Potluck: semi-ontology alignment for casual users

Authors:
David F. Huynh;Robert C. Miller;David R. Karger
Affiliations:
MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA;MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA;MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA
Venue:
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Year:
2007

Citing 4
Cited 6

Multiple selections in smart text editing

Proceedings of the 7th international conference on Intelligent user interfaces
Faceted metadata for image search and browsing

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Ontology mapping: the state of the art

The Knowledge Engineering Review
Piggy bank: experience the semantic web inside your web browser

ISWC'05 Proceedings of the 4th international conference on The Semantic Web

Wrangler: interactive visual specification of data transformation scripts

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Data collection by the people, for the people

CHI '11 Extended Abstracts on Human Factors in Computing Systems
Proactive wrangling: mixed-initiative end-user programming of data transformation scripts

Proceedings of the 24th annual ACM symposium on User interface software and technology
ShowMeHow: translating user interface instructions between applications

Proceedings of the 24th annual ACM symposium on User interface software and technology
Ontology-Driven conceptual design of ETL processes using graph transformations

Journal on Data Semantics XIII
Research directions in data wrangling: visuatizations and transformations for usable and credible data

Information Visualization - Special issue on State of the Field and New Research Directions

Quantified Score

Hi-index	0.00

Visualization

Abstract

Potluck is a web user interface (Figure 1) that lets casual users-- those without programming skills and data modeling expertise--repurpose heterogeneous Semantic Web data. It lets users merge, navigate, visualize, and clean up data all at the same time, using direct visual manipulation. This iterative process of integrating the data while constructing useful visualizations is desirable when the user is unfamiliar with the data at the beginning--a common case--and wishes to get immediate value out of the data without having to spend the overhead of completely and perfectly integrating the data first.