Estimating Information Loss for Multi-ontology Based Query Processing
The World Wide Web is fast becoming a ubiquitous computing environment. Prevalent Keyword-based search techniques are scalable, but are incapable of accessing information based on concepts. We investigate the use of concepts from multiple, real-world pre-existing, domain ontologies to describe the underlying data content and support information access at a higher level of abstraction. It is infeasible to expect a single domain ontology to describe the vasts amounts of data on the web. In fact we expect multiple ontologies to be used as different world views and present an approach to 'browse' ontologies as a paradigm for information access. A critical challenge in this approach is the vocabulary heterogeneity problem. Queries are rewritten using interontology relationships to obtain translations across ontologies. However, some translations may not be semantics preserving, leading to uncertainty or loss in the information retrieved. We present a novel approach for estimating loss of information based on navigation of ontological terms. We define measures for loss of information based on intensional information as well as on well established metrics like precision and recall based on extensional information. This measures are used to select results of the desired quality of information.
& Sheth, A. P.
(1998). Estimating Information Loss for Multi-ontology Based Query Processing. Proceedings of the 13th Biennial European Conference on Artificial Intelligence (ECAI), 93-108.