Our work aims to extend the principles of the ontology mapping approach as well as the emerging Web services standards in order to support the manageability and interoperability of heterogeneous data sources. A fundamental problem with ontology mapping involves the integration of heterogeneous data sources, which has been researched extensively in the last two decades (Rahm & Bernstein, 2001).
Background Knowledge Used in Mapping
Some research approaches (Sabou, d’Aquin, & Motta, 2006; Aleksovski, Klein, ten Kate, & Harmelen, 2006; Ehrig & Staab, 2004) have considered the use of external background knowledge as a way of obtaining semantic mappings between syntactically dissimilar ontologies. WordNet is one of the most frequently used sources of background knowledge. The literature (Li, Szpakowicz, & Matwin, 1995) shows that WordNet has been used successfully for word sense disambiguation algorithms in other contexts, particularly in text. WordNet is an extremely large and readily available in an online database, which is divided into various parts of speech such as nouns, verbs, adjectives, and adverbs. The nouns are organized as a hierarchy of nodes where each node is a word meaning or, as it is termed in WordNet, a synset, which is simply a set of English words that express the same meaning in at least one context.
SUMO was initially created by Ian Niles and Adam Pease (2001). As one of three starter documents under consideration by the IEEE, SUMO was developed to facilitate data interoperability, information search and retrieval, automated inference, and natural language processing.