Change Representation For OWL 2 Ontologies

Ontologies are entities that evolve over time; therefore it is essential to represent and manage changes to ontologies along with the ontologies themselves. In this paper we propose a change ontology for the OWL 2 ontology language. This change ontology comprises a fine-grained taxonomy of ontology changes that considers the lowest-level atomic operations that can be performed in an ontology, but in addition also on other abstraction levels (ontology entity, composite). It thus allows to describe on a fine grained level how an ontology has changed from one version to another, and it also provides the vocabulary to talk about the changes that enables, for instance, to associate provenance or other rich metadata, such as argumentation structures. Additionally, we discuss some useful applications of our change ontology and its technological support

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

Policies for role based agents in environments with changing ontologies

Software agents try to achieve the goals of roles that they have in an environment. It is supposed that the dynamic structure of role based agents can be connected with updatable domain ontologies of the environment. Ontology evolution can cause the update of agent behaviors or access restrictions to ontological elements. So regulation for the agent behaviors may be needed. Our motivation is to create a suitable policy model for agents, environments and organizations when ontologies in the environment can change

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

Layered change log model: bridging between ontology change representation and pattern mining

To 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

Represent Changes of Knowledge Organization Systems on the Semantic Web

Traditional knowledge organization systems (KOS) including thesauri, classification schemes, taxonomies, subject heading systems, name authorities, and other lists of terms and codes have been playing important roles in indexing, information organization, and retrieval. With the advent of the semantic web, a large number of them have been converted into Linked Open Data (LOD) datasets. Since the Simple Knowledge Organization Systems (SKOS) and SKOS eXtension for Labels (SKOS-XL) are languages for representation of knowledge organization systems, they have been applied to knowledge organization systems. In this article, the issues surrounding changes, versioning control, and evolution of KOS are investigated. From KOS services providers and consumers perspectives, this study focuses on representation of changes on the semantic web

A holistic approach to collaborative ontology development based on change management

This paper describes our methodological and technological approach for collaborative ontology development in inter-organizational settings. It is based on the formalization of the collaborative ontology development process by means of an explicit editorial workflow, which coordinates proposals for changes among ontology editors in a flexible manner. This approach is supported by new models, methods and strategies for ontology change management in distributed environments: we propose a new form of ontology change representation, organized in layers so as to provide as much independence as possible from the underlying ontology languages, together with methods and strategies for their manipulation, version management, capture, storage and maintenance, some of which are based on existing proposals in the state of the art. Moreover, we propose a set of change propagation strategies that allow keeping distributed copies of the same ontology synchronized. Finally, we illustrate and evaluate our approach with a test case in the fishery domain from the United Nations Food and Agriculture Organisation (FAO). The preliminary results obtained from our evaluation suggest positive indication on the practical value and usability of the work here presented

Ontology change management and identification of change patterns

Ontologies 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