Michalis Mountantonakis – författare

Linked Data is a method of publishing structured data to facilitate sharing, linking, searching and re-use. Many such datasets have already been published, but although their number and size continues to increase, the main objectives of linking and integration have not yet been fully realized, and even seemingly simple tasks, like finding all the available information for an entity, are still challenging.This book, Services for Connecting and Integrating Big Numbers of Linked Datasets, is the 50th volume in the series ‘Studies on the Semantic Web’. The book analyzes the research work done in the area of linked data integration, and focuses on methods that can be used at large scale. It then proposes indexes and algorithms for tackling some of the challenges, such as, methods for performing cross-dataset identity reasoning, finding all the available information for an entity, methods for ordering content-based dataset discovery, and others. The author demonstrates how content-based dataset discovery can be reduced to solving optimization problems, and techniques are proposed for solving these efficiently while taking the contents of the datasets into consideration.To order them in real time, the proposed indexes and algorithms have been implemented in a suite of services called LODsyndesis, in turn enabling the implementation of other high level services, such as techniques for knowledge graph embeddings, and services for data enrichment which can be exploited for machine-learning tasks, and which also improve the prediction of machine-learning problems.

This book constitutes the joint refereed proceedings of the 18th China Conference on Knowledge Graph and Semantic Computing and the 13th International Joint Conference on Knowledge Graphs, CCKS-IJCKG 2024, held in Chongqing, China, during September 20–22, 2024. The 30 full papers and 11 other papers presented in this volume were carefully reviewed and selected from 168 submissions. They are organized in the following topical sections: Knowledge representation and reasoning; Knowledge graph construction and knowledge integration; Graph database and knowledge management; Machine learning on graphs; Knowledge retrieval and information retrieval; Knowledge graph and large language model applications; Knowledge graph open resources; Poster and demo; Evaluations.