Survey on Semantic Similarity Based on Document Clustering

Abstract

Clustering is a branch of data mining which involves grouping similar data in a collection known as cluster. Clustering can be used in many fields, one of the important applications is the intelligent text clustering. Text clustering in traditional algorithms was collecting documents based on keyword matching, this means that the documents were clustered without having any descriptive notions. Hence, non-similar documents were collected in the same cluster. The key solution for this problem is to cluster documents based on semantic similarity, where the documents are clustered based on the meaning and not keywords. In this research, fifty papers which use semantic similarity in different fields have been reviewed, thirteen of them that are using semantic similarity based on document clustering in five recent years have been selected for a deep study. A comprehensive literature review for all the selected papers is stated. A comparison regarding their algorithms, used tools, and evaluation methods is given. Finally, an intensive discussion comparing the works is presented.

Design and Implementation of LOD Explorer: A LOD Exploration and Visualization Model

“…The future plan for the system is to enrich it with several further functions such as adding more RDF datasets, giving users the opportunity to select a desired shape for the nodes, and adding path finding feature so as to find the exact relation between two or more resources. Furthermore, adding advanced features to include semantic similarity for words and suggest semantically similar words to a query as in [11].…”

Artificial Intelligence Approach for Target Classification: A State of The Art

“…Deep Learning is itself a sub-domain of ML, in which we develop algorithms capable of recognizing abstract concepts, like a young child who is taught to distinguish a dog from a horse [88]. Deep Learning aim to understand concepts in a more précised way.…”

Text documents clustering using data mining techniques

“…In addition, there are also various other approaches for classifying the documents by applying different techniques such as using text mining based on the technology of natural language processing [23,24], building a semantic representation of articles from their associated entities [25,26], and using N-grams and efficient similarity measure that known as improved sqrt-cosine similarity measure [27]. As mentioned in the examples above, the importance of documents clustering and classifying is highlighted to satisfy users and facilitate the retrieval process of relevant documents.…”