Commonsense knowledge is paramount to enable intelligent systems. Typically, it is characterized as being implicit and ambiguous, hindering thereby the automation of its acquisition. To address these challenges, this paper presents semantically enhanced models to enable reasoning through resolving part of commonsense ambiguity. The proposed models enhance in a knowledge graph embedding framework for knowledge base completion. Experimental results show the effectiveness of the new semantic models in commonsense reasoning.

First and foremost, I thank Allah, The Most Beneficent, The Most Merciful, for giving me the strength and patience to learn and work continually and complete this work. I would like to express my sincere gratitude to my advisor Prof. Erik Cambria for helping me in developing the necessary research skills, and for encouraging me to learn and explore different areas of research. I also would like to thank my co-advisor Dr. Zhang NengSheng for his invaluable guidance and suggestions. Thanks both for your continuous supervision through my master work and research. I would like to thank my lab mates and colleagues from our department for offering their precious help when needed. I owe a lot to my friends who helped me stay strong in the toughest times of all. A special thank you goes to Noor for her contentious encouragement, concern, and prayers along the whole Masters journey. Israa, thank you for your unconditional support, listening, offering me advice, and for the good laugh. I thank all my friends whom I met here at NTU especially Ahmed, and Shah. Indeed, my Master's journey would not be the same without having such an awesome company. Last but not least, I would like to express my deepest gratitude to my parents and my siblings for being my backbone in life, I will never be able to thank you enough!

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