Abstract. With rising importance of knowledge interchange, many industrial and academic applications have adopted ontologies as their conceptual backbone. However, industrial and academic environments are very dynamic, thus inducing changes to application requirements. To fulfill these changes, often the underlying ontology must be evolved as well. As ontologies grow in size, the complexity of change management increases, thus requiring a wellstructured ontology evolution process. In this paper we identify a possible sixphase evolution process and focus on providing the user with capabilities to control and customize it. We introduce the concept of an evolution strategy encapsulating policy for evolution with respect to user’s requirements. 1

The benefits of modular representations are well known from many areas of computer science. In this paper, we concentrate on the benefits of modular ontologies with respect to local containment of terminological reasoning. We define an architecture for modular ontologies that supports local reasoning by compiling implied subsumption relations.

exist related to applying ontologies in real-world environments. The authors present an integrated enterprise-knowledge management architecture, focusing on how to support multiple ontologies and manage ontology evolution. Ontologies for

This paper introduces a new logical formalism, intended for temporal conceptual modelling, as a natural combination of the well-known description logic DLR and point-based linear temporal logic with Since and Until. The expressive power of the resulting DLRUS logic is illustrated by providing a characterisation of the most important temporal conceptual modelling constructs appeared in the literature. We define a query language (where queries are non-recursive Datalog programs and atoms are complex DLRUS expressions) and investigate the problem of checking query containment under the constraints defined by DLRUS conceptual schemas---i.e., DLRUS knowledge bases---as well as the problems of schema satisfiability and logical implication.

Abstract: Ontologging is an ontology-driven environment to enable next generation knowledge management applications building on Semantic Web technology. In this paper we first present the conceptual architecture underlying Ontologging. Second, we focus on two important challenges for ontology-based knowledge management, namely the supporting multiple ontologies and managing ontology evolution. We will provide a general approach for handling these two essential issues within the Ontologging architecture. Key words: Knowledge management, ontology mapping, ontology evolution. 1.

In the first part of this Chapter we will introduce a general temporally enhanced conceptual data model able to represent time varying data, in the spirit of a temporally enhanced Entity-Relationship data model. In the second part, we will introduce an object-oriented conceptual data model enriched with schema change operators, which are able to represent the explicit temporal evolution of the schema while maintaining a consistent view on the (static) instantiated data. We will introduce a provably correct encoding of both conceptual data models and their inference problems in Description Logics. In this way, we study the properties of both the temporal conceptual data model and the object-oriented data model with schema change facilities.

Ontologies play a key role in the advent of the Semantic Web. An important problem when dealing with ontologies is the modification of an existing ontology in response to a certain need for change. This problem is a complex and multifaceted one, because it can take several different forms and includes several related subproblems, like heterogeneity resolution or keeping track of ontology versions. As a result, it is being addressed by several different, but closely related and often overlapping research disciplines. Unfortunately, the boundaries of each such discipline are not clear, as the same term is often used with different meanings in the relevant literature, creating a certain amount of confusion. The purpose of this paper is to identify the exact relationships between these research areas and to determine the boundaries of each field, by performing a broad review of the relevant literature.

In this paper a semantic approach for the specification and the management of databases with evolving schemata is introduced. It is shown how a general objectoriented model for schema versioning and evolution can be formalised; how the semantics of schema change operations can be defined; how interesting reasoning tasks can be supported, based on an encoding in Description Logics.

Abstract. The real-world knowledge management applications require administrative features such as versioning, fine-grained access control, and metainformation to be supported by the back-end infrastructure. Those features together with the needs of the ontology maintenance and development process raise the issue of tracking changes in knowledge bases. Part of the research presented is as basic as defining the rules of the game, the proper formal models to build upon – what to count as a change and what to ignore, how to represent and manage the tracking information. A number of more “technical ” issues such as tracking changes in imported and inferred knowledge are also discussed. The implementation is a part of the ontology middleware module developed under the On-To-Knowledge project

As with any other artifact produced as part of the software life cycle, software architectures evolve and this evolution must be managed. One approach to doing so would be to apply any of a host of existing configuration management systems, which have long been used successfully at the level of source code. Unfortunately, such an approach leads to many problems that prevent effective management of architectural evolution. To overcome these problems, we have developed an alternative approach centered on the use of an integrated architectural and configuration management system model. Because the system model combines architectural and configuration management concepts in a single representation, it has the distinct benefit that all architectural changes can be precisely captured and clearly related to each other---both at the fine-grained level of individual architectural elements and at the coarse-grained level of architectural configurations. To support the use of the system model, we have developed Mae, an architectural evolution environment through which users can specify architectures in a traditional manner, manage the evolution of the architectures using a check-out/check-in mechanism that tracks all changes, select a specific architectural configuration, and analyze the consistency of a selected configuration. We demonstrate the benefits of our approach by showing how the system model and its accompanying environment were used in the context of several representative projects.