Semantic enrichment of data for AI applications
Fatma Özcan, Chuan Lei, Abdul Quamar, Vasilis Efthymiou
Abstract
In this work, we use semantic knowledge sources, such as cross-domain knowledge graphs (KGs) and domain-specific ontologies, to enrich structured data for various AI applications. By enriching our understanding of the underlying data with semantics brought in from external ontologies and KGs, we can better interpret the data as well as the queries to answer more questions, provide more complete answers, and deal with entity disambiguation. To semantically enrich the data with external knowledge sources, we need to find the correspondences between the structured data and the entities in the cross-domain KGs and/or the domain-specific ontologies. In this paper, we break this problem into several steps, and provide detailed solutions for each step. We showcase the practical value of semantic enrichment of data using our proposed techniques in entity disambiguation, natural language querying and conversational interfaces to data, query relaxation, as well as query answering, with promising results.