Debugging Ontology Mappings: A Static Approach

Ontology mapping is the bottleneck in solving interoperation between Semantic Web applications using heterogeneous ontologies. Many mapping methods have been proposed in recent years, but in practice, it is still difficult to obtain satisfactory mapping results having high precision and recall. Different from existing methods, which focus on finding efficient and effective solutions for the ontology mapping problem, we place emphasis on analyzing the mapping result to detect/diagnose the mapping defects. In this paper, a novel technique called debugging ontology mappings is presented. During debugging, some types of mapping errors, such as redundant and inconsistent mappings, can be detected. Some warnings, including imprecise mappings or abnormal mappings, are also locked by analyzing the features of mapping result. More importantly, some errors and warnings can be repaired automatically or can be presented to users with revising suggestions. The experimental results reveal that the ontology debugging technique is promising, and it can improve the quality of mapping result

Connecting Software Metrics across Versions to Predict Defects

Accurate software defect prediction could help software practitioners allocate test resources to defect-prone modules effectively and efficiently. In the last decades, much effort has been devoted to build accurate defect prediction models, including developing quality defect predictors and modeling techniques. However, current widely used defect predictors such as code metrics and process metrics could not well describe how software modules change over the project evolution, which we believe is important for defect prediction. In order to deal with this problem, in this paper, we propose to use the Historical Version Sequence of Metrics (HVSM) in continuous software versions as defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN), a popular modeling technique, to take HVSM as the input to build software prediction models. The experimental results show that, in most cases, the proposed HVSM-based RNN model has a significantly better effort-aware ranking effectiveness than the commonly used baseline models

Conductance oscillation and quantization in monoatomic Al wires

We present first-principles calculations for the transport properties of monoatomic Al wires sandwiched between Al(100) electrodes. The conductance of the monoatomic Al wires oscillates with the number of the constituent atoms as a function of the wire length, either with a period of four-atom for wires with the typical interatomic spacing or a period of six-atom with the interatomic spacing of the bulk fcc aluminum, indicating a dependence of the period of conductance oscillation on the interatomic distance of the monoatomic Al wires