Regulation, Competition, and Information

You know it is very hard after the Governor, State Bank, to make a presentation but I will try to do it in a very mundane way. You know the Regulatory Bodies specially in the Economic Sector in recent times. There has been a sort of resurgence, leaving aside the regulation of the financial sector, which has been doing very well. Our old memory of regulation is not so pleasant. Long ago, there used to be a transport Authority which used to dole out “Route Permits” as political favours, and there was you know fixation of Bus Fares not always based on economic considerations but based on arbitrariness. But luckily we have learnt a lot. First, we learnt that it is good to deregulate and I think the primary purpose of the present resurgence is to deregulate. You have a regulatory body to deregulate. Secondly as the finance Minister said yesterday himself that this is a new paradigm. The regulation now has a major ingredient of a development role and in Pakistan with the combination of licencing as necessary part of regulation, you are very effective in that role and it also genuinely provides an opportunity for a one window type of operation where you give a permission and you facilitate the type approvals and then you help them dealing with the local agencies. Although you have brief period for evaluation but the preliminary perception is that they are fairing better than our Industrial Development Corporations which were given the role to promote the Private Sector. Now in this regulatory field, the new entrant is the Pakistan Electronic Media Regulatory Authority. I will present you some salient features how it works. I am not raising any issues as such like an economist would do but my presentation will be more informatic and tell you that in the new Regulatory regime in Pakistan, where is the stress now. Focus on professionalism, transparency and community participation. I will use the slides.

Utilising ontology-based modelling for learning content management

Learning content management needs to support a variety of open, multi-format Web-based software applications. We propose multidimensional, model-based semantic annotation as a way to support the management of access to and change of learning content. We introduce an information architecture model as the central contribution that supports multi-layered learning content structures. We discuss interactive query access, but also change management for multi-layered learning content management. An ontology-enhanced traceability approach is the solution

Ontology-based domain modelling for consistent content change management

Ontology-based modelling of multi-formatted software application content is a challenging area in content management. When the number of software content unit is huge and in continuous process of change, content change management is important. The management of content in this context requires targeted access and manipulation methods. We present a novel approach to deal with model-driven content-centric information systems and access to their content. At the core of our approach is an ontology-based semantic annotation technique for diversely formatted content that can improve the accuracy of access and systems evolution. Domain ontologies represent domain-specific concepts and conform to metamodels. Different ontologies - from application domain ontologies to software ontologies - capture and model the different properties and perspectives on a software content unit. Interdependencies between domain ontologies, the artifacts and the content are captured through a trace model. The annotation traces are formalised and a graph-based system is selected for the representation of the annotation traces

Analyzing impacts of change operations in evolving ontologies

Ontologies evolve over time to adapt to the dynamically changing knowledge in a domain. The evolution includes addition of new entities and modification or deletion of obsolete entities. These changes could have impacts on the remaining entities and dependent systems of the ontology. In this paper, we address the impacts of changes prior to their permanent implementation. To this end, we identify possible structural and semantic impacts and propose a bottom-up change impact analysis method which contains two phases. The first phase focuses on analyzing impacts of atomic change operations and the second phase focuses on analyzing impacts of composite changes which include impact cancellation, balancing and transformation due to implementation of two or more atomic changes. This method provides crucial information on the impacts and could be used for selecting evolution strategies and conducting what-if analysis before evolving the ontologies

Dependency analysis in ontology-driven content-based systems

Ontology-driven content-based systems are content-based systems (ODCBS) that are built to provide a better access to information by semantically annotating the content using ontologies. Such systems contain ontology layer, annotation layer and content layer. These layers contain semantically interrelated and interdependent entities. Thus, a change in one layer causes many unseen and undesired changes and impacts that propagate to other entities. Before any change is implemented in the ODCBS, it is crucial to understand the impacts of the change on other ODCBS entities. However, without getting these dependent entities, to which the change propagates, it is difficult to understand and analyze the impacts of the requested changes. In this paper we formally identify and define relevant dependencies, formalizing them and present a dependency analysis algorithm. The output of the dependency analysis serves as an essential input for change impact analysis process that ensures the desired evolution of the ODCBS

Composite ontology change operators and their customizable evolution strategies

Change operators are the building blocks of ontology evolution. Elementary, composite and complex change operators have been suggested. While lower-level change operators are useful in terms of finegranular representation of ontology changes, representing the intent of change requires higher-level change operators. Here, we focus on higherlevel composite change operators to perform an aggregated task. We introduce composite-level evolution strategies. The central role of the evolution strategies is to preserve the intent of the composite change with respect to the user’s requirements and to reduce the change operational cost. Composite-level evolution strategies assist in avoiding the illegal changes or presence of illegal axioms that may generate inconsistencies during application of a composite change. We discuss few composite changes along with the defined evolution strategies as an example that allow users to control and customize the ontology evolution process

Graph-based discovery of ontology change patterns

Ontologies can support a variety of purposes, ranging from capturing conceptual knowledge to the organisation of digital content and information. However, information systems are always subject to change and ontology change management can pose challenges. We investigate ontology change representation and discovery of change patterns. Ontology changes are formalised as graph-based change logs. We use attributed graphs, which are typed over a generic graph with node and edge attribution.We analyse ontology change logs, represented as graphs, and identify frequent change sequences. Such sequences are applied as a reference in order to discover reusable, often domain-specific and usagedriven change patterns. We describe the pattern discovery algorithms and measure their performance using experimental result

A layered framework for pattern-based ontology evolution

The challenge of ontology-driven modelling of information components is well known in both academia and industry. In this paper, we present a novel approach to deal with customisation and abstraction of ontology-based model evolution. As a result of an empirical study, we identify a layered change operator framework based on the granularity, domain-specificity and abstraction of changes. The implementation of the operator framework is supported through layered change logs. Layered change logs capture the objective of ontology changes at a higher level of granularity and support a comprehensive understanding of ontology evolution. The layered change logs are formalised using a graph-based approach. We identify the recurrent ontology change patterns from an ontology change log for their reuse. The identified patterns facilitate optimizing and improving the definition of domain-specific change patterns