Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments

Knowledge engineering relies on ontologies, since they provide formal descriptions of real-world knowledge. However, ontology development is still a nontrivial task. From the view of knowledge engineering, ontology learning is helpful in generating ontologies semi-automatically or automatically from scratch. It not only improves the efficiency of the ontology development process but also has been recognized as an interesting approach for extending preexisting ontologies with new knowledge discovered from heterogenous forms of input data. Driven by the great potential of ontology learning, we present an automatic ontology-based model evolution approach to account for highly dynamic environments at runtime. This approach can extend initial models expressed as ontologies to cope with rapid changes encountered in surrounding dynamic environments at runtime. The main contribution of our presented approach is that it analyzes heterogeneous semi-structured input data for learning an ontology, and it makes use of the learned ontology to extend an initial ontology-based model. Within this approach, we aim to automatically evolve an initial ontology-based model through the ontology learning approach. Therefore, this approach is illustrated using a proof-of-concept implementation that demonstrates the ontology-based model evolution at runtime. Finally, a threefold evaluation process of this approach is carried out to assess the quality of the evolved ontology-based models. First, we consider a feature-based evaluation for evaluating the structure and schema of the evolved models. Second, we adopt a criteria-based evaluation to assess the content of the evolved models. Finally, we perform an expert-based evaluation to assess an initial and evolved models’ coverage from an expert’s point of view. The experimental results reveal that the quality of the evolved models is relevant in considering the changes observed in the surrounding dynamic environments at runtime.

Assembly method in GIS INTEGRO and its usage for solving of gravitational inverse problem

Various methods based on growing bodies are lately gaining attention in a context of inverse gravity problem that we call a family of “assembly methods”. A variant of method was adopted for GIS INTEGRO in original formulation that is fit for the problem of multiple bodies incorporated in an environment of varying density, in absolute densities (not density contrasts) that are however have to be a priori specified. Such formulation allowed the implementation of the method that is suitable for territory modeling in the regional scale. To workaround method’s instability a number of changes are proposed that consist of introduction of priority on atomic modifications, modification queue and assessment of model evolution instead of just the final result. The developed software allows processing of large grids (tens of millions of tiling elements) even on 5–8 year old desktops. Based on method approbation experience some insights and practice methods are presented. An application example is presented as part of work on modeling of Enisei-Khatanga regional depression territory.

Supplemental Material: Curved orogenic belts, back-arc basins, and obduction as consequences of collision at irregular continental margins

Detailed figures of the model evolution, as well as a short description of the methodology and parameters used.<br>

Supplemental Material: Curved orogenic belts, back-arc basins, and obduction as consequences of collision at irregular continental margins

Detailed figures of the model evolution, as well as a short description of the methodology and parameters used.<br>

Evaluation of a highly refined prediction model in knowledge-based volumetric modulated arc therapy planning for cervical cancer

Background and purpose To explore whether a highly refined dose volume histograms (DVH) prediction model can improve the accuracy and reliability of knowledge-based volumetric modulated arc therapy (VMAT) planning for cervical cancer. Methods and materials The proposed model underwent repeated refining through progressive training until the training samples increased from initial 25 prior plans up to 100 cases. The estimated DVHs derived from the prediction models of different runs of training were compared in 35 new cervical cancer patients to analyze the effect of such an interactive plan and model evolution method. The reliability and efficiency of knowledge-based planning (KBP) using this highly refined model in improving the consistency and quality of the VMAT plans were also evaluated. Results The prediction ability was reinforced with the increased number of refinements in terms of normal tissue sparing. With enhanced prediction accuracy, more than 60% of automatic plan-6 (AP-6) plans (22/35) can be directly approved for clinical treatment without any manual revision. The plan quality scores for clinically approved plans (CPs) and manual plans (MPs) were on average 89.02 ± 4.83 and 86.48 ± 3.92 (p < 0.001). Knowledge-based planning significantly reduced the Dmean and V18 Gy for kidney (L/R), the Dmean, V30 Gy, and V40 Gy for bladder, rectum, and femoral head (L/R). Conclusion The proposed model evolution method provides a practical way for the KBP to enhance its prediction ability with minimal human intervene. This highly refined prediction model can better guide KBP in improving the consistency and quality of the VMAT plans.