Shiva: A Framework for Graph Based Ontology Matching
This addresses the need for interoperability among corporations using ontologies, but appears incremental as it builds on existing graph-based methods without claiming major breakthroughs.
The paper tackles the problem of merging different ontologies from the same domain to enable data sharing by proposing a graph-based framework for ontology matching, but does not provide concrete results or numbers.
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.