@article {717,
	title = {Reasoning over RDF Knowledge Bases using Deep Learning},
	journal = {arXiv preprint arXiv:1811.04132},
	year = {2018},
	abstract = {<p>Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from knowledge expressed in such standards, is traditionally based on logical deductive methods and algorithms which can be proven to be sound and complete and terminating, i.e. correct in a very strong sense. For various reasons, though, in particular the scalability issues arising from the ever-increasing amounts of Semantic Web data available and the inability of deductive algorithms to deal with noise in the data, it has been argued that alternative means of reasoning should be investigated which bear high promise for high scalability and better robustness. From this perspective, deductive algorithms can be considered the gold standard regarding correctness against which alternative methods need to be tested. In this paper, we show that it is possible to train a Deep Learning system on RDF knowledge graphs, such that it is able to perform reasoning over new RDF knowledge graphs, with high precision and recall compared to the deductive gold standard.</p>
},
	url = {https://arxiv.org/abs/1811.04132},
	author = {Ebrahimi, Monireh and Md Kamruzzaman Sarker and Bianchi, Federico and Xie, Ning and Doran, Derek and Pascal Hitzler}
}
@article {636,
	title = {On the Challenges of Sentiment Analysis for Dynamic Events},
	journal = {IEEE Intelligent Systems},
	year = {2017},
	author = {Ebrahimi, Monireh and Yazdavar, Amir Hossein and Sheth, Amit}
}
@conference {637,
	title = {Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media},
	booktitle = {ASONAM},
	year = {2017},
	author = {Yazdavar, Amir Hossein and Al-Olimat, Hussein S and Ebrahimi, Monireh and Bajaj, Goonmeet and Banerjee, Tanvi and Thirunarayan, Krishnaprasad and Pathak, Jyotishman and Sheth, Amit}
}
@article {639,
	title = {Fuzzy Based Implicit Sentiment Analysis on Quantitative Sentences},
	journal = {Journal of Soft Computing and Decision Support Systems},
	volume = {3},
	year = {2016},
	pages = {7{\textendash}18},
	author = {Yazdavar, Amir Hossein and Ebrahimi, Monireh and Salim, Naomie}
}
@article {638,
	title = {Recognition of side effects as implicit-opinion words in drug reviews},
	journal = {Online Information Review},
	volume = {40},
	year = {2016},
	pages = {1018{\textendash}1032},
	author = {Ebrahimi, Monireh and Yazdavar, Amir Hossein and Salim, Naomie and Eltyeb, Safaa}
}
@article {642,
	title = {Analytical modeling and simulation of I{\textendash}V characteristics in carbon nanotube based gas sensors using ANN and SVR methods},
	journal = {Chemometrics and Intelligent Laboratory Systems},
	volume = {137},
	year = {2014},
	pages = {173{\textendash}180},
	author = {Akbari, Elnaz and Buntat, Zolkafle and Enzevaee, Aria and Ebrahimi, Monireh and Yazdavar, Amir Hossein and Yusof, Rubiyah}
}
@article {641,
	title = {Transmission of data with orthogonal frequency division multiplexing technique for communication networks using GHz frequency band soliton carrier},
	journal = {IET Communications},
	volume = {8},
	year = {2014},
	pages = {1364{\textendash}1373},
	author = {Amiri, IS and Ebrahimi, Monireh and Yazdavar, Amir Hossein and Ghorbani, S and Alavi, SE and Idrus, Sevia M and Ali, J}
}
@mastersthesis {643,
	title = {Side Effects Recognition as Implicit Opinion Words in Drug Reviews},
	year = {2013},
	school = {Universiti Teknologi Malaysia},
	type = {Master},
	author = {Ebrahimi, Monireh}
}
