Ljiljana Stojanovic

User-Driven Ontology Evolution Management

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 well-structured ontology evolution process. In this paper we identify a possible six-phase 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.

Ontologies for enterprise knowledge management

Several challenges 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.

Consistent evolution of OWL ontologies

Support for ontology evolution is extremely important in ontology engineering and application of ontologies in dynamic environments. A core aspect in the evolution process is the to guarantee consistency of the ontology when changes occur. In this paper we discuss the consistent evolution of OWL ontologies. We present a model for the semantics of change for OWL ontologies, considering structural, logical, and user-defined consistency. We introduce resolution strategies to ensure that consistency is maintained as the ontology evolves.

KAON - Towards a Large Scale Semantic Web

The Semantic Web will bring structure to the content of Web pages, being an extension of the current Web, in which information is given a well-defined meaning. Especially within e-commerce applications, Semantic Web technologies in the form of ontologies and metadata are becoming increasingly prevalent and important. This paper introduce KAON - the Karlsruhe Ontology and Semantic WebTool Suite. KAON is developed jointly within several EU-funded projects and specifically designed to provide the ontology and metadata infrastructure needed for building, using and accessing semantics-driven applications on the Web and on your desktop.

Migrating data-intensive web sites into the Semantic Web

The Semantic Web is intended to enable machine processability of web content and seems to be a solution for many drawbacks of the current Web. It is based on metadata that describe the formal semantics of Web contents. We present a novel, integrated and automated approach for migrating data-intensive Web applications into the Semantic Web. This approach can be applied to a broad range of todays business Web sites.

Managing multiple and distributed ontologies on the Semantic Web

Abstract.In traditional software systems, significant attention is devoted to keeping modules well separated and coherent with respect to functionality, thus ensuring that changes in the system are localized to a handful of modules. Reuse is seen as the key method in reaching that goal. Ontology-based systems on the Semantic Web are just a special class of software systems, so the same principles apply. In this article, we present an integrated framework for managing multiple and distributed ontologies on the Semant ic Web. It is based on the representation model for ontologies, trading off between expressivity and tractability. In our framework, we provide features for reusing existing ontologies and for evolving them while retaining the consistency. The approach is implemented within KAON, the Karlsruhe Ontology and Semantic Web tool suite.

An infrastructure for searching, reusing and evolving distributed ontologies

The vision of the Semantic Web can only be realized through proliferation of well-known ontologies describing different domains. To enable interoperability in the Semantic Web, it will be necessary to break these ontologies down into smaller, well-focused units that may be reused. Currently, three problems arise in that scenario. Firstly, it is difficult to locate ontologies to be reused, thus leading to many ontologies modeling the same thing. Secondly, current tools do not provide means for reusing existing ontologies while building new ontologies. Finally, ontologies are rarely static, but are being adapted to changing requirements. Hence, an infrastructure for management of ontology changes, taking into account dependencies between ontologies is needed. In this paper we present such an infrastructure addressing the aforementioned problems.

The role of ontologies in autonomic computing systems

The goal of IBMs autonomic computing strategy is to deliver information technology environments with improved self-management capabilities, such as self-healing, self-protection, self-optimization, and self-configuration. Data correlation and inference technologies can be used as core components to build autonomic computing systems. They can also be used to perform automated and continuous analysis of enterprise-wide event data based upon user-defined configurable rules, such as those intended for detecting threats or system failures. Furthermore, they may trigger corrective actions for protecting or healing the system. In this paper, we discuss the use of ontologies as a high-level, expressive, conceptual modeling approach for describing the knowledge upon which the processing of a correlation engine is based. The introduction of explicit models of state-based information technology resources into the correlation technology approach allows the construction of autonomic computing systems that are capable of dealing with policy-based goals on a higher abstraction level. We demonstrate some of the benefits of this approach by applying it to a particular IBM implementation, the eAutomation correlation engine.

Unveiling the Hidden Bride: Deep Annotation for Mapping and Migrating Legacy Data to the Semantic Web

Abstract The success of the Semantic Web crucially depends on the easy creation, integration, and use of semantic data. For this purpose, we consider an integration scenario that defies core assumptions of current metadata construction methods. We describe a framework of metadata creation where Web pages are generated from a database and the database owner is cooperatively participating in the Semantic Web. This leads us to the deep annotation of the database—directly by annotation of the logical database schema or indirectly by annotation of the Web presentation generated from the database contents. From this annotation, one may execute data mapping and/or migration steps, and thus prepare the data for use in the Semantic Web. We consider deep annotation as particularly valid because: (i) dynamic Web pages generated from databases outnumber static Web pages, (ii) deep annotation may be a very intuitive way to create semantic data from a database, and (iii) data from databases should remain where it can be handled most efficiently—in its databases. Interested users can then query this data directly or choose to materialize the data as RDF files.

Ontology evolution as reconfiguration-design problem solving

In this paper we present an approach to model ontology evolution as reconfiguration-design problem solving. The problem is reduced to a graph search where the nodes are evolving ontologies and the edges represent the changes that transform the source node into the target node. The search is guided by the constraints provided partially by a user and partially by a set of rules defining ontology consistency. In this way we allow a user to specify an arbitrary request declaratively and ensure its resolving. The approach is implemented in the KAON framework and the evaluation study shows its benefits.