Exploiting Ontology Recommendation Using Text Categorization Approach

Sarwar, Muhammad; Ahmed, Mansoor; Habib, Asad; Khalid, Muhammad Noman; Ali, Muhammad; Raza, Mohsin; Hussain, Shahid; Ahmed, Ghufran

doi:10.1109/access.2020.3047364

Cited by 7 publications

(2 citation statements)

References 76 publications

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“…Reference [24] is an ontology recommendation framework that employs text categorization and unsupervised learning techniques. Explicit user requirements along with the ontology repository are the input to the framework.…”

Section: Related Workmentioning

confidence: 99%

A Personalized Ontology Recommendation System to Effectively Support Ontology Development by Reuse

Abdelreheim,

Soliman,

Klan

2023

Future Internet

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The profusion of existing ontologies in different domains has made reusing ontologies a best practice when developing new ontologies. The ontology reuse process reduces the expensive cost of developing a new ontology, in terms of time and effort, and supports semantic interoperability. Existing ontology development tools do not assist in the recommendation of ontologies or their concepts to be reused. Also, existing ontology recommendation tools could suggest whole ontologies covering a set of input keywords without referring to which parts of them (e.g., concepts) can be reused. In this paper, we propose an effective ontology recommendation system that helps the user in the iterative development and reuse of ontologies. The system allows the user to provide explicit preferences about the new ontology, and iteratively guides the user to parts from existing ontologies which match his preferences for reuse. Finally, we developed a prototype of our ontology recommendation system and conducted a user-based evaluation to assess the effectiveness of our approach.

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Section: Related Workmentioning

confidence: 99%

A Personalized Ontology Recommendation System to Effectively Support Ontology Development by Reuse

Abdelreheim,

Soliman,

Klan

2023

Future Internet

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“…Although there exist a number of linear classifiers, support vector machine has gained noticeable attention in semantic orientation of user-generated text [41]. It is widely used in text categorization, information retrieval and author profiling [42]. Linear classifiers work well for practical problems, such as text classification and document categorization.…”

Section: Supervised Learningmentioning

confidence: 99%

IoT-Based Pervasive Sentiment Analysis: A Fine-Grained Text Normalization Framework for Context Aware Hybrid Applications

Habib¹,

Raza²

2021

Information and Knowledge in Internet of Things

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The proliferation of Web 2.0 has altered the general styles of research and academia. The Internet of things (IoT) is one of the core IT fields in the current arena, as it transforms the physical objects into sensor-based virtual intelligent objects. There exist millions of physical objects that are interlinked with sensor-based devices in order to mimic the real-world intelligence. The IoT industry is taking benefit of big data sentiment classification in reshaping and improving the sensor-based integrated information processing systems. Sentiment analysis (SA) proved to be the backbone in orientation and betterment of IoT technology. Opinion mining is the problem of natural language processing (NLP) and field of data mining and machine learning. The goal of opinion mining is the identification of public sentiments, feeling and appraisals. Opinion mining is the significant research area in this new arena of web mining and text classification. It draws great attention in the last few years due to social media sites and their usage. Proliferation of internet applications has changed the public communication trends. Online users adopt social media sites for communication and collaboration. Most valuable communication channels are Twitter, Facebook, Tumbler and Plurk. There exist various classes of webbased application for communication, i.e. discussion forum, blogs, review sites and

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MR-AMFO-CNN: An intelligent recommendation system using optimized deep learning classifications

Cheriyan,

Chitra

2023

Int. j. inf. tecnol.

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Exploiting Ontology Recommendation Using Text Categorization Approach

Cited by 7 publications

References 76 publications

A Personalized Ontology Recommendation System to Effectively Support Ontology Development by Reuse

A Personalized Ontology Recommendation System to Effectively Support Ontology Development by Reuse

IoT-Based Pervasive Sentiment Analysis: A Fine-Grained Text Normalization Framework for Context Aware Hybrid Applications

MR-AMFO-CNN: An intelligent recommendation system using optimized deep learning classifications

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