Eva Blomqvist

The Semantic Web - ISWC 2011 - 10th International Semantic Web Conference, Bonn, Germany, October 23-27, 2011, Proceedings, Part I

The Semantic Web - ISWC 2011 - 10th International Semantic Web Conference, Bonn, Germany, October 23-27, 2011, Proceedings, Part I

Experiments on pattern-based ontology design

This paper addresses the evaluation of pattern-based ontology design through experiments. An initial method for reuse of content ontology design patterns (Content ODPs) was used by the participants during the experiments. Hypotheses considered include the usefulness of Content ODPs for ontology developers, and we additionally study in what respects they are useful and what open issues remain. The main positive conclusions when using Content ODPs include: ontology developers perceived them as useful, ontology quality is improved, coverage of the task increases, usability is improved, and common modelling mistakes can be avoided.

The use of Semantic Web technologies for decision support --a survey

The Semantic Web shares many goals with Decision Support Systems DSS, e.g., being able to precisely interpret information, in order to deliver relevant, reliable and accurate information to a user when and where it is needed. DSS have in addition more specific goals, since the information need is targeted towards making a particular decision, e.g., making a plan or reacting to a certain situation. When surveying DSS literature, we discover applications ranging from Business Intelligence, via general purpose social networking and collaboration support, Information Retrieval and Knowledge Management, to situation awareness, emergency management, and simulation systems. The unifying element is primarily the purpose of the systems, and their focus on information management and provision, rather than the specific technologies they employ to reach these goals. Semantic Web technologies have been used in DSS during the past decade to solve a number of different tasks, such as information integration and sharing, web service annotation and discovery, and knowledge representation and reasoning. In this survey article, we present the results of a structured literature survey of Semantic Web technologies in DSS, together with the results of interviews with DSS researchers and developers both in industry and research organizations outside the university. The literature survey has been conducted using a structured method, where papers are selected from the publisher databases of some of the most prominent conferences and journals in both fields Semantic Web and DSS, based on sets of relevant keywords representing the intersection of the two fields. Our main contribution is to analyze the landscape of semantic technologies in DSS, and provide an overview of current research as well as open research areas, trends and new directions. An added value is the conclusions drawn from interviews with DSS practitioners, which give an additional perspective on the potential of Semantic Web technologies in this field; including scenarios for DSS, and requirements for Semantic Web technologies that may attempt to support those scenarios.

Constructing an enterprise ontology for an automotive supplier

This paper presents experiences and conclusions from ontology engineering applied in the automotive suppliers domain. The work focuses on construction of enterprise ontologies to support structuring of enterprise information and knowledge management. Two methods for ontology construction, developed by previous research activities, were used in parallel when developing an ontology for a company in automotive supplier industries. One method is automatic and the other method is a manual approach. A conclusion was that the developed ontologies complemented each other well and therefore the decision was made to merge them for use in the project. The resulting ontology will now be used in several pilot applications.

Experimenting with eXtreme design

Ontology Design Patterns (ODPs) support reusability and use of best practices in ontology engineering. Previous studies have shown that Content ODPs, in particular, have some measurable beneficial effects on the produced ontologies. However, another conclusion was that methodology and tool support was needed. Now such support exist, in the form of the XD methodology and the XD Tools. In this paper we present a set of experiments for (i) confirming previous conclusions concerning the usefulness of Content ODPs, (ii) investigating the usefulness of the XD methodology, and (iii) investigating the usefulness of the XD Tools. Main conclusions are that we can confirm most of the previous results concerning the usefulness of Content ODPs, and certain negative effects observed previously are now mitigated by the new tool support. The tool is perceived as quite useful, however it also adds some overhead. The XD methodology is found to be a helpful means to organize the design process, and the main benefit shown through the experiments is the testing focus, resulting in a drastic decrease of certain frequent mistakes.

Fully Automatic Construction of Enterprise Ontologies Using Design Patterns: Initial Method and First Experiences

The main contribution of this paper is an initial method for automatically exploiting ontology design patterns with the aim of further automating the creation of enterprise ontologies in small-scale application contexts. The focus is so far on developing a fully automated construction method, thereby somewhat reducing the requirements on ontology customization and level of detail. In this paper we present an approach how to use knowledge (patterns) from other areas, like data modeling, knowledge reuse, software analysis and software design, to create ontology patterns. These design patterns are then used within our method for automatically matching and pruning them, in accordance with information extracted from existing knowledge sources within the company in question. Though the method still needs some fine-tuning, it has already been used when creating an enterprise ontology for a supplier-company within the automotive industry.

Pattern-Based Ontology Design

In this chapter, we present ontology design patterns (ODPs), which are reusable modeling solutions that encode modeling best practices. ODPs are the main tool for performing pattern-based design of ontologies, which is an approach to ontology development that emphasizes reuse and promotes the development of a common “language” for sharing knowledge about ontology design best practices. We put specific focus on content ODPs (CPs) and show how they can be used within a particular methodology. CPs are domain-dependent patterns, the requirements of which are expressed by means of competency questions, contextual statements, and reasoning requirements. The eXtreme Design (XD) methodology is an iterative and incremental process, which is characterized by a test-driven and collaborative development approach. In this chapter, we exemplify the XD methodology for the specific case of CP reuse. The XD methodology is also supported by a set of software components named XD Tools, compatible with the NeOn Toolkit, which assist users in the process of pattern-based design.

OntoCase-Automatic Ontology Enrichment Based on Ontology Design Patterns

OntoCase is a framework for semi-automatic pattern-based ontology construction. In this paper we focus on the retain and reuse phases, where an initial ontology is enriched based on content ontology design patterns (Content ODPs), and especially the implementation and evaluation of these phases. Applying Content ODPs within semi-automatic ontology construction, i.e. ontology learning (OL), is a novel approach. The main contributions of this paper are the methods for pattern ranking, selection, and integration, and the subsequent evaluation showing the characteristics of ontologies constructed automatically based on ODPs. We show that it is possible to improve the results of existing OL methods by selecting and reusing Content ODPs. OntoCase is able to introduce a general top structure into the ontologies, and by exploiting background knowledge the ontology is given a richer overall structure.

Pattern ranking for semi-automatic ontology construction

When developing semantic applications, the construction of ontologies is a crucial part. We are developing a semiautomatic ontology construction approach, OntoCase, relying on ontology patterns as additional resources. A crucial part of this approach is how to select the appropriate patterns based on the input representation extracted from a text corpus. In this paper, we suggest a pattern ranking and selection approach with the ability to partially bridge the gap between abstract patterns and specific terms, as well as being specifically tuned to the characteristics of ontology patterns. Compared to existing ontology ranking schemes our approach adds indirect matching of terms as well as relation matching. An initial experiment indicates that OntoCase ranking performs better, especially when ranking small and abstract patterns, than existing ranking approaches.

OntoCase: a pattern-based ontology construction approach

As the technologies facilitating the Semantic Web become more and more mature they are also adopted by the business world. When developing semantic applications, constructing the underlying ontologies is a crucial part. Construction of enterprise ontologies need to be semi-automatic in order to reduce the effort required and the need for expert ontology engineers. Another important issue is to introduce knowledge reuse in the ontology construction process. By basing our semi-automatic method on the principles of case-based reasoning we envision a novel semi-automatic ontology construction process. The approach is based on automatic selection and application of patterns but also includes ontology evaluation and revision, as well as pattern candidate discovery. The development of OntoCase is still ongoing work, in this paper we report mainly on the initial realisation and first experiments concerning the retrieval and reuse phases.