An Empirical Analysis of Semantic Techniques Applied to a Network Management Classification Problem

Abstract

Semantic technologies are increasingly being employed to integrate, relate and classify heterogeneous data from various problem domains. To date, however, little empirical analysis has been carried out to help identify the benefits and limitations of different semantic approaches on specific data integration and classification problems. This paper evaluates three alternative semantic techniques for performing classification over data derived from the telecommunications domain. The problem of interest involves inferring the "health" status of network nodes (femtocells) from synthesized performance management (PM) instance data based on the operational PM schema. The semantic approaches used in the comparison include OWL2 axioms, SPARQL queries and SWRL rules. Empirical tests were performed across a range of data set sizes, using Pellet for axioms and rules and ARQ for queries. The experimental results provide (mostly) quantitative and (some) qualitative indication of the relative merits of each approach. Key among these findings is confirmation of the clear superiority of queries over rules and axioms in terms of raw performance and scalability.