Terje Brasethvik

Evaluating the quality of information models: empirical testing of a conceptual model quality framework

This paper conducts an empirical analysis of a semiotics-based quality framework for quality assuring information models. 192 participants were trained in the concepts of the quality framework, and used it to evaluate models represented in an extended Entity Relationship (ER) language. A randomised, double-blind design was used, in which each participant independently reviewed multiple models and each model was evaluated by multiple reviewers. A combination of quantitative and qualitative analysis techniques were used to evaluate the results, including reliability analysis, validity analysis, interaction analysis, influence analysis, defect pattern analysis and task accuracy analysis.. An analysis was also conducted of the frameworks likelihood of adoption in practice. The study provides strong support for the validity of the framework and suggests that it is likely to be adopted in practice, but raises questions about its reliability and the ability of participants to use it to accurately identify defects. The research findings provide clear directions for improvement of the framework. The research methodology used provides a general approach to empirical validation of quality frameworks.

On the challenges of business modeling in large-scale reengineering projects

Modeling existing and future business processes is crucial to the outcome of large-scale reengineering projects. The use of parameterized or standard components does not render business models redundant, but shifts the modeling focus even more from technical aspects to the real-world business processes. In current reengineering projects, however, there is often a lack of coordination of modeling activities and the consistency of conceptual models across project activities can be threatened. There are often several models of the same phenomenon that are used by different people, in different phases, or for different purposes. This paper discusses some of the challenges of business modeling and presents a three-tier model description that explains some of the model variants found in reengineering projects. The findings from a large-scale SAP project in Europe underline the importance of a balanced business model and show how individually tailored conceptual models may hamper the formation of a common understanding of the domain and badly affect the reengineering of the business processes.

Evaluating the Quality of Process Models: Empirical Testing of a Quality Framework

This paper conducts an empirical analysis of a conceptual model quality framework for evaluating the quality of process models. 194 participants were trained in the concepts of the quality framework, and then used it to evaluate models represented in a workflow modelling language. A randomised, double-blind design was used, and the results evaluated using a combination of quantitative and qualitative techniques. An analysis was also conducted of the frameworks likelihood of adoption in practice, which is an issue rarely addressed in IS design research. The study provides strong support for the validity of the framework and suggests that it is likely to be adopted in practice, but raises questions about its reliability. The research findings provide clear direction for further research to improve the framework.

Internet-based organizational memory and knowledge management

On Knowledge Management in the Internet Age Internet Enabled Corporate Knowledge Sharing and Utilization Essential Factors in Knowledge Management with COTS Products The Challenge for Customer Service - Managing Heterogeneous Knowledge Semi-Structured Data Extraction from Heterogeneous Sources Adding Knowledge to Accounting Systems for Virtual Enterprises Concept Indexes - Sharing Knowledge from Documents Facilitating Knowledge Transfer in an R&D Environment - a Case Study On Knowledge Management - a Field Study The Challenges of Interorganizational Management - an Emerging Issue for Virtual Organizations Managing Knowledge in an ERP Enabled Virtual Organization Using an Intranet to Manage Knowledge for a Virtual Project Team.

Semantic enrichment for improving systems interoperability

The overall goal addressed in this paper is to improve semantic interoperability in heterogeneous systems by means of establishing mappings between relevant domain ontologies. The mappings are discovered based on the technique of semantic enrichment through extension analysis, i.e. using instance information of the ontology to enrich the original ontology and further to calculate similarities between concepts in two ontologies. Text categorization is used to automatically assign instance to the concepts in the ontology. Information retrieval techniques are used to calculate similarity between concepts. Based on the similarities measure, a heuristic method is used to establish mapping assertions for the two ontologies. The method is illustrated using a product catalogue scenario.

A model-driven ERP Environment with search facilities

Traditional business models are primarily used in the initial phases of BPR or enterprise resource planning projects. They are static representations of systems requirements and business procedures, but have proven less useful in supporting the actual use of the systems. With the model-driven business management (MDBM) approach, dynamic and adaptable business models constructed as part of the implementation project are afterwards used to access the system and monitor the real business flows. In this paper, we present the linguistic aspects of the MDBM approach and discuss how the linguistic part and the modeling part of MDBM mutually support each other.

A Conceptual Modeling Approach to Semantic Document Retrieval

This paper describes an approach to semantic document retrieval geared towards cooperative document management. In our conceptual modeling approach, a semantic modeling language is used to construct a domain model of the subject domain referred to by the document collection. This domain model is actively used for the tasks of document classification and search. Moreover, linguistic techniques are used to facilitate both the construction of the model and its use. This paper presents our prototype model-based classification and search tool and how it is applied on a document collection from a Norwegian company.

Semantically Accessing Documents Using Conceptual Model Descriptions

When publishing documents on the Web, the user needs to describe and classify her documents for the benefit of later retrieval and use. This paper presents an approach to semantic document classification and retrieval based on Natural Language Processing and Conceptual Modeling. The Referent Model language is used in combination with a lexical analysis tool to define a controlled vocabulary for classifying documents. Documents are classified by means of sentences that contain the high frequency words in the document that also occur in the domain model defining the vocabulary. The sentences are parsed using a DCG-like grammar, mapped into a Referent Model fragment and stored along with the document using RDF-XML syntax. The model fragment represents the connection between the document and the domain model and serves as a document index. The approach is being implemented for a document collection published by the Norwegian Center for Medical Informatics (KITH).

A Semantic Modeling Approach to Metadata.

States that heterogeneous project groups today may be expected to use the mechanisms of the Web for sharing information. Metadata has been proposed as a mechanism for expressing semantics of information and, hence, facilitate information retrieval, understanding and use. Presents an approach to sharing of information which aims to use a semantic modeling language as the basis for expressing semantics of information and designing metadata schemes. Functioning in the borderlines between human and computer understandability, the modeling language could be able to express semantics of published Web documents. Reporting on work in progress, the paper presents the overall framework and ideas.

A Hybrid Approach to Ontology Relationship Learning

Most ontology learning tools concentrate on extracting concepts and instances from text corpora. There are some recent tools that employ linguistics or data mining to uncover concept relationships, but the results are mixed. Since relationships are semantically complex notions, it seems interesting to combine approaches that address different aspects of concept relationships. In this paper we present a hybrid approach that combines the co-occurrence principle from association rules with contextual similarities from linguistics. The technique has been tested in an ontology engineering project, and the results show significant improvements over traditional techniques.