Semantic Queries by Example
Venue
Proceedings of the 16th International Conference on Extending Database Technology (EDBT 2013) (to appear)
Publication Year
2013
Authors
Lipyeow Lim, Haixun Wang, Min Wang
BibTeX
Abstract
With the ever increasing quantities of electronic data, there is a growing need to
make sense out of the data. Many advanced database applications are beginning to
support this need by integrating domain knowledge encoded as ontologies into
queries over relational data. However, it is extremely difficult to express queries
against graph structured ontology in the relational SQL query language or its
extensions. Moreover, semantic queries are usually not precise, especially when
data and its related ontology are complicated. Users often only have a vague notion
of their information needs and are not able to specify queries precisely. In this
paper, we address these challenges by introducing a novel method to support
semantic queries in relational databases with ease. Instead of casting ontology
into relational form and creating new language constructs to express such queries,
we ask the user to provide a small number of examples that satisfy the query she
has in mind. Using those examples as seeds, the system infers the exact query
automatically, and the user is therefore shielded from the complexity of
interfacing with the ontology. Our approach consists of three steps. In the first
step, the user provides several examples that satisfy the query. In the second
step, we use machine learning techniques to mine the semantics of the query from
the given examples and related ontologies. Finally, we apply the query semantics on
the data to generate the full query result. We also implement an optional active
learning mechanism to find the query semantics accurately and quickly. Our
experiments validate the effectiveness of our approach.
