Anne Schlicht

Ontology modularization for knowledge selection: experiments and evaluations

Problems with large monolithical ontologies in terms of reusability, scalability and maintenance have led to an increasing interest in modularization techniques for ontologies. Currently, existing work suffers from the fact that the notion of modularization is not as well understood in the context of ontologies as it is in software engineering. In this paper, we experiment on applying state-of-the-art tools for ontology modularization in the context of a concrete application: the automatic selection of knowledge components to be used for Web page annotation and semantic browsing. We conclude that, in a broader context, an evaluation framework is required to guide the choice of a modularization tool, in accordance with the requirements of the considered application.

Criteria and Evaluation for Ontology Modularization Techniques

While many authors have argued for the benefits of applying principles of modularization to ontologies, there is not yet a common understanding of how modules are defined and what properties they should have. In the previous section, this question was addressed from a purely logical point of view. In this chapter, we take a broader view on possible criteria that can be used to determine the quality of a modules. Such criteria include logic-based, but also structural and application-dependent criteria, sometimes borrowing from related fields such as software engineering. We give an overview of possible criteria and identify a lack of application-dependent quality measures. We further report some modularization experiments and discuss the role of quality criteria and evaluation in the context of these experiments.

Structure-Based Partitioning of Large Ontologies

In this chapter we describe a method for structure-based ontology partitioning and its implementation that is practically applicable to very large ontologies. We show that a modularization based on structural properties of the ontology only already results in modules that intuitively make sense. The method was used for creating an overview graph for ontologies and for extracting key topics from an ontology that correspond to topics selected by human experts. Because the optimal modularization of an ontology greatly depends on the application it is used for, we implemented the partitioning algorithm in a way that allows for adaption to different requirements. Furthermore this adaption can be performed automatically by specifying requirements of the application.

Distributed Resolution for Expressive Ontology Networks

The Semantic Web is commonly perceived as a web of partially interlinked machine readable data. This data is inherently distributed and resembles the structure of the web in terms of resources being provided by different parties at different physical locations. A number of infrastructures for storing and querying distributed semantic web data, primarily encoded in RDF have been developed but almost all the work on description logic reasoning as a basis for implementing inference in the Web Ontology Language OWL still assumes a centralized approach where the complete terminology has to be present on a single system and all inference steps are carried out on this system. We propose a distributed reasoning method that preserves soundness and completeness of reasoning under the original OWL import semantics. The method is based on resolution methods for

A Flexible Partitioning Tool for Large Ontologies

\mathcal{ALCHIQ}

PEER-TO-PEER REASONING FOR INTERLINKED ONTOLOGIES

ontologies that we modify to work in a distributed setting. Results show a promising runtime decrease compared to centralized reasoning and indicate that benefits from parallel computation trade off the overhead caused by communication between the local reasoners.

Towards Distributed Ontology Reasoning for the Web

The benefits of modular ontologies in terms of easier creation and maintenance as well as better computational properties have been recognized by different researchers. As most real world ontologies, however, are still designed in a monolithic way, there is a need for methods that partition an existing ontology into a set of modules. Currently, existing work suffers from the fact that the notion of modularization is not as well understood in the context of ontologies as it is in software engineering. In this paper we present a flexible partitioning tool for large ontologies that can be adapted to the needs of different applications based on criteria that the resulting modular ontology should satisfy.

Constructs Replacing and Complexity Downgrading via a Generic OWL Ontology Transformation Framework

The Semantic Web is commonly perceived as a web of partially-interlinked machine readable data. This data is inherently distributed and resembles the structure of the web in terms of resources being provided by different parties at different physical locations. A number of infrastructures for storing and querying distributed semantic web data, primarily encoded in RDF have been developed. While there are first attempts for integrating RDF Schema reasoning into distributed query processing, almost all the work on description logic reasoning as a basis for implementing inference in the Web Ontology Language OWL still assumes a centralized approach where the complete terminology has to be present on a single system and all inference steps are carried out on this system.We have designed and implemented a distributed reasoning method that preserves soundness and completeness of reasoning under the original OWL import semantics and has beneficial properties regarding parallel computation and overhead caused by communication effort and additional derivations. The method is based on sound and complete resolution methods for the description logic ALC that we modify to work in a distributed setting.

Towards structural criteria for ontology modularization

The use of description logics as one of the primary logical languages for knowledge representation on the Web has created new challenges with respect to reasoning in these logics. In order to support the vision of a semantic Web of interrelated ontologies, reasoning procedures have to be highly scalable and able to deal with physically distributed knowledge models. A natural way of addressing these problems is to rely on distributed inference procedures that can distribute the load between different solvers, thus reducing potential bottlenecks both in terms of memory and computation time. In this paper, we propose a distributed resolution approach that solves the problem by local resolution and propagation of derived axioms between different reasoners. The method is complete for first order logic, terminates for ALC ontologies and avoids duplication of axioms and inferences. The work can be seen as a building block for a large scale distributed reasoning infrastructure for the semantic Web as envisioned in recent activities such as the large knowledge collider (LarKC) project.

Distributed Resolution for ALC

Many of the tools supporting the OWL ontological language face complexity problems when handling certain constructs of the language. This leads to the requirement of automatically changing the ontology, either by removing a specific type of construct or by adhering (downgrading) the ontology to a predefined OWL2 profile such as OWL2 EL. We present an approach to construct replacing and complexity downgrading that relies on transformation patterns processed by a generic ontology transformation framework. Transformation patterns allow to declaratively formulate and transparently execute axiom replacement operations. This potentially preserves derivations that would otherwise be lost due to simple removal of problematic axioms.