• Dr .A.Mekala

With the advent of the Internet, the amount of Semantic Web documents that describe real-world entities and their inter-links as a set of statements have grown considerably. These descriptions are usually lengthy, which makes the utilization of the underlying entities a difficult task. Entity summarization, which aims to create summaries for real world entities, has gained increasing attention in recent years. we present hierarchical relation based Labelled Latent Dirichlet Allocation (L-LDA), a data-driven hierarchical topic model for extracting terminological ontologies from a large number of heterogeneous documents. In contrast to traditional topic models, L-LDA relies on noun phrases instead of unigrams, considers syntax and document structures, and enriches topic hierarchies with topic relations. Through a series of experiments, we demonstrate the superiority of L-LDA over existing topic models, especially for building hierarchies. Furthermore, we illustrate the robustness of L-LDA in the settings of noisy data sets, which are likely to occur in many practical scenarios. Our ontology evaluation results show that ontologies extracted from L-LDA are very competitive with the ontologies created by domain experts.

Ontology, L-LDA, Recall, Precision, F-Measure Rate.

"Ontology Summarization Using Labelled Latent Dirichlet Allocation Method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.757-760, July-2018, Available :http://www.jetir.org/papers/JETIR1807122.pdf

"Ontology Summarization Using Labelled Latent Dirichlet Allocation Method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp757-760, July-2018, Available at : http://www.jetir.org/papers/JETIR1807122.pdf