A Hybrid Approach for Measuring Semantic Similarity between Documents and its Application in Mining the Knowledge Repositories

Sumathy, K. L.; Chidambaram,

doi:10.14569/ijacsa.2016.070831

Cited by 8 publications

(6 citation statements)

References 24 publications

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“…WordNet ontology integration provides more semantics to the clusters and thus allows it easier for users to search for more versatility [15]. WordNet is used to determine the document context, as it has been used widely and consistently to facilitate a better understanding of documents [16]. With its semantic associations of terms, it has been used widely to increase the quality of the cluster of the text [3].…”

Section: Resultsmentioning

confidence: 99%

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State of the art document clustering algorithms based on semantic similarity

Jacksi¹,

Salih²

2020

Jurnal Informatika

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Section: Resultsmentioning

confidence: 99%

“…The proposed method uses ontology and corpus in a second fold to evaluate the similarity of the document. The proposed method achieves high accuracy in the similarity to the document evaluation due to the hybrid approach [16].…”

Section: Literature Reviewmentioning

confidence: 99%

State of the art document clustering algorithms based on semantic similarity

Jacksi¹,

Salih²

2020

Jurnal Informatika

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“…This method is a mixture of several characteristics, namely: attribute similarity, ontology structure, information content and Lowest Common Ancestor (LCA) node depth. This method has been the subject of several studies, including [20] which proposed a hybrid method that combines term-based and concept-based methods. These measures concern structure and contextual relevance measures.…”

Section: Hybrid-based Methodsmentioning

confidence: 99%

An Information Content and Set of Common Superconcepts-Based Algorithm to Estimate Similarity between Concepts of Ontologies

Gbame,

Morie,

Brou

2023

OJAppS

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Ontologies have been used for several years in life sciences to formally represent concepts and reason about knowledge bases in domains such as the semantic web, information retrieval and artificial intelligence. The exploration of these domains for the correspondence of semantic content requires calculation of the measure of semantic similarity between concepts. Semantic similarity is a measure on a set of documents, based on the similarity of their meanings, which refers to the similarity between two concepts belonging to one or more ontologies. The similarity between concepts is also a quantitative measure of information, calculated based on the properties of concepts and their relationships. This study proposes a method for finding similarity between concepts in two different ontologies based on feature, information content and structure. More specifically, this means proposing a hybrid method using two existing measures to find the similarity between two concepts from different ontologies based on information content and the set of common superconcepts, which represents the set of common parent concepts. We simulated our method on datasets. The results show that our measure provides similarity values that are better than those reported in the literature.

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“…Precision, recall, and purity. Furthermore, K. and Chidambaram, 2016 [117] Presented A hybrid approach for the measurement of semantic similarity among documents. In information retrieval and text processing, Semantic similarity plays an important role.…”

Section: Related Workmentioning

confidence: 99%

Document Clustering in the Age of Big Data: Incorporating Semantic Information for Improved Results

Haji¹,

Al-Zebari²,

Şengür³

et al. 2023

JASTT

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There has been a meteoric rise in the total amount of digital texts as a direct result of the proliferation of internet access. As a direct result of this, document clustering has evolved into a crucial method that must be used in order to successfully extract relevant information from big document collections. When employing the document clustering approach, documents are automatically sorted into groups whose members have a high degree of similarity to one another. These groups are created by applying the document clustering technique. Because they do not take into account the semantic linkages that exist between the texts, traditional clustering approaches are unable to provide an acceptable description of a collection of texts. This is because traditional clustering techniques. Document clusters, in which texts are ordered according to their meaning rather than their use of keywords, have been extensively utilized as a means of overcoming these challenges as a result of the incorporation of semantic information. This has been possible as a result of the fact that document clusters can group together related texts. In this investigation, we looked at a total of 27 distinct papers that were published over the previous five years and categorized the documents based on the semantic similarities that existed between the various pieces. A detailed literature evaluation is included to each and every one of the publications that were selected for further consideration. Comparative research is carried out on a wide variety of evaluation strategies, including as algorithms, similarity metrics, instruments, and processes. Following that, there is a drawn-out discussion that analyzes the similarities and differences between the activities.

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A Hybrid Approach for Measuring Semantic Similarity between Documents and its Application in Mining the Knowledge Repositories

Cited by 8 publications

References 24 publications

State of the art document clustering algorithms based on semantic similarity

State of the art document clustering algorithms based on semantic similarity

An Information Content and Set of Common Superconcepts-Based Algorithm to Estimate Similarity between Concepts of Ontologies

Document Clustering in the Age of Big Data: Incorporating Semantic Information for Improved Results

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