Pascal Hitzler, Ph.D. (Advisor); Mateen M. Rizki, Ph.D. (Committee Member); Yong Pei, Ph.D. (Committee Member)
Master of Science (MS)
In recent years, the research in deep learning and knowledge engineering has made a wide impact on the data and knowledge representations. The research in knowledge engineering has frequently focused on modeling the high level human cognitive abilities, such as reasoning, making inferences, and validation. Semantic Web Technologies and Deep Learning have an interest in creating intelligent artifacts. Deep learning is a set of machine learning algorithms that attempt to model data representations through many layers of non-linear transformations. Deep learning is in- creasingly employed to analyze various knowledge representations mentioned in Semantic Web and provides better results for Semantic Web Reasoning and querying. Researchers at Data Semantic Laboratory(DaSe lab) have developed a method to train a deep learning model which is based on End-to-End memory network over RDF knowl- edge graphs which can be able to perform reasoning over new RDF graph with the help of triple normalization with high precision and recall when compared to traditional deduc- tive algorithms. Researchers have also found out that its 40 times faster to train than the non-normalized model on a dataset which they have performed experiments on. They have created efficient model capable of transferring its reasoning ability ( by applying normal- ization ) from one domain to another without any re/pre-training or fine-tunning over new domain which constitutes Transfer learning. In this thesis, we are testing the transfer learning approach on the research which is done by Bassem Makni and James Hendler ”Deep Learning for Noise-tolerant RDFS reasoning”. The main limitation of their approach is that the training is done on a dataset that uses only on ontology for the inference. We found out that their approach is not suitable for Transfer Learning which will help to reason over different ontologies/domains.
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
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