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

Empirical analysis of impacts of instance-driven changes in ontologies

Changes in the characterization of instances in digital contents are one of the rationales to change or evolve ontologies which support the domain. These changes can impacts on one or more of interrelated ontologies. Before implementing changes, their impact on the target ontology, other dependent ontologies or dependent systems should be analysed. We investigate three concerns for the determination of impacts of changes in ontologies: representation of changes to ensure minimum impact, impact determination and integrity determination. Key elements of our solution are the operationalization of change operations to minimize impacts, a parameterization approach for the determination of impacts, a categorization scheme for identified impacts, and prioritization technique for change operations based on the severity of impacts

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

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

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

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

Is your cultural heritage collection AI ready? A methodology for semantic enrichment of cultural images and photographs.

The recent advancement in Artificial Intelligence (AI) has paved the way for the wide adoption of new tools and techniques in numerous disciplines. Galleries, Libraries, Archives, and Museums (GLAMs) are adopting AI-based solutions to efficiently organise, analyse, and utilise their digital collections. The application of AI-based solutions in GLAMs is mainly based on the foundational work librarians, archivists and museologists did in digitising their collections in a machine-readable format. Following the digitisation effort, the organisation of the digitised resources by integrating metadata that provides useful information to properly utilise the resources paved the way for the application of AI solutions. Nowadays, GLAMs have started exploiting the technology in digital image processing, semantic enrichment, and interlinking of historical and cultural collections including images, photographs, drawings, sketches and other archival collections. To efficiently utilise these AI solutions and assist non-technical experts who are working in GLAMs, a methodology that works not only for AI experts but also for all stakeholders is a necessary condition. In this paper, I discuss a methodology that has been used in projects that are dedicated to the organisation of cultural heritage collections using AI-based solutions. The methodology has three phases: the preparation phase focuses on domain understanding, acquisition of target collection, and ontology selection; the analysis phase focuses on semantic enrichment (annotation) and knowledge graph generation; the deployment and exploration phase focuses on focuses on the implementation of the solutions and exploitation of the semantically enriched AI-Ready resources using the AI-based solutions. This paper will further present two case studies where the methodology is applied and presents the lessons learned from the two projects

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

Change impact analysis for evolving ontology-based content management

Ontologies have become ubiquitous tools to embed semantics into content and applications on the semantic web. They are used to define concepts in a domain and allow us to reach at a common understanding on subjects of interest. Ontologies cover wide range of topics enabling both humans and machines to understand meanings and to reason in different contexts. They cover topics such as semantic web, artificial intelligence, information retrieval, machine translation, software development, content management, etc. We use ontologies for semantic annotation of content to facilitate understandability of the content by humans and machines. However, building ontology and annotations is often a manual process which is error prone and time consuming. Ontologies and ontology-driven content management systems (OCMS) evolve due to a change in conceptualization, the representation or the specification of the domain knowledge. These changes are often immense and frequent. Implementing the changes and adapting the OCMS accordingly require a huge effort. This is due to complex impacts of the changes on the ontologies, the content and dependent applications. Thus, evolving the OCMS with minimum and predictable impacts is among the top priorities of evolution in OCMS. We approach the problem of evolution by proposing a framework which clearly represents the interactions of the components of an OCMS. We proposed a layered OCMS framework which contains an ontology layer, content layer and annotation layer. Further, we propose a novel approach for analysing impacts of change operations. Impacts of atomic change operations are assigned individually by analysing the target entity and all the other entities that are structurally or semantically dependent on it. Impacts of composite change operations are analysed following three stage process. We use impact cancellation, impact balancing and impact transformation to analyse the impacts when two or more atomic changes are executed as part of a composite or domain specific change operation. We build a model which estimates the impacts of a complete change operation enabling the ontology engineer to specify the weight associated with each optimization criteria. Finally, the model identifies the implementation strategy with minimum cost of evolution. We evaluate our system by building a prototype as a proof of concept and find out encouraging results