) meta-path-based similarity search and mining; (3) relation strength-aware mining, and many other potential developments. This book introduces this new research frontier and points out some promising research directions. Table of Contents: Introduction / Ranking-Based Clustering / Classification of Heterogeneous Information Networks / Meta-Path-Based Similarity Search / Meta-Path-Based Relationship Prediction / Relation Strength-Aware Clustering with Incomplete Attributes / User-Guided Clustering via Meta-Path Selection / Research Frontiers

This book provides a coherent and unifying view for logic and representation learning to contribute to knowledge graph (KG) reasoning and produce better computational tools for integrating both worlds.  To this end, logic and deep neural network models are studied together as integrated models of computation.  This book is written for readers who are interested in KG reasoning and the new perspective of neuro-symbolic integration and have prior knowledge to neural networks and deep learning.  The authors first provide a preliminary introduction to logic and background knowledge closely related to the surveyed techniques such as the introduction of knowledge graph and ontological schema and the technical foundations of first-order logic learning.  Reasoning techniques for knowledge graph completion are presented from three perspectives, including: representation learning-based, logical, and neuro-symbolic integration.  The book then explores question answering on KGs with specific focus on multi-hop and complex-logic query answering before outlining work that addresses the rule learning problem.  The final chapters highlight foundations on ontological schema and introduce its usage in KG before closing with open research questions and a discussion on the potential directions in the future of the field.

This book provides a coherent and unifying view for logic and representation learning to contribute to knowledge graph (KG) reasoning and produce better computational tools for integrating both worlds.  To this end, logic and deep neural network models are studied together as integrated models of computation.  This book is written for readers who are interested in KG reasoning and the new perspective of neuro-symbolic integration and have prior knowledge to neural networks and deep learning.  The authors first provide a preliminary introduction to logic and background knowledge closely related to the surveyed techniques such as the introduction of knowledge graph and ontological schema and the technical foundations of first-order logic learning.  Reasoning techniques for knowledge graph completion are presented from three perspectives, including: representation learning-based, logical, and neuro-symbolic integration.  The book then explores question answering on KGs with specific focus on multi-hop and complex-logic query answering before outlining work that addresses the rule learning problem.  The final chapters highlight foundations on ontological schema and introduce its usage in KG before closing with open research questions and a discussion on the potential directions in the future of the field.

This book constitutes the refereed proceedings of the 16th International Conference on Web-Age Information Management, WAIM 2015, held in Qingdao, China, in June 2015. The 33 full research papers, 31 short research papers, and 6 demonstrations were carefully reviewed and selected from 164 submissions. The focus of the conference is on following topics: advanced database and web applications, big data analytics big data management, caching and replication, cloud computing, content management, crowdsourcing data and information quality, data management for mobile and pervasive computing, data management on new hardware, data mining, data provenance and workflow, data warehousing and OLAP, deep web, digital libraries, entity resolution and entity linking and graph data management and RDF.

This book constitutes the refereed proceedings of the 16th International Conference on Web-Age Information Management, WAIM 2015, held in Qingdao, China, in June 2015. The 33 full research papers, 31 short research papers, and 6 demonstrations were carefully reviewed and selected from 164 submissions. The focus of the conference is on following topics: advanced database and web applications, big data analytics big data management, caching and replication, cloud computing, content management, crowdsourcing data and information quality, data management for mobile and pervasive computing, data management on new hardware, data mining, data provenance and workflow, data warehousing and OLAP, deep web, digital libraries, entity resolution and entity linking and graph data management and RDF.

This book constitutes the refereed proceedings of the 5th China Conference on Knowledge Graph and Semantic Computing, CCKS 2020, held in Nanchang, China, in November 2020. The 26 revised full papers presented were carefully reviewed and selected from 173 submissions. The papers are organized in topical sections on ​knowledge extraction: lexical and entity; knowledge extraction: relation; knowledge extraction: event; knowledge applications: question answering, dialogue, decision support, and recommendation.