This is ms template of synopsis.

1. Title {Calibri 36 Font Size}
Student Name
Degree Program {MS(CS)/PhD(CS)}
Supervisor Supervisor Name Here
University Institute of Information Technology,
Pir Mehr Ali Shah Arid Agriculture University Rawalpindi.

2. Contents
• Introduction
– Problem Statement
– Research Questions
– Research Objectives
• Literature Review
• Proposed Methodology
• Results
• Conclusion
2

3. INTRODUCTION
3

4. • Important
– Text Readability is the most important thing. All text
should be uniformly balanced among all slides.
(Dey,2001). Apply spell checker on all slides.
• Figures
– Figures should be clear and readable. All figures
should be labeled properly. Please do not resize
images unevenly. A figure should be resized from both
width and height.
– These are some guidelines. It should not be treated as
instructions or rules. Consult your supervisor for
details.
Introduction
4
A. Dey, Understanding and using context, Personal Ubiquitous Computing. 5 (1) (2001) 4–7.

5. • References
– Only most important references would be
provided as footnote on a given slide. However,
referencing guidelines should be consulted with
supervisor.
• Tables
– Tables can be overflowed on different slides. Text
within tables should be readable. Please don’t not
skew text so much that it become unreadable.
Introduction Cont.
5
- Lenat, Douglas B. 1995. “CYC: A Large-Scale Investment in Knowledge Infrastructure.” Commun. ACM 38(11): 33–38.
- Suchanek, Fabian M., Gjergji Kasneci, and Gerhard Weikum. 2008. “YAGO: A Large Ontology from Wikipedia and WordNet.” Web
Semantics: Science, Services and Agents on the World Wide Web 6(3): 203–17.
- Fellbaum, Christiane. 2012. WordNet. The Encyclopedia of Applied Linguistics. MIT Press.

6. 6
Knowledge Base Taxonomy
Towards knowledge modeling and manipulation technologies: A survey, International Journal of Information Management
Volume 36, Issue 6, Part A, December 2016, Pages 857–871

7. LITERATURE REVIEW
7

8. 8
Literature Review Cont. (KB Modelling Techniques)
State-of-Art Design Approach Property Meaning Associations
Modeled
Context
Disambiguation
(Chou, Tsai, and Hsu
2017)
Context-Aware
Sentiment Propagation
Using LDA Topic
Modeling on Chinese
ConceptNet
Automated,
Sentiment Weight
Assignment of
Chinese ConceptNet
No No No
(Mondal et al. 2017)
MediConceptNet: An
Affinity Score Based
Medical Concept
Network.
Automated, Medical
Concepts Modeling
in ConceptNet
No No No
(Chowdhury,
Tandon, and
Weikum 2016)
Know2Look:
Commonsense
Knowledge for Visual
Search.
Query Based Image
retrieval, Query is
mapped to a
commonsense
knowledge
i.e.ConceptNet
Yes No Partial,
(Combination of
Query terms
formulate a context)

9. METHODOLOGY
9

10. • Auer, Sören et al. 2007. “DBpedia: A Nucleus for a Web of Open Data.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in
Artificial Intelligence and Lecture Notes in Bioinformatics), eds. Karl Aberer et al. Berlin, Heidelberg: Springer Berlin Heidelberg, 722–35.
http://dx.doi.org/10.1007/978-3-540-76298-0_52.
• Bollacker, Kurt et al. 2008. “Freebase: A Collaboratively Created Graph Database for Structuring Human Knowledge.” In Proceedings of the 2008 ACM
SIGMOD International Conference on Management of Data, SIGMOD ’08, New York, NY, USA: ACM, 1247–50.
http://doi.acm.org/10.1145/1376616.1376746.
• Di Caro, Luigi, Alice Ruggeri, Loredana Cupi, and Guido Boella. 2015. “Common-Sense Knowledge for Natural Language Understanding: Experiments
in Unsupervised and Supervised Settings.” In Congress of the Italian Association for Artificial Intelligence, , 233–45.
• Chou, Po-Hao, Richard Tzong-Han Tsai, and Jane Yung-jen Hsu. 2017. “Context-Aware Sentiment Propagation Using LDA Topic Modeling on Chinese
ConceptNet.” Soft Computing 21(11): 2911–21.
• Chowdhury, Sreyasi Nag. 2016. “Commonsense for Making Sense of Data.” In PhD@ VLDB,.
• Chowdhury, Sreyasi Nag, Niket Tandon, and Gerhard Weikum. 2016. “Know2Look: Commonsense Knowledge for Visual Search.” In AKBC@ NAACL-
HLT, , 57–62.
• Fellbaum, Christiane. 2012. WordNet. The Encyclopedia of Applied Linguistics. MIT Press.
• Havasi, Catherine, Robert Speer, and Jason Alonso. 2007. “ConceptNet 3: A Flexible, Multilingual Semantic Network for Common Sense Knowledge.”
In Recent Advances in Natural Language Processing, Borovets, Bulgaria.
• Krawczyk, Marek, Rafal Rzepka, and Kenji Araki. 2015. “Populating ConceptNet Knowledge Base with Information Acquired from Japanese
Wikipedia.” In Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on, , 2985–89.
• Lenat, Douglas B. 1995. “CYC: A Large-Scale Investment in Knowledge Infrastructure.” Commun. ACM 38(11): 33–38.
http://doi.acm.org/10.1145/219717.219745.
• Mondal, Anupam, Erik Cambria, Dipankar Das, and Sivaji Bandyopadhyay. 2017. “MediConceptNet: An Affinity Score Based Medical Concept
Network.”
• Rzeniewicz, Jacek, and Julian Szymański. 2013. “Bringing Common Sense to WordNet with a Word Game.” In International Conference on
Computational Collective Intelligence, , 296–305.
• Suchanek, Fabian M., Gjergji Kasneci, and Gerhard Weikum. 2008. “YAGO: A Large Ontology from Wikipedia and WordNet.” Web Semantics: Science,
Services and Agents on the World Wide Web 6(3): 203–17. http://linkinghub.elsevier.com/retrieve/pii/S1570826808000437 (January 20, 2014).
• Tandon, Niket et al. 2016. “Commonsense in Parts: Mining Part-Whole Relations from the Web and Image Tags.” In AAAI, , 243–50.
• Tandon, Niket, Gerard de Melo, Fabian Suchanek, and Gerhard Weikum. 2014. “Webchild: Harvesting and Organizing Commonsense Knowledge from
the Web.” In Proceedings of the 7th ACM International Conference on Web Search and Data Mining, , 523–32.
10
References

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