Matthew West | University of Leeds (original) (raw)

Papers by Matthew West

Publisher Summary This chapter presents a data model that provides a framework for analyzing busi... more Publisher Summary This chapter presents a data model that provides a framework for analyzing business requirements, provides a data model of some detailed areas that shows how business terms can be incorporated into a data model, and is easily extensible, either by adding entity types or reference data. From this, it can clearly be seen that the model is coherent and cohesive, as well as extensible in a regular way; all desirable properties in a changing world. Working things out properly is actually the fastest way to the right answer, plus it helps to assure that one has the right answer. In data modeling, it is not the time it takes to produce a model that determines the effort it takes, but how many times the model has to be redone before it sets right. The result overall is improved consistency, especially when one has a team of data modelers, and a reduction in errors and rework. All of this leads to high quality data models that meet the information requirements of the business.

Developing High Quality Data Models, 2011

This chapter takes a look at the different types and uses of data models that can be found, what ... more This chapter takes a look at the different types and uses of data models that can be found, what they are good for, and what are the differences are between them. It also analyzes the needs of integration architecture and the special requirements it puts on a data model. Data models have many purposes. These cause differences in both style and content, which can cause confusion, surprise, and disagreement. The purposes and the different types of data model are explained in the chapter. Following this, the chapter takes a look at integration data models and describes the process of integrating data including architecture and a methodology that can be used for data integration. The model integration process takes a number of application models and an integration model. It ensures that all the concepts of the application models are represented in the integration model, and it develops a mapping specification between the integration model and each of the application models. To conclude, data integration presents some particular challenges, and the desirable characteristics of integration models are not necessarily the same as those developed for other purposes.

Developing High Quality Data Models, 2011

This chapter illustrates some examples of traps found with relationship types in data models. It ... more This chapter illustrates some examples of traps found with relationship types in data models. It then demonstrates how the principles for conceptual, integration, and enterprise data models can help overcome or avoid these issues. The principles for relationship types are—activities should be represented by entity types (not relationship types), relationship types (in the entity/relationship sense) should only be used to represent things about which there is nothing to say, and cardinality constraints on relationship types should be true always. Applying the principles makes the data models more consistent, and they are more likely to support the data needed, rather than just the data first thought of. Making the data model more general is relatively easy. One simply removes the constraints that may not always be true. Introducing the fudge data to overcome the incorrect cardinalities can have expensive consequences. Sometimes cardinalities are set to one-to-many, meaning one at a time, when the cardinalities are really many-to-many over time because the relationship type is transferable. Imposing restrictions through the data structure means—arbitrary or inappropriate restrictions are placed on the data that can be held, historical data about a relationship cannot be held, the entity type will only work within the context defined, and the resultant system is harder to share.

Applied Ontology, 2006

On March 14-15, 2006, at the US National Institute of Standards and Technology (NIST) in Gaithers... more On March 14-15, 2006, at the US National Institute of Standards and Technology (NIST) in Gaithersburg, MD, there took place the first Upper Ontology Summit (UOS). This was a convening of custodians of several prominent upper ontologies, key technology participants, and interested other parties, with the purpose of finding a means to relate the different ontologies to each other. The result is reflected in a joint communiqu��, directed to the larger ontology community and the general public, and expressing a joint intent to build bridges ...

Ontologies have been part of developing information systems in Shell for some twenty years, takin... more Ontologies have been part of developing information systems in Shell for some twenty years, taking the form of data models and reference data used within information systems. A problem in reusing or integrating systems is the context that they assume, which may not be valid beyond the scope of an implementation. Lessons learnt include trying to ensure that the context is explicit, and that what are really local rules in a global context are not defined as global rules in a local context. These lessons have been applied in the development of International Standards to provide an architecture for integration and a data model that includes both a foundation ontology that has been developed on a well defined and consistent basis, and provides a framework for extension of the ontology through reference data.

Applied Ontology

The Ontology Summit 2012 explored the current and potential uses of ontology, its methods and par... more The Ontology Summit 2012 explored the current and potential uses of ontology, its methods and paradigms, in big systems and big data: How ontology can be used to design, develop, and operate such systems. The systems addressed were not just software systems, although software systems are typically core and necessary components, but more complex systems that include multiple kinds and levels of human and community interaction with physical-software systems, systems of systems, and the socio-technical environments for those ...

Problem Currently, there is no agreed on methodology for development of ontologies, and there is ... more Problem Currently, there is no agreed on methodology for development of ontologies, and there is no consensus on how ontologies should be evaluated. Consequently, evaluation techniques and tools are not widely utilized in the development of ontologies. This can lead to ontologies of poor quality and is an obstacle to the successful deployment of ontologies as a technology. Approach The goal of the Ontology Summit 2013 was to create guidance for ontology developers and users on how to evaluate ontologies. Over a period of four months a variety of approaches were discussed by participants, who represented a broad spectrum of ontology, software, and system developers and users. We explored how established best practices in systems engineering and in software engineering can be utilized in ontology development. Results This document focuses on the evaluation of five aspects of the quality of ontologies: intelligibility, fidelity, craftsmanship, fitness, and deployability. A model for the ontology life cycle is presented, and

Replaceable parts (also known as facilities, tag parts, components or functional components) is a... more Replaceable parts (also known as facilities, tag parts, components or functional components) is a concept that is relevant, in particular, to many complex artefacts. These include general engineering products such as refineries and aircraft, and artefacts of the built ...

The role that ontologies play or can play in designing and employing semantic technologies has be... more The role that ontologies play or can play in designing and employing semantic technologies has been widely acknowledged by the Semantic Web and Linked Data communities. But the level of collaboration between these communities and the Applied Ontology community has been much less than expected. And ontologies and ontological techniques appear to be of marginal use in Big Data and its applications. To understand this situation and foster greater collaboration, Ontology Summit 2014 brought together representatives from the Semantic Web, Linked Data, Big Data and Applied Ontology communities, to address three basic problems involving applied ontology and these communities: (1) The role of ontologies [in these communities], (2) Current uses of ontologies in these communities, and (3) Engineering of ontologies and semantic integration. The intent was to identify and understand: (a) causes and challenges (e.g. scalability) that hinder reuse of ontologies in SW and LD, (b) solutions that can reduce the differences between ontologies on and off line, and (c) solutions to overcome engineering bottlenecks in current Semantic Web and Big Data applications.

Developing High Quality Data Models, 2011

Applied Ontology, 2006

Applied Ontology

Developing High Quality Data Models, 2011

MULTI 2020, co located with MODELS 2020, 16 October 2020, Online, Oct 16, 2020

In the multi-level type modeling community, claims that most enterprise application systems use o... more In the multi-level type modeling community, claims that most enterprise application systems use ontologically multi-level types are ubiquitous. To be able to empirically verify this claim one needs to be able to expose the (often underlying) ontological structure and show that it does, indeed, make a commitment to multi-level types. We have not been able to find any published data showing this being done. From a top-level ontology requirements perspective, checking this multi-level type claim is worthwhile. If the datasets for which the top-level ontology is required are ontologically committed to multi-level types, then this is a requirement for the top-level ontology. In this paper, we both present some empirical evidence that this ubiquitous claim is correct as well as describing the process we used to expose the underlying ontological commitments and examine them. We describe how we use the bCLEARer process to analyse the UNICLASS classifications making their implicit ontological commitments explicit. We show how this reveals the requirements for two general ontological commitments; higher-order types and first-class relations. This establishes a requirement for a top-level ontology that includes the UNICLASS classification to be able to accommodate these requirements. From a multi-level type perspective, we have established that the bCLEARer entification process can identify underlying ontological commitments to multi-level type that do not exist in the surface linguistic structure. So, we have a process that we can reuse on other datasets and application systems to help empirically verify the claim that ontological multi-level types are ubiquitous.

This is the first in a series of presentations that should be seen as an integrated whole rather ... more This is the first in a series of presentations that should be seen as an integrated whole rather than a collection of separate presentations. It is an introduction to the whole and covers the Information Quality Management angle which is the motivation for our interest in 4-Dimensionalism. Later presentations will go down through the 7 circles of information management showing how 4D permeates what we are doing in developing and using 4-Dimensionalism on the National Digital Twin programme.