@article{MAKHILLJEAS201914217337,
    title = {Automated Ensemble Framework for Integration of Ontology
Based Large Scale Semantic Knowledge Base},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {2},
    pages = {399-404},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.399.404},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.399.404},
    author = {G. and},
    keywords = {Ontology,knowledge base,semantic web,data integration,ensemble technique,efficiency},
    abstract = {Knowledge base is growing exponentially now a days using different techniques. The ontology has
been used widely to integrate the knowledge base for easy retrieval of the web document contents to the user
queries. Several steps have been taken in the literatures to integrate the knowledge base which contains the
overlapping and complementary information. In this study, we propose a novel technique to knowledge based
integration named “automatic ensemble framework for integration of ontology based sematic knowledge base”.
It considers the semantic heterogeneous class structures. The proposed framework provides the Solution to
the NP hard problem in terms of query selection. Ensemble framework produces the multiple class structures
to the knowledge base as knowledge base is large in size and structure matching model is leverages to identify
the relationship based on semantic in order to integrate the complex structures of the different KBs. Integrated
Knowledge base is been available to access through queries but improper information selection to query leads
to complex problem which can be avoided by placing the adaptive query selection algorithm using greedy
algorithms. The experimental result demonstrates that proposed model outperforms the state of art approaches
in terms of effectiveness, efficiency and accuracy.}
    }