Semantic impact - a novel approach for domain concept selection in ontology learning

One of the remaining challenges of Ontology Learning (OL) is the significant dependence on human interference to decide which of the “learnt” concepts from a training corpus are relevant and/or important to the domain of discourse. Though part of this challenge is deeply rooted in expert knowledge of the application domain, there is no doubt that a good relevance/importance measure with which concepts can be semantically judged serves as a good enhancement to the OL weaponry. A new measure called “Semantic Impact” (SI) is, therefore, proposed to bridge between explicitly defined formal semantics (in the form of ontologies) and the distributional semantics learnt from a vast amount of data. SI aims to consistently and objectively quantify the semantic importance of a concept by aggregating two different measures: informativeness of a concept and its connectivity (or correlation) with the other concepts. Furthermore, it has been evaluated through two experiments. The first experiment was conducted within the news domain – using 200 BBC News articles about Donald Trump (between February 2017 and September 2017) to semantically assess the impact of the concepts identified from the corpus/corpora. This experiment successfully learnt, for example, the Date concept is one of the most important concepts in the News domain, even if it has not been included in the BBC Core Concept ontology. The second experiment was conducted within the biological area – using 2000 documents from PubMed on “Candida” to determine which diseases are more “semantic impact” in the Candida domain knowledge. The results are promising. The proposed system has identified that the most correlated (connected) concept to Disease_D003645 (Sudden Death) is Disease_D003643 (Death) without any pre-defined knowledge (or symbolic processing of such labels). Furthermore, a semantic analogy has been identified between Disease_D008223 (Lymphoma) and Disease_D008228 (Non-Hodgkin Lymphoma) due to a close SI between the two concepts. In addition, we have systematically evaluated the result from various angles and demonstrated that each component within the SI can produce a good and consistent result. At the macro-level, the overall SI result shows a strong clustering trend. At the micro-level, the SI results for both semantically important and non-important concepts are reasonable and reproducible. Moreover, we have compared it with a contemporary mainstream method to show the advantages of the SI algorithm together with its reproducibility

Hepatitis C Virus Induced a Novel Apoptosis-Like Death of Pancreatic Beta Cells through a Caspase 3-Dependent Pathway

Epidemiological and experimental studies have suggested that Hepatitis C virus (HCV) infection is associated with the development of type 2 diabetes. Pancreatic beta cell failure is central to the progression of type 2 diabetes. Using virus infection system, we investigate the influence of HCV infection on the fate of the insulinoma cell line, MIN6. Our experiments demonstrate that the HCV virion itself is indispensable and has a dose- and time-dependent cytopathic effect on the cells. HCV infection inhibits cell proliferation and induces death of MIN6 cells with apoptotic characteristics, including cell surface exposure of phosphatidylserine, decreased mitochondrial membrane potential, activation of caspase 3 and poly (ADP-ribose) polymerase, and DNA fragmentation in the nucleus. However, the fact that HCV-infected cells exhibit a dilated, low-density nucleus with intact plasma and nuclear membrane indicates that a novel apoptosis-like death occurs. HCV infection also causes endoplasmic reticulum (ER) stress. Further, HCV RNA replication was detected in MIN6 cells, although the infection efficiency is very low and no progeny virus particle generates. Taken together, our data suggest that HCV infection induces death of pancreatic beta cells through an ER stress-involved, caspase 3-dependent, special pathway

Research data related to Document-based Ontology (DbO) sets for the second experiment - 3000 in each corpus

The dataset supports the thesis "Semantic Impact - A novel approach for domain concept selection in ontology learning". Document-based Ontology (DbO) sets for the second experiment - 3000 in each corpus. Constructed by adding additional 2000 documents into the original 1000 corpus set. Folder X -> Target Corpus Folder Y -> Source Corpu

Research data related to DbO_Experiment1

The Document-based Ontology set for the first experiment

Research data related to NeuralNetwork-Experiment2

The dataset supports the thesis "Semantic Impact - A novel approach for domain concept selection in ontology learning". The neural network set trained in the second experiment (by the best NN structure identified as part of the experiment, which contains 3 hidden layers and 1500 nodes on each layer). The format is as follow: TrainedNet__ Source is indicated as Y Target is indicated as X NN is trained by using DeepLearning4J 1.0.0-beta3 and can be loaded with the following code: String strPath = "path to the NN file"; MultiLayerNetwork myNN = ModelSerializer.restoreMultiLayerNetwork(strPath); myNN.getLayerWiseConfigurations().setTrainingWorkspaceMode(WorkspaceMode.ENABLED)