The nature of statistical learning theory
The nature of statistical learning theory
Introduction to algorithms
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
RStar: an RDF storage and query system for enterprise resource management
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Optimizing progressive query-by-example over pre-clustered large image databases
Proceedings of the 2nd international workshop on Computer vision meets databases
Supporting ontology-based semantic matching in RDBMS
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Unifying data and domain knowledge using virtual views
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Alison balter's mastering microsoft® office access 2007 development
Alison balter's mastering microsoft® office access 2007 development
Semantic queries in databases: problems and challenges
Proceedings of the 18th ACM conference on Information and knowledge management
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Hi-index | 0.00 |
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.