Description
Research in and development of Natural Language Processing (NLP) and Information Retrieval (IR) approaches, methods and techniques has been key for the Semantic Web (SW). The synergy between these research fields established novel approaches for entity linking, knowledge mining, and ontology population, among other core SW tasks. This track invites high quality submissions that show how NLP and IR methods and techniques can be applied to develop and improve SW methods or how SW technologies and resources can be used to enhance NLP and/or IR methods.
Topic of Interest
Topics of interest include but are not limited to:
- Language resources, corpora building and annotation languages for the SW
- Lexical semantics for the SW
- NL-based representations and knowledge systems using SW languages
- Information and knowledge extraction (including taxonomy extraction, ontology learning, knowledge graph learning)
- Data, information and knowledge integration across languages
- Use of knowledge graphs and ontologies for NLP
- Natural language understanding and the SW
- Semantic search and exploratory search
- Semantic similarity metrics and analogy
- Question answering over knowledge graphs
- Natural language text generation from knowledge graphs and ontologies
- Text analytics for the Internet of Things
- Data mining and knowledge discovery from Linked Data and ontologies
- Learning from Big Data for web-scale knowledge graphs
- Ontology population through NLP and IR methods
- Learning Analytics Knowledge
- Dialogue systems for the SW
- Multimodal human-computer communication for the SW
- Combining NL and SW in learning environments
- Applications of SW and NLP for digital humanities, intelligent tutoring, social sciences
Track Chairs
Marieke van Erp, KNAW Humanities Cluster, Amsterdam, the Netherlands
Paul Buitelaar, NUI Galway, Galway, Ireland
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