Automatic Relationship Construction in Domain Ontology Engineering using Semantic and Thematic Graph Generation Process and Convolution Neural Network
By Dr. Sivaramakrishnan R Guruvayur, R.Suchithra
In recent studies, Ontology construction plays an important role in
translating raw text into useful knowledge. The proposed methodology
supports efficient retrieval using multidimensional theory and implements
integrated data training techniques before enter the trial process. The
proposed approach has used the Semantic and Thematic Graph Generation
Process to extract useful knowledge, and uses data mining techniques and web
solutions to present knowledge as well as improve search speed and
information retrieval accuracy. Established ontology can help clarify what
it means for different ideas and relationships. Due to the rise of the
ontology repository, the process of matching can take a long time. To avoid
this, the method produces a hierarchical structure with in-depth
interpretation of the data. A system is designed to remove domain
dependencies using a dynamic labeling scheme using basic theorem, and the
results show that it is possible to automatically and independently
construct an independent domain.
Index terms: Automatic Ontology Generation, Semantic Web, Semantic Graph
Generation, Thematic Graph Generation Process and Convolution Neural
Network.