The Semantic Web - ISWC 2016
Description
The two-volume set LNCS 9981 and 9982 constitutes the refereed proceedings of the 15th
International Semantic Web Conference, ISWC 2016, which was held in Kobe, Japan, in October 2016. The 75 full papers presented in these proceedings were carefully reviewed and selected from 326 submissions.
The International Semantic Web Conference is the premier forum for Semantic Web research, where cutting edge scientific results and technological innovations are presented, where problems and solutions are discussed, and where the future of this vision is being developed. It brings together specialists in fields such as artificial intelligence, databases, social networks, distributed computing, Web engineering, information systems, human-computer interaction, natural language processing, and the social sciences.
The Research Track solicited novel and significant research contributions addressing theoretical, analytical, empirical, and practical aspects of the Semantic Web. The Applications Track solicited submissions exploring the benefits and challenges of applying semantic technologies in concrete, practical applications, in contexts ranging from industry to government and science. The newly introduced Resources Track sought submissions providing a concise and clear description of a resource and its (expected) usage. Traditional resources include ontologies, vocabularies, datasets, benchmarks and replication studies, services and software. Besides more established types of resources, the track solicited submissions of new types of resources such as ontology design patterns, crowdsourcing task designs, workflows, methodologies, and protocols and measures.
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Content
Structuring Linked Data Search Results Using Probabilistic Soft Logic.- The multiset semantics of SPARQL patterns.- Ontop of Geospatial Databases.- Expressive Multi-Level Modeling for the Semantic Web.- Practical Acyclicity Notions for Query Answering Horn-SRIQ Ontologies.- Containment of Expressive SPARQL Navigational Queries.- WebBrain: Joint Neural Learning of Large-Scale Commonsense Knowledge.- Efficient Algorithms for Association Finding and Frequent Association Pattern Mining.- A Reuse-based Annotation Approach for Medical Documents.- Knowledge Representation on the Web revisited: the Case for Prototypes.- Updating DL-Lite Ontologies through First-Order Queries.- Are Names Meaningful? Quantifying Social Meaning on the Semantic Web.- User validation in ontology alignment.- Seed, an End-user Text Composition Tool for the Semantic Web.- Exception-enriched Rule Learning from Knowledge Graphs.- Planning Ahead: Stream-Driven Linked-Data Access under Update-Budget Constraints.- Explicit Query Interpretation and Diversification for Context-driven Concept Search across Ontologies.- Predicting Energy Consumption of Ontology Reasoning over Mobile Devices.- Walking without a Map: Ranking-Based Traversal for Querying Linked Data.- CubeQA-Question Answering on RDF Data Cubes.- Optimizing Aggregate SPARQL Queries using Materialized RDF Views.- Algebraic calculi for weighted ontology alignments.- Ontologies for Knowledge Graphs: Breaking the Rules.- An Extensible Linear Approach For Holistic Ontology Matching.- Semantic Sensitive Simultaneous Tensor Factorization.- Multi-level semantic labelling of numerical values.- Semantic labeling: A domain-independent approach.- Exploiting Emergent Schemas to make RDF systems more efficient.- Distributed RDF Query Answering with Dynamic Data Exchange.- RDF2Vec: RDF Graph Embeddings for Data Mining.- SPARQL-to-SQL on Internet of Things Databases and Streams.- Can you imagine... a language for combinatorial creativity?.- Leveraging Linked Data to Discover Semantic Relations within Data Sources.- Integrating medical scientific knowledge with the semantically Quantified Self.- Learning to Assess Linked Data Relationships Using Genetic Programming.- A Probabilistic Model for Time-Aware Entity Recommendation.- A Knowledge Base Approach to Cross-lingual Keyword Query Interpretation.- Context-Free Path Queries on RDF Graphs.- Unsupervised Entity Resolution on Multi-type Graphs.