Bio
Ramakrishna Kolikipogu is an Associate Professor in the Department of Information Technology at Sridevi Womens Engineering College, Hyderabad, India. His research focuses on Information Retrieval and Text Mining, with a specialization in term expansion techniques using semantic resources. He has published over 20 scholarly papers, earning more than 140 citations and an h-index of 5. He is also an active peer reviewer for multiple journals and has reviewed over 150 manuscripts. Currently, he is completing his PhD in Computer Science and Engineering, further deepening his expertise in the field. His work bridges theoretical advances and practical applications in information retrieval and related areas.
Educational Journey
B.Tech-Computer Science and Information Technology M.Tech-Computer Science with Software Engineering Ph.D - Computer Science and Engineering (About to Complete)
Experience
Sridevi Women's Engineering College
Associate Professor
0 - 0 • Department of ITResearch
Dynamic Vs Static Term-Expansion using Semantic Resources in Information Retrieval
Information Retrieval in a Telugu language is upcoming area of research. Telugu is one of the recognized Indian languages. We present a novel approach in reformulating item terms at the time of crawling and indexing. The idea is not new, but use of synset and other lexical resources in Indian languages context has limitations due to unavailability of language resources. We prepared a synset for 1,43,001 root words out of 4,83,670 unique words from training corpus of 3500 documents during indexing. Index time document expansion gave improved recall ratio, when compared to base line approach i.e. simple information retrieval without term expansion at both the ends. We studied the effect of query terms expansion at search time using synset and compared with simple information retrieval process without expansion, recall is greatly affected and improved. We further extended this work by expanding terms in two sides and plotted results, which resemble recall growth. Surprisingly all expansions are showing improvement in recall and little fall in precision. We argue that expansion of terms at any level may cause inverse effect on precision. Necessary care is required while expanding documents or queries with help of language resources like Synset, WordNet and other resources.
