Simon K. Milton

An Ontology of Data Modelling Languages: A Study Using a Common-Sense Realistic Ontology

Data modelling languages are used in today’s information systems engineering environments. Many have a degree of hype surrounding their quality and applicability with narrow and specific justification often given in support of one over another. We want to more deeply understand the fundamental nature of data modelling languages. We thus propose a theory, based on ontology, that should allow us to understand, compare, evaluate, and strengthen data modelling languages. In this paper we present a method (conceptual evaluation) and its extension (conceptual comparison), as part of our theory. Our methods are largely independent of a specific ontology. We introduce Chisholm’s ontology and apply our methods to analyse some data modelling languages using it. We find a good degree of overlap between all of the data modelling languages analysed and the core concepts of Chisholm’s ontology, and conclude that the data modelling languages investigated reflect an ontology of commonsense-realism.

Towards Quality Measures for Evaluating Thesauri

Thesaurus evaluation has often focused on the task of retrieval or analyzed the information environment including factors such as the user, the user interface. Rarely ever has the quality of a thesaurus as an artefact itself been focus of attention. In particular, there are no measures, which provide a holistic description of its intrinsic characteristics as an artefact. This paper suggests a range of abstract measurement constructs based on quality notions in thesaurus literature and inference from other related literature. The suggested measurement constructs allow representing a thesaurus in evaluation approaches, which thereby becomes empirically testable.

Service blueprinting and BPMN: a comparison

Purpose – The purpose of this paper is to compare and contrast a customer‐focused service process diagram tool (blueprinting) with an organizational‐focused process diagram tool (business process modeling notation, or BPMN).Design/methodology/approach – Using a hotel stay as an example, the paper presents both a service blueprint and a BPMN diagram. The authors then explicitly discuss the similarities, differences resulting from an ontological comparison of service blueprints and BPMN, and show where the two tools can be complementary.Findings – The authors have found that one similarity is that service blueprinting segments processes into parts that are similar to BPMNs idea of swimlanes. However, the swimlanes in service blueprinting separate customer actions, customer‐facing employees’ actions and functions, and back‐stage functions, actors, and information systems, thereby effectively mandating certain swimlanes for the purpose of analyzing points of contact between the firm and a customer. Another s...

One hundred tasks an hour: An observational study of emergency department consultant activities

Objective: To determine work activity patterns undertaken by ED consultants.

Thesaurus and ontology structure: Formal and pragmatic differences and similarities

Thesauri and other types of controlled vocabularies are increasingly re‐engineered into ontologies described using the Web Ontology Language (OWL), particularly in the life sciences. This has led to the perception by some that thesauri are ontologies once they are described by using the syntax of OWL while others have emphasized the need to re‐engineer a vocabulary to use it as ontology. This confusion is rooted in different perceptions of what ontologies are and how they differ from other types of vocabularies. In this article, we rigorously examine the structural differences and similarities between thesauri and meaning‐defining ontologies described in OWL. Specifically, we conduct (a) a conceptual comparison of thesauri and ontologies, and (b) a comparison of a specific thesaurus and a specific ontology in the same subject field. Our results show that thesauri and ontologies need to be treated as 2 orthogonal kinds of models with superficially similar structures. An ontology is not a good thesaurus, nor is a thesaurus a good ontology. A thesaurus requires significant structural and other content changes to become an ontology, and vice versa.

Relationships and relata in ontologies and thesauri: Differences and similarities

Ontologies and thesauri structure concepts and, from the perspective of a practitioner, do not appear to be very different. Nevertheless, experts acknowledge that ontologies are different from thesauri in several respects. In this paper we aim to clarify some of the similarities and differences by systematically comparing the structure of each: the relations and their relata in ontologies and the relationships and their relata in thesauri. In particular, we analyze thesaurus relationships and their relata as they are defined in the latest version of the international ISO thesaurus standard against formally well-defined ontological relationships and relata from ontology literature --more specifically ontology literature based in realism. We have found that the relata as well as the relationships in thesauri need to be classified further before any reasonable matching to formal ontological relationships is possible. Isolated hierarchical relationships in thesauri then may correspond to the is-a relationship, specific mereological relationships, or fundamental relationships such as the instantiation between universals and individuals in ontologies. Determining how such correspondences apply in domain-specific cases depends on whether the thesaurus relationships contribute to the specifications of necessary and sufficient conditions for their respective relata in the ontology --a function that relationships do not have in thesauri. Our findings make it clear that thesauri require structural and definitional reengineering in order to be reused or treated as ontologies, but that adherence to the international standard for thesauri provides a good base for such reengineering.

Data modeling: Description or design?

Data modeling for database creation has generally been considered to be a descriptive process: the real-world is observed and represented in a conceptual model that is then transformed into a logical structure for a database. This is reflected in prescriptive methods and is the dominant assumption in most studies. However, data modeling can also be considered a type of design with negotiable requirements, a creative process, and many workable solutions. Our paper discusses empirical results from almost 500 practitioners on three continents comparing data modeling to design. We found that data modeling, as practiced, was better characterized as design.

Conceptual modeling in practice: An evidence-based process-oriented theory

Interviews were undertaken with 26 highly experienced conceptual modelers to determine how they undertook conceptual modeling in practice. Major types of cognitive states and events experienced by the conceptual modelers, as manifested in typed transcripts of the interviews, were then identified and coded. Based on the codes and the interview transcripts, a descriptive, process-oriented theory of how the modelers undertook conceptual modeling was induced.

A content-focused method for re-engineering thesauri into semantically adequate ontologies using OWL

The re-engineering of vocabularies into ontologies can save considerable time in the development of ontologies. Current methods that guide the re-engineering of thesauri into ontologies often convert vocabularies syntactically only and ignore the problems that stems from interpreting vocabularies as statements of truth (ontologies). Current reengineering methods also do not make use of the semantic capabilities of formal languages like OWL in order to detect logical mistakes and to improve vocabularies. In this paper, we introduce a content-focused method for building domain-specific ontologies based on a thesaurus, a popular type of vocabulary. The method results in a semantically adequate ontology that does not only contain a semantically rich description of the entities to be modeled, but also enables non-trivial consistency checks and classifications based on automated reasoning, and can be integrated with other ontologies following the same development principles. The identification of membership conditions, the alignment to a top-level ontology and formal relations, and the consistency check and inference using a reasoner are the central steps in our method. We explain the motivation and sub-activities for each of these steps and illustrate their application through a case study in the domain of agricultural fertilizers based on the ACROVOC Thesaurus. Foremost, our method shows that simple syntactic conversions are insufficient to derive an ontology from a thesaurus. Instead, considerable structural changes are required to derive an ontology that corresponds to the reality it represents. Our method relies on a manual development effort and is particularly useful where a highly reliable is-a hierarchy is crucial.

Components of service value in business-to-business Cloud Computing

Cloud computing services are used in many businesses. However, little is known about the components of service value in B2B cloud computing services from a customer perspective. In the B2C service literature, service value has four components: customer perceptions of the quality, equity, benefits, and sacrifices for the delivered service. The purpose of this research is to determine whether the components from an established B2C model applies to B2B cloud computing services. We followed a qualitative approach and interviewed twenty-one managers responsible for handling cloud computing services and for the decision to repurchase services. The interviews were then analysed to determine whether the existing model covers cloud computing services completely. We found broad support for the established service value components in a B2B cloud computing context. Importantly, we found evidence for a fifth component we called “cloud service governance”. A deeper understanding of service value perceptions among business users of cloud computing services means vendors can measure this for their customers. This is important because perceptions of service value directly influences customer satisfaction, impacting the buyer’s intention to repurchase the service. Similarly, this study will help buyers of B2B cloud computing services to assess the value extracted from their cloud computing service relationships. It will also help cloud providers and new competitors focus their efforts (e.g., into increasing technical reliability) to improve their customers’ perceptions of value obtained from their cloud computing services. This research advances the literature by extending the established B2C service value model to the context of B2B cloud computing, and providing the first evidence of an extra component in B2B services more generally.