Detecting Missing IS-A Relations in Ontologies

Abstract: Biological ontologies can be used to classify basic terms in biological domains and relations between them. They can also be used as the foundation for interoperability between systems, as community reference and as well as for searching, integration and biological data exchange. Developing ontologies is not easy and most of the time the end result is incomplete or inconsistent. In many cases, such developed ontologies although useful, result into problems when used in semantically-enabled applications. This led to drawing wrong conclusions or failing to see the correct ones. To deal with these problems ontologies need to be repaired. Till today much of the work has been done on searching and repairing the semantic flaws like unsatisfiable concepts and inconsistent ontologies. In this thesis our goal in particular is to find the missing structural relations (is–a hierarchy) between different concepts in ontologies. These concepts are extracted by matching different patterns like Hearst or Hyponymy patterns. Furthermore, the patterns are either noun phrases or the subclasses within a class of patterns. Moreover, we also apply external knowledge such as PubMed to validate the missing is-a relations between concepts found by the algorithm using Hearst and Hyponymy patterns through the documents provided by PubMed. The validation provides us the path to conclude the correctness of our developed algorithm.