Navigation Cost Modeling Based On Ontology

Abstract

Web databases when queried result in huge number of records when users of query need a portion of those results which are real interest to them. This problem can be solved using concept hierarchies. Knowledge representation in the form of concepts and the relationships among them (Ontology) allows effective navigation. This paper presents provisions for categorization and ranking in order to reduce the number of results of query and also ensure that the navigation is effective. User should not spend much time to view the actual subset of records he is interested in from the avalanche of records that have been retrieved. For experiments, PubMed database which is in the public domain is used. The PubMed data is medical in nature and organized as per the annotations provided that is instrumental in making concept hierarchies to represent the whole dataset of PubMed. The proposed technique in this paper provides a new search interface that facilitates end users to have effective navigation of query results that are presented in the form of concept hierarchies. Moreover the query results are presented in such a way that the navigation cost is minimized and thus giving rich user experience in this area. The empirical results revealed that the proposed navigation system is effective and can be adapted to real world systems where huge number of tuples is to be presented. Index Terms - Concept hierarchy, effective navigation, annotated data I. Introduction The amount of data provided over World Wide Web (WWW) is increasing rapidly every year. In the past decade in started growing drastically. Especially biomedical data and the literature pertaining to it that reviews the aspects of biomedical data across the globe have seen tremendous growth in terms of quantity. Biological data sources such as (2), (3), and (4) are growing in terms of lakhs of new citations every year. The queries made by people associated with healthcare domain have to search such databases by providing a search keyword. The results are very huge in number and the users are not able to view all the records when they actually need a subset of them. This has led to users to refine query with other keywords and get the desired results after many trials. Here it has to be observed that user time is wasted in refining search criteria and also the navigation of query results which are abundant and bulky. The navigation cost is more as user has to spend lot of time in finding the required subset of rows from the bulk of search results. This problem has been researched in (2), (3), (4) and the problem is identified as information overload. Figure 1 shows static navigation of MeSh hierarchy of biomedical data.