Dr. Imran Ihsan - Ph.D in Knowledge Graphs, Explainable AI

In recent scientific advances, Artificial Intelligence and Natural Language Processing are the major contributors to classifying documents and extracting information. Classifying citations in different classes have gathered a lot of attention due to the large volume of citations available in different digital libraries. Typical citation classification uses sentiment analysis, where various techniques are applied to citations texts to mainly classify them in "Positive", "Negative" and "Neutral" sentiments. However, there can be innumerable reasons why an author selects another research for citation. Citations' Context and Reasons Ontology - CCRO uses a clear scientific method to articulate eight basic reasons for citing by using an iterative process of sentiment analysis, collaborative meanings, and experts' opinions. Using CCRO, this research paper adopts an ontology-based approach to extract citation's reasons and instantiate ontology classes and properties on two different corpora of citation sentences. One corpus of citation sentences is a publicly available dataset, while the other is our own manually curated. The process uses a two-step approach. The first part is an interface to manually annotate each citation text in the selected corpora on CCRO properties. A team of carefully selected annotators has annotated each citation to achieve a high inter-annotator agreement. The second part focuses on the automatic extraction of these reasons. Using Natural Language Processing, Mapping Graph, and Reporting Verb in a citation sentence, citation's reason is extracted and mapped onto a CCRO property. After comparing both manual and automatic mapping, accuracy is calculated. Based on experiments and results, accuracy is calculated for both publicly available and own corpora of citation sentences.

Keywords:

NLP-Based Citation Analysis; Qualitative Research Evaluation; Text Classification; Ontology

#10. VerbNet based Citation Sentiment Class Assignment using Machine Learning

Amjad, Z., & Ihsan, I, International Journal of Advanced Computer Science and Applications, Volume 11, pp: 621--627

2020
Citations: 4

10.14569/IJACSA.2020.0110973

HJRS: Y

#9. Efficiency Analysis of Collaborative Based Recommendation System

Khalid, W., Xing, X., Niu, Y., Tahir, O., & Ihsan, I, Jouranl of Physics: Conference Series, Volume 1646, pp: 012150

2020
Citations: 1

10.1088/1742-6596/1646/1/012150

HJRS: -

#8. CCRO: Citation's Context and Reasons Ontology

Ihsan, I., & Qadir, M. A., IEEE Access, Volume 7, pp: 30423--30436

2019
IF: 3.367
Citations: 7

10.1109/ACCESS.2019.2903450

HJRS: W

Abstract

Research papers can be visualized as a networked information space that contains a collection of information entities, inter-connected by directed links, commonly known as citation graph. There is a possibility to enrich the citation graph with meaningful relations using semantic tags. We have discovered and evaluated more than 150 citations' reasons from the existing published literature to be represented as citation tags. Many of these reasons have overlapped and diffused meanings. Annotating such a large volume of citation graphs with citation's reasons manually is nearly impossible, giving rise to a need to discover the citation's reasons automatically with high accuracy. The rst step towards this is developing a minimal set of citation's context and reasons that are disjoint in nature. It would be a great help to the reasoning system if these reasons are represented in a formal way in the form of an ontology. By adopting a wellde ned scientic methodology to formulate an ontology of citation reasons, we have reduced 150 reasons into only eight disjoint reasons, using an iterative process of sentiment analysis, collaborative meanings, and experts' opinions. Based on our ndings and experiments, we have proposed a citation's context and reasons ontology (CCRO) that provides abstract conceptualization required to organize citations' relations. CCRO has been veried, validated, and assessed by using the well-dened procedures and tools proposed in the literature for ontology evaluation. The results show that the proposed ontology is concise, complete, and consistent. For the instantiation and mapping of ontology classes on real data, we have developed a mapping graph among the verbs with predicative complements in the English Language, the verbs extracted from the selected corpus using the NLP and CCRO classes. Using this mapping graph, the mapping of ontology classes in each citation's sentiment is explained with a complete mapping on the selected dataset.

Keywords:

Citation Ontology; Citation Graphs; Citation Analysis; Ontology Development; Natural Language Processing; Computational Linguistics

#7. Using Academy Awards to Predict Success of Bollywood Movies using Machine Learning Algorithms

Masih, S., & Ihsan, I., International Journal of Advanced Computer Science and Applications, Volume 10, pp: 438--446

2019
Citations: 4

10.14569/IJACSA.2019.0100257

HJRS: Y

#6. Sentiment Based Study of Citations Reporting Verb Corpus Using Natural Language Processing

Ihsan, I., Imran, S., Tahir, O., & Qadir, M. A., Corporum: Journal of Corpus Linguistics, Volume 2, pp: 25--35

2019
Citations: 1

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HJRS: Y

#5. Intelligent Classification of Learning Objects using Information Content, Intra Document Terms and Domain Vocabulary

Ihsan, I., & Fayyaz, F, Journal of Software Engineering and Intelligent Systems, Volume 2, pp: 184--191

2017

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HJRS: -

#4. Making MOOC Slides Semantically Accessible: A Semantic Content Generation Tool for PowerPoint Slides

Ihsan, I., Journal of Distance Education and Research, Volume 2, pp: 40--52

2017
Citations: 1

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HJRS: -

#3. SPoint - A Semantic Based e-Learning Content Development Tools

Ihsan, I., Rehman, M., Ahmed, M. U., & Qadir, M. A., Journal of Applied Sciences, Volume 8, pp: 127--133

2008
IF: 0.19
Citations: 4

10.3923/JAS.2008.127.133

HJRS: Y

#2. Generic Multimedia Database Architecture Based upon Semantic Libraries

Hamid, O. A., Qadir, M. A., Iftikhar, N., Rehman, M., Ahmed, M. U., & Ihsan, I, Informatica, Volume 18, pp: 483--510

2007
IF: 2.688
Citations: 5

10.15388/Informatica.2007.190

HJRS: X

#1. Moving from Problem Space to Solution Space

Raja, B. S., Iqbal, M. A., & Ihsan, I, World Academy of Science, Engineering and Technology, International Journal of Industrial and Manufacturing Engineering, Volume 1, pp: 764--767

2007
Citations: 5

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HJRS: -

Google Scholar

Real-time citations: 73
h-index: 5
i10-index: 2

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Publications - Journal Articles

Connected Research: An Ontological Model for Scientific Discourse in Scholarly Knowledge Graphs

Abstract

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Improving In-Text Citation Reason Extraction and Classification using Supervised Machine Learning Techniques

Abstract

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Reason to Cite: A Qualitative Analysis of Citations' Context and Reasons

Abstract

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SEAL: Semantically Enriched Authoring in LaTeX - A Model for Scientific Discourse

Abstract

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An NLP-Based Citation Reason Analysis using CCRO

Abstract

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VerbNet based Citation Sentiment Class Assignment using Machine Learning

Abstract

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Efficiency Analysis of Collaborative Based Recommendation System

Abstract

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CCRO: Citation's Context and Reasons Ontology

Abstract

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Using Academy Awards to Predict Success of Bollywood Movies using Machine Learning Algorithms

Abstract

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Sentiment Based Study of Citations Reporting Verb Corpus Using Natural Language Processing

Abstract

Keywords:

Intelligent Classification of Learning Objects using Information Content, Intra Document Terms and Domain Vocabulary

Abstract

Keywords:

Making MOOC Slides Semantically Accessible: A Semantic Content Generation Tool for PowerPoint Slides

Abstract

Keywords:

SPoint - A Semantic Based e-Learning Content Development Tools

Abstract

Keywords:

Generic Multimedia Database Architecture Based upon Semantic Libraries

Abstract

Keywords:

Moving from Problem Space to Solution Space

Abstract

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