The Semantic Web - ISWC 2021

This book constitutes the proceedings of the 20th International Semantic Web Conference, ISWC 2021, which took place in October 2021. Due to COVID-19 pandemic the conference was held virtually. The papers included in this volume deal with the latest advances in fundamental research, innovative technology, and applications of the Semantic Web, linked data, knowledge graphs, and knowledge processing on the Web. Papers are organized in a research track, resources and in-use track. The research track details theoretical, analytical and empirical aspects of the Semantic Web and its intersection with other disciplines. The resources track promotes the sharing of resources which support, enable or utilize semantic web research, including datasets, ontologies, software, and benchmarks. And finally, the in-use-track is dedicated to novel and significant research contributions addressing theoretical, analytical and empirical aspects of the Semantic Web and its intersection with other disciplines.

PCSG: Pattern-Coverage Snippet Generation for RDF Datasets.- A Source-to-Target Constraint rewriting for Direct Mapping.- Learning to Predict the Departure Dynamics of Wikidata Editors.- owards Neural Schema Alignment for OpenStreetMap and Knowledge Graphs.- Improving Inductive Link Prediction Using Hyper-Relational Facts.- Large-scale Multi-granular Concept Extraction Based on Machine Reading Comprehension.- Multi-Hop Scene Graph Reasoning for Visual Question Answering.- EDG-based Question Decomposition for Complex Question Answering over Knowledge Bases.- Zero-shot Visual Question Answering using Knowledge Graph.- Zero-shot Visual Question Answering using Knowledge Graph.- Learning to Recommend Items to Wikidata Editors.- Graph-boosted Active Learning for Multi-Source Entity Resolution.- Computing CQ lower-bounds over OWL 2 through approximation to RSA.- Rank for Large Knowledge Databases.- Automatically Extracting OWL Versions of FOL Ontologies.- Using Compositional Embeddings for Fact Checking.- Background Knowledge in Schema Matching: Strategy vs. Data.