  Since long, corporations are looking for knowledge sources which can provide
structured description of data and can focus on meaning and shared
understanding. Structures which can facilitate open world assumptions and can
be flexible enough to incorporate and recognize more than one name for an
entity. A source whose major purpose is to facilitate human communication and
interoperability. Clearly, databases fail to provide these features and
ontologies have emerged as an alternative choice, but corporations working on
same domain tend to make different ontologies. The problem occurs when they
want to share their data/knowledge. Thus we need tools to merge ontologies into
one. This task is termed as ontology matching. This is an emerging area and
still we have to go a long way in having an ideal matcher which can produce
good results. In this paper we have shown a framework to matching ontologies
using graphs.
