TY - JOUR
T1 - Exploiting Ontology Recommendation Using Text Categorization Approach
AU - Sarwar, Muhammad Azeem
AU - Ahmed, Mansoor
AU - Habib, Asad
AU - Khalid, Muhammad
AU - Ali, Akhtar
AU - Raza, Mohsin
AU - Hussain, Shahid
AU - Ahmed, Ghufran
PY - 2021/2/18
Y1 - 2021/2/18
N2 - Semantic Web is considered as the backbone of web 3.0 and ontologies are an integral part of the Semantic Web. Though an increase of ontologies in different domains is reported due to various benefits which include data heterogeneity, automated information analysis, and reusability, however, finding an appropriate ontology according to user requirement remains cumbersome task due to time and efforts required, context-awareness, and computational complexity. To overcome these issues, an ontology recommendation framework is proposed. The Proposed framework employs text categorization and unsupervised learning techniques. The benefits of the proposed framework are twofold: 1) ontology organization according to the opinion of domain experts and 2) ontology recommendation with respect to user requirement. Moreover, an evaluation model is also proposed to assess the effectiveness of the proposed framework in terms of ontologies organization and recommendation. The main consequences of the proposed framework are 1) ontologies of a corpus can be organized effectively, 2) no effort and time are required to select an appropriate ontology, 3) computational complexity is only limited to the use of unsupervised learning techniques, and 4) due to no requirement of context awareness, the proposed framework can be effective for any corpus or online libraries of ontologies.
AB - Semantic Web is considered as the backbone of web 3.0 and ontologies are an integral part of the Semantic Web. Though an increase of ontologies in different domains is reported due to various benefits which include data heterogeneity, automated information analysis, and reusability, however, finding an appropriate ontology according to user requirement remains cumbersome task due to time and efforts required, context-awareness, and computational complexity. To overcome these issues, an ontology recommendation framework is proposed. The Proposed framework employs text categorization and unsupervised learning techniques. The benefits of the proposed framework are twofold: 1) ontology organization according to the opinion of domain experts and 2) ontology recommendation with respect to user requirement. Moreover, an evaluation model is also proposed to assess the effectiveness of the proposed framework in terms of ontologies organization and recommendation. The main consequences of the proposed framework are 1) ontologies of a corpus can be organized effectively, 2) no effort and time are required to select an appropriate ontology, 3) computational complexity is only limited to the use of unsupervised learning techniques, and 4) due to no requirement of context awareness, the proposed framework can be effective for any corpus or online libraries of ontologies.
KW - clustering
KW - recommendation system
KW - semantic web
KW - ontology
KW - text categorization
KW - text mining
KW - unsupervised learning
U2 - 10.1109/ACCESS.2020.3047364
DO - 10.1109/ACCESS.2020.3047364
M3 - Article
VL - 9
SP - 27304
EP - 27322
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
ER -