SCOPE and OBJECTIVES
Knowledge graphs represent world knowledge in multigraph and labeled
structures. Entities linked through relationships enable effective navigation
and pattern discovery. Knowledge graphs have become an integral part of many AI
applications empowered by sophisticated machine learning models including deep neural
networks. Vast amount of unstructured data on the Web are analyzed and converted to
knowledge graphs for improved machine processing. Moreover, many enterprises are building
large-scale knowledge graphs that drive their products.
In past several years, knowledge graph research has attracted an increased
attention in both academia and industry. While publicly available knowledge
graphs such as FreeBase and YAGO are useful for experimenting with novel ideas,
large-scale real-world knowledge graphs start to play a critical role in biomedical,
healthcare, and business applications, including drug discovery, fraud detection,
item recommendation, search engine, question answering, financial intelligence,
image processing, virtual assistant, human computer interaction, and robotics.
Big industry-scale knowledge graphs bring out many new research issues.
This workshop provides a platform for knowledge graph researchers and
practitioner to exchange research ideas and solutions related to knowledge
graph representation, mining, reasoning, and applications in big data settings.