Theses and Dissertations - UTB/UTPA

Date of Award


Document Type


Degree Name

Master of Science (MS)


Computer Science

First Advisor

Dr. Artem Chebotko

Second Advisor

Dr. Xiang Lu

Third Advisor

Dr. Bin Fu


We formulate and tackle a flexible and useful query, namely k-nearest keyword (k-NK) query, which can identify the relationship between vertices (or keywords) in an RDF graph, where users are only required to specify two query keywords q and w (without knowing the domain knowledge). In particular, a k-NK query returns k closest pairs of vertices (ui; vi) in the RDF graph such that vertices ui and vi contain keywords q and w, respectively, and vi has the smallest (shortest path) distance to ui (i.e., vi is the nearest neighbor of ui). In order to efficiently answer k-NK queries, in this paper, we propose three efficient query answering techniques that utilize effective pruning strategies and cost-model-based indexing mechanisms. We also confirm the effects of our proposed approaches on real and synthetic RDF data sets through extensive experiments.


Copyright 2012 Eugenio De Hoyos. All Rights Reserved.

Granting Institution

University of Texas-Pan American