Providing ranked cooperative query answers using the metricized knowledge abstraction hierarchy

Abstract

Abstract Cooperative query answering supports query relaxation and provides approximate answers as well as exact answers. To facilitate the query relaxation, a knowledge representation framework has been widely adopted, which accommodates semantic relationships or distance metrics to represent similarities among data values. In this paper, we propose a metricized knowledge abstraction hierarchy (MKAH) that supports multi-level data abstraction hierarchy and distance metric among data values. We show that the abstraction hierarchy is useful in representing the semantic relationship, and the abstraction hierarchy can provide data values with different scope according to their abstraction levels. The distance metric expresses the semantic similarity among data values with quantitative measure, and thus it enables query results to be ranked. To verify the practicality and effectiveness of the MKAH, we have implemented a prototype system in the area of career job search. Through various experiments, we show that the MKAH provides rich semantic representation and high quality distance measure. Furthermore, the experiments confirm that the domain adopting the MKAH can be compatible with other numeric domains, and that is advantageous in building up large scaled systems.