UNIpedia: A Unified Ontological Knowledge Platform for Semantic Content Tagging and Search

The emergence of an ever increasing number of documents makes it more and more difficult to locate them when desired. An approach for improving search results is to make use of user-generated tags. This approach has led to improvements. However, they are limited because tags are (1) free from context and form, (2) user generated, (3) used for purposes other than description, and (4) often ambiguous. As a formal, declarative knowledge representation model, Ontologies provide a foundation upon which machine understandable knowledge can be obtained and tagged, and as a result, it makes semantic tagging and search possible. With an ontology, semantic web technologies can be utilized to automatically generate semantic tags. WordNet has been used for this purpose. However, this approach falls short in tagging documents that refer to new concepts and instances. To address this challenge, we present UNIpedia a platform for unifying different ontological knowledge bases by reconciling their instances as WordNet concepts. Our mapping algorithms use rule based heuristics extracted from ontological and statistical features of concept and instances. UNIpedia is used to semantically tag contemporary documents. For this purpose, the Wikipedia and OpenCyc knowledge bases, which are known to contain up to date instances and reliable metadata about them, are selected. Experiments show that the accuracy of the mapping between WordNet and Wikipedia is 84% for the most relevant concept name and 90% for the appropriate sense.