TY - GEN
T1 - Conceptual clustering of documents for automatic ontology generation
AU - Krishnan, Reshmy
AU - Hussain, Amir
AU - Sherimon, P. C.
PY - 2013
Y1 - 2013
N2 - In Information retrieval, Keyword based retrieval is unsatisfactory for user needs since it can't always retrieve relevant words according to the concept. Since different words can represent the same concept (polysemy) and one word can represent different concepts (homonymy), mapping problem will lead to word sense Disambiguation. Through the implementation of domain dependent ontology, concept based information retrieval (IR) can be achieved. Since Semantic concept extraction from keywords is the initial phase for automatic construction of ontology process, this paper propose an effective method for it. Reuters21578 is used as the input of this process, followed by indexing, training and clustering using self-Organizing Map. Based on the feature vector, the clustering of documents are formed using automatic concept selections, in order to make the hierarchy. Clusters are represented hierarchically based on the topics assigned .Ontology will be generated automatically for each cluster, based on the topic assigned.
AB - In Information retrieval, Keyword based retrieval is unsatisfactory for user needs since it can't always retrieve relevant words according to the concept. Since different words can represent the same concept (polysemy) and one word can represent different concepts (homonymy), mapping problem will lead to word sense Disambiguation. Through the implementation of domain dependent ontology, concept based information retrieval (IR) can be achieved. Since Semantic concept extraction from keywords is the initial phase for automatic construction of ontology process, this paper propose an effective method for it. Reuters21578 is used as the input of this process, followed by indexing, training and clustering using self-Organizing Map. Based on the feature vector, the clustering of documents are formed using automatic concept selections, in order to make the hierarchy. Clusters are represented hierarchically based on the topics assigned .Ontology will be generated automatically for each cluster, based on the topic assigned.
KW - Clustering
KW - Information retrieval
KW - Self-Organizing Map
KW - feature vector
KW - homonymy
KW - indexing
KW - polysemy
UR - https://www.scopus.com/pages/publications/84880264820
U2 - 10.1007/978-3-642-38786-9_27
DO - 10.1007/978-3-642-38786-9_27
M3 - Conference contribution
AN - SCOPUS:84880264820
SN - 9783642387852
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 235
EP - 244
BT - Advances in Brain Inspired Cognitive Systems - 6th International Conference, BICS 2013, Proceedings
T2 - 6th International Conference on Brain Inspired Cognitive Systems, BICS 2013
Y2 - 9 June 2013 through 11 June 2013
ER -