BEGIN:VCALENDAR METHOD:PUBLISH PRODID:-//Apple Computer\, Inc//iCal 1.0//EN X-WR-CALNAME;VALUE=TEXT:USC VERSION:2.0 BEGIN:VEVENT DESCRIPTION:PhD Candidate: Binh Vu\n \n Title: Building Semantic Description of Data Sources\n \n Committee: Craig Knoblock, Sven Koenig, Yolanda Gil, Muhao Chen, Daniel O'Leary\n \n 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. SEQUENCE:5 DTSTART:20220831T150000 LOCATION: DTSTAMP:20220831T150000 SUMMARY:PhD Thesis Proposal - Binh Vu UID:EC9439B1-FF65-11D6-9973-003065F99D04 DTEND:20220831T163000 END:VEVENT END:VCALENDAR