1,720,967 research outputs found
Change impact analysis for evolving ontology-based content management
Full text linkOntologies 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
Empirical analysis of impacts of instance-driven changes in ontologies
Full text linkChanges 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
Composite ontology change operators and their customizable evolution strategies
Full text linkChange 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
Utilising ontology-based modelling for learning content management
Full text linkLearning 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
Layered change log model: bridging between ontology change representation and pattern mining
Full text linkTo date, no ontology change management system exists that records the ontology changes based on the different levels of granularity. Once changes are performed using elementary level change operations, they are recorded in the database at the elementary level accordingly. Such a change representation procedure is not sufficient to represent the intuition behind any applied change and thus, cannot capture the semantic impact of a change. In this paper, we discuss recording of the applied ontology changes in the form of a layered change log. We support the implementation of a layered change operator framework through layered change logs. We utilize the lower level ontology change log in two ways, i.e. recording of applied ontology changes (operational) and mining of higher level change patterns (analytical). The higher level change logs capture
the objective of the ontology changes at a higher level of granularity and support a comprehensive understanding of the ontology evolution. The knowledge-based change log facilitates the detection of similarities within different time series, mining of change patterns and reuse of knowledge.The layered change logs are formalised using a graph-based approach
Analyzing impacts of change operations in evolving ontologies
Full text linkOntologies 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
Ontology evolution for learning content management systems
Full text linkLearning content management systems have become common tools to support the development and delivery of digital learning content for many years. We investigate here how semantic annotation can support the management of change and evolution of learning content. We introduce a semantic information model that supports multi-layered learning content structures, based on which an ontology-based evolution framework is developed. Ontology-enhanced traceability approach is the solution
Ontology-based domain modelling for consistent content change management
Full text linkOntology-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
Ontology change management and identification of change patterns
Full text linkOntologies can support a variety of purposes,
ranging from capturing the 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.
In this sense, the application and representation
of ontology changes in terms of higher-level change operations
can describe more meaningful semantics behind
the applied change. In this paper, we propose a
fourphase process that covers the operationalization,
representation and detection of higherlevel changes in
ontology evolution life cycle. We present different levels
of change operators based on the granularity and
domainspecificity of changes. The first layer is based on
generic atomic level change operators, whereas the next
two layers are user-defined (generic/domainspecific) change
patterns. We introduce layered change logs for the explicit operational representation of ontology changes.
We formalised the change log using a graph-based approach.
We introduce a technique to identify composite
changes that not only assists in formulating ontology
change log data in a more concise manner, but
also helps in realizing the semantics and intent behind
any applied change. Furthermore, we identify frequent
change sequences that are applied as a reference in order
to discover reusable, often domainspecific and usagedriven
change patterns. We describe the pattern identification
algorithms and evaluate their performance
Graph-based discovery of ontology change patterns
Full text linkOntologies 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
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