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.

Contents Introduction Literature Review Proposed Methodology Results Problem Statement Research Questions Research Objectives Literature Review Proposed Methodology Results Conclusion Readablility is most important in your presentation. All text and colors should be readable and should be uniformly balanced in size.

Introduction

Introduction Important Figures 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. A. Dey, Understanding and using context, Personal Ubiquitous Computing. 5 (1) (2001) 4–7.

Introduction Cont. References Tables 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.  - 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.

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

Literature Review

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 (Mondal et al. 2017) MediConceptNet: An Affinity Score Based Medical Concept Network. Automated, Medical Concepts Modeling in ConceptNet (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 Partial, (Combination of Query terms formulate a context)

Methodology

References 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.

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