A Graph Derivation Based Approach for Measuring and Comparing Structural Semantics of Ontologies

A GRAPH DERIVATION BASED APPROACH FOR MEASURING AND COMPARING STRUCTURAL SEMANTICS OF ONTOLOGIES

ABSTRACT:

Ontology reuse offers great benefits by measuring and comparing ontologies. However, the state of art approaches for

Measuring ontologies neglects the problems of both the polymorphism of ontology representation and the addition of implicit semantic knowledge. One way to tackle these problems is to devise a mechanism for ontology measurement that is stable, the basic criteria for automatic measurement. In this paper, we present a graph derivation representation based approach (GDR) for stable semantic measurement, which captures structural semantics of ontologies and addresses those problems that cause unstable measurement of ontologies. This paper makes three original contributions. First, we introduce and define the concept of semantic measurement and the concept of stable measurement. We present the GDR based approach, a three-phase process to transform ontology to its GDR. Second, we formally analyze important properties of GDRs based on which stable semantic measurement and comparison can be achieved successfully. Third but not the least, we compare our GDR based approach with existing graph based methods using a dozen real world exemplar ontologies. Our experimental comparison is conducted based on nine ontology measurement entities and distance metric, which stably compares the similarity of two ontologies in terms of their GDRs.

EXISTING SYSTEM:

As the size and the number of ontologies continue to increase, the reuse and the stable measurement of ontologies offer several important benefits. First, the effort of constructing new ontologies can be significantly reduced by reusing existing ontologies instead of starting from scratch. Another benefit of ontology reuse is its potential to significantly facilitate the data interoperability in heterogeneous information systems by sharing a common ontology.

DISADVANTAGES OF EXISTING SYSTEM:

v They are only graphical models representing ontological structure.

v   Semantic derivation cases are the common causes for the problem of polymorphism of ontology representation.

v    

PROPOSED SYSTEM:

We propose a graph derivation (GDR) based approach for stable semantic measurements of ontologies in three phases. In Phase 1, we generate a GDR for each concept in a given ontology by recursively applying a series of derivation. In Phase 2, we utilize the integration operations to merge multiple GDRs to produce an initial integrated GDR for the given ontology. In Phase 3, we generate the complete GDR representation for the given ontology by treating those relations that cause unstable semantic measurements, such as the problems of non-direct transitive relations, cycles of inheritance, and double counting. We argue that the GRD based approach will significantly facilitate reliable ontology measurement and comparison.

ADVANTAGES OF PROPOSED SYSTEM:

v Using Graph Derivation method.

SYSTEM CONFIGURATION:-

HARDWARE REQUIREMENTS:-

Processor                  -        Pentium –IV

Speed                        -        1.1 Ghz

RAM                         -        512 MB(min)

Hard Disk                 -        40 GB

Key Board                -        Standard Windows Keyboard

Mouse                       -        Two or Three Button Mouse

Monitor                     -        LCD/LED

SOFTWARE REQUIREMENTS:

Operating system      :         Windows XP.

Coding Language      :         .Net

Data Base                 :         SQL Server 2005

Tool                          :         VISUAL STUDIO 2008.

REFERENCE:

Yinglong Ma, Ling Liu, Senior Member, IEEE, Ke Lu, Beihong Jin, and Xiangjie Liu, “A Graph Derivation Based Approach for Measuring and Comparing Structural Semantics of Ontologies” IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 26, NO. 5, MAY 2014