ONTOLOGY MERGING USING PROTÉGÉ – A CASE STUDY

The exponential growth of data and the boom of online businesses necessitates the need for data to be machine-readable, as humans are no longer able to manually manage the vast amounts of data. Ontologies can define concepts and relations that are amenable to processing by machines. Ontologies are created in silos and pockets of domains, and the need to merge these resources is key to universal access to multi-domain knowledge. Merging of ontologies has been explored to an extent over the last two decades, and this paper explores the extent of the tools and techniques available with a case study of merging two ontologies which are publicly available, the Person ontology and Institutional ontology, using the latest tools available on the most popular ontology editor, Protégé. It is found that automated merging tools have not been improved much over the last two decades, and the most current merging tools provided combine the two ontologies into one but do not unite or merge any of the classes or axioms which are equivalent. This can be seen in the axiom count, which does not decrease in the merged ontology, showing that no similar classes or actual axioms were merged. Protégé plugins which used to provide the semi-automatic mapping of similar classes to assist the merging process were found to be no longer available, and manual mapping by the knowledge engineer was required. This supports further research in automated ontology merging techniques.

A Feature-Based Evaluation of Model Merge Methods for e-Health Solutions

Model-driven engineering (MDE) paradigm considers models as central artifacts for software development lifecycle during which models evolve. Developing an e-health solution using MDE poses challenges of model version control, model differencing and model merging, which requires appropriate software configuration management (SCM). In this paper we focus on model-driven merging, which refers to combining two or more versions of a model into a single consolidated version. SCM for model-driven merging leverages evolution of valid configurations, which is a highly desired behavior. Our investigation is based on the features that are required for model-driven SCM realization. Initially, we identify these features using which the existing model-driven merging techniques are evaluated. It is observed that though various proposals are made by academia and research community, a standard model-driven SCM solution that can cater to the needs of industry is still absent. This is in contrary to the situation of traditional SCM systems where standard solutions exist. We also present the usefulness of each technique along with the tradeoffs involved. Finally, guidelines are provided to select techniques appropriate for given circumstances.

A Feature-Based Evaluation of Model Merge Methods for e-Health Solutions

Model-driven engineering (MDE) paradigm considers models as central artifacts for software development lifecycle during which models evolve. Developing an e-health solution using MDE poses challenges of model version control, model differencing and model merging, which requires appropriate software configuration management (SCM). In this paper we focus on model-driven merging, which refers to combining two or more versions of a model into a single consolidated version. SCM for model-driven merging leverages evolution of valid configurations, which is a highly desired behavior. Our investigation is based on the features that are required for model-driven SCM realization. Initially, we identify these features using which the existing model-driven merging techniques are evaluated. It is observed that though various proposals are made by academia and research community, a standard model-driven SCM solution that can cater to the needs of industry is still absent. This is in contrary to the situation of traditional SCM systems where standard solutions exist. We also present the usefulness of each technique along with the tradeoffs involved. Finally, guidelines are provided to select techniques appropriate for given circumstances.

Exploring Tradeoffs in Merged Pipeline Infrastructure for Carbon Dioxide Integration Networks

Carbon integration aims to identify appropriate CO2 capture, allocation, and utilization options, given a number of emission sources and sinks. Numerous CO2-using processes capture and convert emitted CO2 streams into more useful forms. The transportation of captured CO2, which poses a major design challenge, especially across short distances. This paper investigates new CO2 transportation design aspects by introducing pipeline merging techniques into carbon integration network design. For this, several tradeoffs, mainly between compression and pipeline costs, for merged pipeline infrastructure scenarios have been studied. A modified model is introduced and applied in this work. It is found that savings on pipeline costs are greatly affected by compression/pumping levels. A case study using two different pipe merging techniques was applied and tested. Backward branching was reported to yield more cost savings in the resulting carbon network infrastructure. Moreover, both the source and sink pressures were found to greatly impact the overall cost of the carbon integration network attained via merged infrastructure. It was found that compression costs consistently decreased with increasing source pressure, unlike the pumping and pipeline costs.

A Tour of Reinforcement Learning: The View from Continuous Control

This article surveys reinforcement learning from the perspective of optimization and control, with a focus on continuous control applications. It reviews the general formulation, terminology, and typical experimental implementations of reinforcement learning as well as competing solution paradigms. In order to compare the relative merits of various techniques, it presents a case study of the linear quadratic regulator (LQR) with unknown dynamics, perhaps the simplest and best-studied problem in optimal control. It also describes how merging techniques from learning theory and control can provide nonasymptotic characterizations of LQR performance and shows that these characterizations tend to match experimental behavior. In turn, when revisiting more complex applications, many of the observed phenomena in LQR persist. In particular, theory and experiment demonstrate the role and importance of models and the cost of generality in reinforcement learning algorithms. The article concludes with a discussion of some of the challenges in designing learning systems that safely and reliably interact with complex and uncertain environments and how tools from reinforcement learning and control might be combined to approach these challenges.

A Study of Various Result Merging Strategies for a Meta Search Engine

As the amount of data is growing day by day, the sources for these data are also growing simultaneously and to search through this very data, we need the use of search engines. Since each search engine is limited to its confined set of data, it would be even better to make use of a Meta search engine which will give us more relevant results than the ones obtained from any single search engine. It acts as an interface that provides the user with a single view from the various underlying search engines. The data is collected from these underlying search engines after they are accessed with the processed query from the Meta search engine. The collected data is merged using an algorithm and the algorithm will be a major factor in giving the best possible results. In this paper, we are going to discuss about the various existing metasearch engines and the different merging techniques and their approaches.