Wed, Aug 31, 2022 @ 03:00 PM - 04:30 PM
PhD Candidate: Binh Vu
Title: Building Semantic Description of Data Sources
Committee: Craig Knoblock, Sven Koenig, Yolanda Gil, Muhao Chen, Daniel O'Leary
Abstract: A semantic description of a data source precisely describes source attributes' types and the relationships between them. Building semantic descriptions is a prerequisite to automatically publish data to knowledge graphs (KGs). Previous work on this task can be placed into two groups: learning-based and value-linked methods. The learning-based methods require manually labeled semantic descriptions to train their systems. The value-linked methods use the linked entities in a data source to discover candidate semantic descriptions by matching the values in the source with values of entities' properties; hence they are unsupervised. However, the value-linked methods need linked entities and do not work well when the source's data is not in KGs. In this thesis proposal, we propose a method to address the limitations of the value-linked methods. We hypothesize that by exploiting knowledge from web tables and KGs, we can learn semantic descriptions of data sources even when there is little overlap between the sources' data and KGs.
Audiences: Everyone Is Invited
Contact: Lizsl De Leon