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ii Dedication To my wife Sandhya Reddy, my parents Tulasamma and Nagi Reddy, and my brother Konda Reddy and his family. iii Acknowledgments Let me first express deepest appreciation and gratitude to my major adviser Dr. Jian-hua Chen, for her inspiration, encouragement, patience, mentorship, and guidance. I am grateful for her assistance and motivation(More)
Ontology of a domain mainly consists of concepts, taxonomical (hierarchical) relations and non-taxonomical relations. Automatic ontology construction requires methods for extracting both taxonomical and non-taxonomical relations. Compared to extensive works on concept extraction and taxonomical relation learning, little attention has been given on(More)
Ontology of a domain mainly consists of concepts, hierarchical relations, and non-hierarchical relations. Even though there exists a variety of methods for extracting concepts and hierarchical relations, very little concentration is on identification and labeling of non-hierarchical relations. In this paper, we present an unsupervised technique for the(More)
— Domain specific concept extraction is a key component in ontology construction for Semantic Web applications. Manual concept extraction is costly both in time and labor. In this paper, we present several heuristic methods for automatic concepts extraction from domain texts. These methods aim to improve the precision and recall over the word(More)
Extraction of concepts and identification of their semantic classes are useful in applications such as automatic instantiation of ontologies and construction of information extraction systems. Even though various techniques exist for the extraction of domain specific concepts from unstructured texts, very little concentration is in the semantic class(More)
Text mining models routinely represent each document with a vector of weighted term frequencies. This bag-of-words approach has many strengths, one of which is representing the document in a compact form that can be used by standard data mining tools. However, this approach loses most of the contextual information that is conveyed in the relationship of(More)
Learning taxonomical relations from domain texts is an important task for ontology learning from texts. We observe that rich information on taxonomical relations is available in the lexical knowledge base WordNet. However, in order to exploit the taxonomical relations in WordNet we need to tackle the difficult problem of word sense disambiguation. In this(More)
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