Words, sentences, paragraphs and documents is an important component in the data mining application area such as information retrieval, text summarization etc. And finding the similarity value between these components is an important action. Now a day, there are various matching techniques are present which are very helpful for finding the similarity between the ontology. In this survey paper discusses the existing works on similarity (On text, words etc.) using element-level techniques and structure level techniques.
On the web, electronic form of information is increasing exponentially with the passage of past few years. Also, this advancement creates its own uncertainties. The overload information result is progressive while finding the relevant data with a chance of HIT or Miss Exposure. For improving this, Information Retrieval Ranking, Tokenization and Clustering techniques are suggestive as probable solutions. In this paper, Probabilistic Ranking using Vectors (PRUV) algorithm is proposed, in which tokenization and Clustering of a given documents are used to create more precisely and efficient rank gratify user's information need to execute sharply reduced search, is believed to be a part of IR. Tokenization involves pre-processing of the given documents and generates its respective tokens and then based on probability score cluster are created. Performance of some of existing clustering techniques (K-Means and DB-Scan) is compared with proposed algorithm PRUV, using various parameters, e.g. Time, Accuracy and Number of Tokens Generated.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.