Heiner Stuckenschmidt

C-OWL: contextualizing ontologies

Ontologies are shared models of a domain that encode a view which is common to a set of different parties. Contexts are local models that encode a partys subjective view of a domain. In this paper we show how ontologies can be contextualized, thus acquiring certain useful properties that a pure shared approach cannot provide. We say that an ontology is contextualized or, also, that it is a contextual ontology, when its contents are kept local, and therefore not shared with other ontologies, and mapped with the contents of other ontologies via explicit (context) mappings. The result is Context OWL (C-OWL), a language whose syntax and semantics have been obtained by extending the OWL syntax and semantics to allow for the representation of contextual ontologies.

Contextualizing ontologies

Ontologies are shared models of a domain that encode a view which is common to a set of different parties. Contexts are local models that encode a partys subjective view of a domain. In this paper, we show how ontologies can be contextualized, thus acquiring certain useful properties that a pure shared approach cannot provide. We say that an ontology is contextualized or, also, that it is a contextual ontology, when its contents are kept local, and therefore not shared with other ontologies, and mapped with the contents of other ontologies via explicit (context) mappings. The result is Context OWL (C-OWL), a language whose syntax and semantics have been obtained by extending the OWL syntax and semantics to allow for the representation of contextual ontologies.

Ontology alignment evaluation initiative: six years of experience

In the area of semantic technologies, benchmarking and systematic evaluation is not yet as established as in other areas of computer science, e.g., information retrieval. In spite of successful attempts, more effort and experience are required in order to achieve such a level of maturity. In this paper, we report results and lessons learned from the Ontology Alignment Evaluation Initiative (OAEI), a benchmarking initiative for ontology matching. The goal of this work is twofold: on the one hand, we document the state of the art in evaluating ontology matching methods and provide potential participants of the initiative with a better understanding of the design and the underlying principles of the OAEI campaigns. On the other hand, we report experiences gained in this particular area of semantic technologies to potential developers of benchmarking for other kinds of systems. For this purpose, we describe the evaluation design used in the OAEI campaigns in terms of datasets, evaluation criteria and workflows, provide a global view on the results of the campaigns carried out from 2005 to 2010 and discuss upcoming trends, both specific to ontology matching and generally relevant for the evaluation of semantic technologies. Finally, we argue that there is a need for a further automation of benchmarking to shorten the feedback cycle for tool developers.

Structure-based partitioning of large concept hierarchies

The increasing awareness of the benefits of ontologies for information processing has lead to the creation of a number of large ontologies about real-world domains. The size of these ontologies and their monolithic character cause serious problems in handling them. In other areas, e.g. software engineering, these problems are tackled by partitioning monolithic entities into sets of meaningful and mostly self-contained modules. In this paper, we suggest a similar approach for ontologies. We propose a method for automatically partitioning large ontologies into smaller modules based on the structure of the class hierarchy. We show that the structure-based method performs surprisingly well on real-world ontologies. We support this claim by experiments carried out on real-world ontologies including SUMO and the NCI cancer ontology. The results of these experiments are available online at http://swserver.cs.vu.nl/partitioning/.

Information Sharing on the Semantic Web

The large-scale and almost ubiquitous availability of information has become as much of a curse as it is a blessing. The more information is available, the harder it is to locate any particular piece of it. And even when it has been successfully found, it is even harder still to usefully combine it with other information we may already possess. This problem occurs at many different levels, ranging from the overcrowded disks of our own PCs to the mass of unstructured information on the World Wide Web. It is commonly understood that this problem of information sharing can only be solved by giving computers better access to the semantics of the information. While it has been recognized that ontologies play a crucial role in solving the open problems, most approaches rely on the existence of well-established data structures. To overcome these shortcomings, Stuckenschmidt and van Harmelen describe ontology-based approaches for resolving semantic heterogeneity in weakly structured environments, in particular the World Wide Web. Addressing problems like missing conceptual models, unclear system boundaries, and heterogeneous representations, they design a framework for ontology-based information sharing in weakly structured environments like the Semantic Web. For researchers and students in areas related to the Semantic Web, the authors provide not only a comprehensive overview of the State of the art, but also present in detail recent research in areas like ontology design for information integration, metadata generation and management, and representation and management of distributed ontologies. For professionals in areas such as e-commerce (e.g., the exchange of product knowledge) and knowledge management (e.g., in large and distributed organizations), the book provides decision support on the use of novel technologies, information about potential problems, and guidelines for the successful application of existing technologies.

Ontologies for geographic information processing

The development of geographical information systems (GIS) and the interoperability between these systems demands new requirements for the description of the underlying data. The exchange of data between GIS systems is problematic and often fails due to confusion in the meaning of concepts. The term semantic translator, a translator between GIS systems and/or catalogue systems which gives the user the option to map data between the systems is a topic of current research. This paper provides an overview of formal ontologies and how they can be used for geographical information processing. A description of an intelligent broker architecture for semantic-based information retrieval is introduced, and shows how this approach can be used for general purposes. In conclusion we attempt to provide a roadmap for the use of ontologies for geographic information processing.

Learning domain ontologies for semantic Web service descriptions

High quality domain ontologies are essential for successful employment of semantic Web services. However, their acquisition is difficult and costly, thus hampering the development of this field. In this paper we report on the first stage of research that aims to develop (semi-)automatic ontology learning tools in the context of Web services that can support domain experts in the ontology building task. The goal of this first stage was to get a better understanding of the problem at hand and to determine which techniques might be feasible to use. To this end, we developed a framework for (semi-)automatic ontology learning from textual sources attached to Web services. The framework exploits the fact that these sources are expressed in a specific sublanguage, making them amenable to automatic analysis. We implement two methods in this framework, which differ in the complexity of the employed linguistic analysis. We evaluate the methods in two different domains, verifying the quality of the extracted ontologies against high quality hand-built ontologies of these domains. Our evaluation lead to a set of valuable conclusions on which further work can be based. First, it appears that our method, while tailored for the Web services context, might be applicable across different domains. Second, we concluded that deeper linguistic analysis is likely to lead to better results. Finally, the evaluation metrics indicate that good results can be achieved using only relatively simple, off the shelf techniques. Indeed, the novelty of our work is not in the used natural language processing methods but rather in the way they are put together in a generic framework specialized for the context of Web services.

Index structures and algorithms for querying distributed RDF repositories

A technical infrastructure for storing, querying and managing RDFdata is a key element in the current semantic web development. Systems like Jena, Sesame or the ICS-FORTH RDF Suite are widelyused for building semantic web applications. Currently, none ofthese systems supports the integrated querying of distributed RDF repositories. We consider this a major shortcoming since the semanticweb is distributed by nature. In this paper we present an architecture for querying distributed RDF repositories by extending the existing Sesame system. We discuss the implications of our architectureand propose an index structure as well as algorithms forquery processing and optimization in such a distributed context.

Reasoning Support for Mapping Revision

Finding correct semantic correspondences between heterogeneous ontologies is one of the most challenging problems in the area of semantic web technologies. As manually constructing such mappings is not feasible in realistic scenarios, a number of automatic matching tools have been developed that propose mappings based on general heuristics. As these heuristics often produce incorrect results, a manual revision is inevitable in order to guarantee the quality of generated mappings. Experiences with benchmarking matching systems revealed that the manual revision of mappings is still a very difficult problem because it has to take the semantics of the ontologies as well as interactions between mappings into account. In this article, we propose methods for supporting human experts in the task of revising automatically created mappings. In particular, we present non-standard reasoning methods for detecting and propagating implications of expert decisions on the correctness of a mapping.

Semantic Web and Peer-to-Peer

Part I: Data Storage and Access. An RDF Query and Transformation Language. RDF and Traditional Query Architectures. Query Processing in RDF/S-based Peer-to-Peer Database Systems.- Part II: Querying the Network. Cayley DHTs. A Group-Theoretic Framework for Analyzing DHTs Based on Cayley Graphs. Semantic Query Routing in Unstructured Networks Using Social Metaphors. Expertise-Based Peer Selection. Personalized Information Access in a Bibliographic Peer-to-Peer System. Designing Semantic Publish/Subscribe Networks Using Super-Peers.- Part III: Semantic Integration. Semantic Coordination of Heterogeneous Classification Schemas. Semantic Mapping by Approximation. Satisficing Ontology Mapping. Scalable, Peer-Based Mediation across XML Schemas and Ontologies. Semantic Gossiping: Fostering Semantic Interoperability in Peer Data Management Systems.- Part IV: Methodology and Systems. A Methodology for Distributed Knowledge Management using Ontologies and Peer-to-Peer. Distributed Engineering of Ontologies. A Peer-to-Peer Solution for Distributed Knowledge Management. Xarop - A Semantic Peer-to-Peer System for a Virtual Organization. Bibster - A Semantics-Based Bibliographic Peer-to-Peer System.