Efficient Information Retrieval By Using Multi- Modality Manifold Ranking Based On Syntactic/Semantic Measurement

Ranking is the major problem in variety of applications like information retrieval (IR), Data mining (DM) and natural language processing (NLP). To rank the objects according to their importance multiplicity has also been identified as a important criterion. Manifold Ranking with Sink Points (MRSP) is one of the novel approaches to conquer this problem. This approach uses a manifold ranking procedure data manifold to determine the most pertinent and significant data objects to rank the data objects. The measurement of the semantic/syntactic similarity between the terms and multimanifold ranking are not supported in the existing system. While considering the semantic and syntactic based measuring also important to improve the information retrieval (IR) result than the normal keyword based results. Compared to normal keyword based results the semantic and syntactic based measuring also important to improve the information retrieval (IR). Proposed system uses a Multi-Modality Manifold Ranking (MMMR) which contains multiple data manifolds, where each and every data is constructed using distinct keywords. It employs syntactic and shallow semantic kernels to estimate the importance between the terms. The tree kernel functions are exploit for multi-modality manifold ranking framework. In addition to syntactic and semantic information can develop the performance of the multimodality manifold-ranking algorithm. Keywords—Manifold ranking with sink points, update summarization, query recommendation, Multi-manifold ranking.