Knowledge Graphs and Semantic Web (eBook, PDF)

Second Iberoamerican Conference and First Indo-American Conference, KGSWC 2020, Mérida, Mexico, November 26-27, 2020, Proceedings
Redaktion: Villazón-Terrazas, Boris; Shandilya, Shishir K.; Tiwari, Sanju M.; Ortiz-Rodríguez, Fernando

• Format: PDF

Produktbeschreibung

This book constitutes the thoroughly refereed proceedings of the Second Iberoamerican Conference, KGSWC 2020, held in Mérida, Mexico, in November 2020. Due to the COVID-19 pandemic the conference was held online.

The 15 papers presented were carefully reviewed and selected from 45 submissions. The papers cover research and practices in several fields of AI, such as knowledge representation and reasoning, natural language processing/text mining, machine/deep learning, semantic web, and knowledge graphs.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 215
• Erscheinungstermin: 9. Dezember 2020
• Englisch
• ISBN-13: 9783030653842
• Artikelnr.: 62293351

Inhaltsangabe

A Domain Ontology for Task Instructions.- Axiomatic relation extraction from text in the domain of Tourism.- Making Neural Networks FAIR.- Creating Annotations for Web Ontology Language Ontology Generated From Relational Databases.- Malware Detection using Machine Learning.- Wikipedia Knowledge Graph for Explainable AI.- Characterizing the Diffusion of Knowledge in an Academic Community Through the Integration of Heterogeneous Data Sources and Graphs.- Relation Classification: How well do Neural Network Approaches Work?.- Standards Conformance Metrics for Geospatial Linked Data.- An Ontological Model for the Failure Detection in Power Electric Systems.- A Spatiotemporal Knowledge Bank from Rape News Articles for Decision Support.- Exploring Sequence-to-Sequence Models for SPARQL Pattern Composition.- Using Domain Ontologies for Text Classification. A Use Case to Classify Computer Science Papers.- Description of OpenData Sets as Semantic Knowledge Graphs to contribute to actions related to the 2030 Agenda and the Sustainable Development Goals.- A Machine Learning Method for Recognizing Invasive Content in Memes.