measuring semantic similarity by internet based knowledge
K.Anandhi,M.Karthick,K.Deepak kumar
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:4 Issue:3 Year: 30 March,2016 Pages:545-550
Abstract
Measuring the semantic similarity between words is an important component in various tasks on the web are relation extraction, community mining, document clustering, and automatic metadata extraction.The usefulness of semantic similarity measures in these applications, accurately measuring semantic similarity between two words remains a challenging task. The project proposes an empirical method to estimate semantic similarity using page counts and text snippets retrieved from a web search engine for two words. Specifically, it defines various word co-occurrence measures using page counts and integrates those with lexical patterns extracted from text snippets. A multidimensional ontology mining method, Specificity and Exhaustivity, is also introduced in the proposed model for analyzing specified concepts. Data mining an interdisciplinary subfield of computer science, is the computational process of discovering patterns in a large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall aim of the data mining process is to extract information from a data set and transform it into an understandable structure for further usage. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and the inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and in online updating Data mining involves six common classes of tasks: Anomaly detection the identification of unusual data records, that is interesting or data errors that require further investigation.
Kewords
Dependency modeling
Reference
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