An Ontology Based Model for Document Clustering

Sridevi, U. K.; Nagaveni, N.

doi:10.4018/jiit.2011070105

Cited by 16 publications

(9 citation statements)

References 50 publications

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“…F-measure is evaluated using the Equation 3. A higher F-measure indicates better performance [21,22].…”

Section: Clustering Evaluationmentioning

confidence: 99%

Agent for Documents Clustering using Semantic-based Model and Fuzzy

Fouad¹,

Hassan²

2013

IJCA

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Text clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large sets of documents into a small number of meaningful clusters. Many fuzzy clustering algorithms, such as K-means, deal with documents as bag of words. The bag of words representation method used for these clustering is often unsatisfactory because it ignores the semantic of words. The proposed agent exploits WordNet ontology to create low dimensional feature vector which allows us to develop an efficient clustering algorithm. A new semantic-based model, that represents documents based on semantic concepts of words, is proposed. The proposed approach aims at increasing the performance of information retrieval process by enhancing the document clustering. The accuracy and the speed of clustering have been examined before and after combining ontology with Vector Space Model (VSM). Experimental results demonstrate that using semantic-based model and fuzzy clustering enhances the clustering quality of sets of documents.

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“…F-measure is evaluated using the Equation 3. A higher F-measure indicates better performance [21,22].…”

Section: Clustering Evaluationmentioning

confidence: 99%

Agent for Documents Clustering using Semantic-based Model and Fuzzy

Fouad¹,

Hassan²

2013

IJCA

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“…Well-known methods like 'tf-idf' (Salton & McGill, 1983) are only suitable in a pre-processing manner because other than the fact that it is not probabilistic, it also holds a small amount of reduction which would lead to unsatisfying scaling behavior in incremental load environments (Rahman, 2010) considering the fast growing database of Twitter streams our research is based on. Other non-probabilistic models include fuzzy based clustering of documents (Thangamani & Thangaraj, 2011) or ontology based models for document segmentation (Sridevi & Nagaveni, 2011). The 'pLSI' model by Hofmann (1999), while probabilistic in its nature, lacks a representation on a document level.…”

Section: Exploring Microblog Entries With Topic Modelsmentioning

confidence: 99%

What is the Conversation About?

Sommer¹,

Schieber

Heinrich

et al. 2012

International Journal of Intelligent Information Technologies

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In Social Commerce customers evolve to be an important information source for companies. Customers use the communication platforms of Web 2.0, for example Twitter, in order to express their sentiments about products or discuss their experiences with them. These sentiments can be very important for the development of products or the enhancement of marketing strategies. The research goal is to analyze customer sentiments in Twitter. The first step in the research is the detection of topics in Twitter entries which contain patterns of interest. For the topic detection, the authors use Latent Dirichlet Allocation for topic modeling. The authors found event based topics in the exemplary context of Sony’s 3D TV sets. In future work, the authors will implement sentiment analysis algorithms in order to determine sentiments in the entries corresponding to the detected topics.

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“…Keyword based search mechanism is improved by the use of Ontologies. PSO based clustering mechanism is used to group documents based on their similarity score which improves the relevancy of documents (Sridevi & Nagaveni, 2011). According to Potok et al, Hybrid PSO and K-Means based clustering improves document relevancy.…”

Section: Related Workmentioning

confidence: 99%

Semantic Clustering of Web Documents

Avanija¹,

Ramar

2012

International Journal of Information Technology and Web Engineering

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With the massive growth and large volume of the web it is very difficult to recover results based on the user preferences. The next generation web architecture, semantic web reduces the burden of the user by performing search based on semantics instead of keywords. Even in the context of semantic technologies optimization problem occurs but rarely considered. In this paper document clustering is applied to recover relevant documents. The authors propose an ontology based clustering algorithm using semantic similarity measure and Particle Swarm Optimization (PSO), which is applied to the annotated documents for optimizing the result. The proposed method uses Jena API and GATE tool API and the documents can be recovered based on their annotation features and relations. A preliminary experiment comparing the proposed method with K-Means shows that the proposed method is feasible and performs better than K-Means.

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An Ontology Based Model for Document Clustering

Cited by 16 publications

References 50 publications

Agent for Documents Clustering using Semantic-based Model and Fuzzy

Agent for Documents Clustering using Semantic-based Model and Fuzzy

What is the Conversation About?

Semantic Clustering of Web Documents

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