Semantic Technology 9th Joint International Conference, JIST 2019, Hangzhou, China, November 25–27, 2019, Revised Selected Papers

This book constitutes the thoroughly refereed proceedings of the 9th Joint International Semantic Technology Conference, JIST 2019, held in Hangzhou, China, in November 2019.

The 12 full papers and 12 short papers presented were carefully reviewed and selected from 70 submissions. The papers present applications of semantic technologies, theoretical results, new algorithms and tools to facilitate the adoption of semantic technologies.  

Building a Large-Scale Knowledge Graph for Elementary Education in China.- A Temporal Semantic Search System for Traditional Chinese Medicine Based on Temporal Knowledge Graphs.- Testing of Various Approaches for Semiautomatic Parish Records Word Standardization.- Concept Similarity under the Agent's Preferences for the Description Logic ALEH.- Data Quality for Deep Learning of Judgment Documents: An Empirical Study.- Aligning Sentences between Comparable Texts of Dierent Styles.- An In-Depth Analysis of Graph Neural Networks for Semi-Supervised Learning.- XTransE: Explainable Knowledge Graph Embedding for Link Prediction with Lifestyles in e-Commerce.- Feasibility Study: Rule Generation for Ontology-based Decision-making Systems.- Attention-based Direct Interaction Model for Knowledge Graph Embedding.- Discovering Hypernym-hyponym Relationship in Chinese Trac Legal Texts.- Multi-task Learning for Attribute Extraction from Unstructured Electronic Medical Records.- Uncertain Ontology-aware Knowledge Graph Embeddings.- Investigating Schema Denitions using RDFS and OWL 2 for RDF Databases in Life Sciences.- RQE: Rule-driven Query Expansion to Solve Empty Answers in SPARQL.- Aspect-level Sentiment Analysis of Online Product Reviews Based on Multi-features.- A Seq2seq-based Approach to Question Answering over Knowledge Bases.- Building Knowledge Graph across Dierent Subdomains using Interlinking Ontology for Biomedical Concepts.- WPQA: A Gaming Support System based on Machine Learning and Knowledge Graph.- Combining Concept Graph with Improved Neural Networks for Chinese Short Text Classication.- Construction of Chinese pediatric medical knowledge graph.- EasyKG: An End-to-end Knowledge Graph Construction System.