Name
Abstract | ||
|---|---|---|
Ontologies provide features like a common vocabulary, reusability, machine-readable content, and also allows for semantic search, facilitate agent interaction and ordering & structuring of knowledge for the Semantic Web (Web 3.0) application. However, the challenge in ontology engineering is automatic learning, i.e., the there is still a lack of fully automatic approach from a text corpus or dataset of various topics to form ontology using machine learning techniques. In this paper, two topic modeling algorithms are explored, namely LSI & SVD and Mr.LDA for learning topic ontology. The objective is to determine the statistical relationship between document and terms to build a topic ontology and ontology graph with minimum human intervention. Experimental analysis on building a topic ontology and semantic retrieving corresponding topic ontology for the user's query demonstrating the effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.engappai.2017.05.006 | Engineering Applications of Artificial Intelligence |
Keywords | DocType | Volume |
Ontology Learning (OL),Latent Semantic Indexing (LSI),Singular Value Decomposition (SVD),Probabilistic Latent Semantic Indexing (pLSI),MapReduce Latent Dirichlet Allocation (Mr.LDA),Correlation Topic Modeling (CTM) | Journal | 63 |
ISSN | Citations | PageRank |
0952-1976 | 8 | 0.51 |
References | Authors | |
37 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Monika Rani | 1 | 33 | 2.80 |
| Amit Kumar Dhar | 2 | 17 | 2.71 |
| Om Prakash Vyas | 3 | 52 | 8.92 |