Matt Selway

Formalising natural language specifications using a cognitive linguistic/configuration based approach

This paper addresses the problem of transforming business specifications written in natural language into formal models suitable for use in information systems development. It proposes a method for transforming controlled natural language specifications based on the Semantics of Business Vocabulary and Business Rules standard. This approach is unique in combining techniques from Model-Driven Engineering (MDE), Cognitive Linguistics, and Knowledge-based Configuration, which allows the reliable semantic processing of specifications and integration with existing MDE tools to improve productivity, quality, and time-to-market in software development. The method first learns the vocabulary of the specification from glossary-like definitions then parses the rules of the specification and outputs the resulting formal SBVR model. Both aspects of the method are tested separately, with the system correctly learning 98% of the vocabulary and correctly interpreting 98% of the rules of an SBVR SE based example. Finally, the proposed method is compared to state-of-the-art approaches for creating formal models from natural language specifications, arguing that it meets the criteria necessary to fulfil the three goals of (1) shifting control of specification to non-technical business experts, (2) reducing the manual effort involved in formalising specifications, and (3) supporting business experts in creating well-formed sets of business vocabularies and rules. HighlightsA method for deep processing of natural language business specifications is proposed.The method is based on Cognitive Linguistics and Knowledge-based Configuration.The method acquires vocabulary from a business glossary and parses business rules.The vocabulary acquisition achieves an accuracy of 96%.The semantic analysis of rules achieves an accuracy of 98%.

On the application of software modelling principles on ISO 15926

The ISO 15926 standard was developed to facilitate the integration of data in support of the life-cycle activities and processes of process plants. It is currently used for the handover of design documents between those companies that design and engineer engineering equipment to those organisations responsible for their operation and maintenance within the oil & gas industry. Part 2 of the standard contains a generic data model which represents the core of the standard. In this paper we applied well established software modelling principles on this part to overcome some previously identified problems such as inconsistent terminology and complexity. The two main outcomes are: (1) a multi-level view on Part 2 that formalises some aspects of the current data model and (2) the simplification of the data model, lowering the barriers to the adoption of ISO 15926 in industry.

A Conceptual Framework for Large-scale Ecosystem Interoperability

One of the most significant challenges in information system design is the constant and increasing need to establish interoperability between heterogeneous software systems at increasing scale. The automated translation of data between the data models and languages used by information ecosystems built around official or de facto standards is best addressed using model-driven engineering techniques, but requires handling both data and multiple levels of metadata within a single model. Standard modelling approaches are generally not built for this, compromising modelling outcomes. We establish the SLICER conceptual framework built on multilevel modelling principles and the differentiation of basic semantic relations that dynamically structure the model and can capture existing multilevel notions. Moreover, it provides a natural propagation of constraints over multiple levels of instantiation.

An integrated multi-level modeling approach for industrial-scale data interoperability

Multi-level modeling is currently regaining attention in the database and software engineering community with different emerging proposals and implementations. One driver behind this trend is the need to reduce model complexity, a crucial aspect in a time of analytics in Big Data that deal with complex heterogeneous data structures. So far no standard exists for multi-level modeling. Therefore, different formalization approaches have been proposed to address multi-level modeling and verification in different frameworks and tools. In this article, we present an approach that integrates the formalization, implementation, querying, and verification of multi-level models. The approach has been evaluated in an open-source F-Logic implementation and applied in a large-scale data interoperability project in the oil and gas industry. The outcomes show that the framework is adaptable to industry standards, reduces the complexity of specifications, and supports the verification of standards from a software engineering point of view.

A conceptual framework for large-scale ecosystem interoperability and industrial product lifecycles

Abstract One of the most significant challenges in information system design is the constant and increasing need to establish interoperability between heterogeneous software systems at increasing scale. The automated translation of data between the data models and languages used by information ecosystems built around official or de facto standards is best addressed using model-driven engineering techniques, but requires handling both data and multiple levels of metadata within a single model. Standard modelling approaches are generally not built for this, compromising modelling outcomes. We establish the SLICER conceptual framework built on multilevel modelling principles and the differentiation of basic semantic relations (such as specialisation, instantiation, specification and categorisation) that dynamically structure the model. Moreover, it provides a natural propagation of constraints over multiple levels of instantiation. The presented framework is novel in its flexibility towards identifying the multilevel structure, the differentiation of relations often combined in other frameworks, and a natural propagation of constraints over multiple levels of instantiation.

Formalising Natural Language Specifications Using a Cognitive Linguistics/Configuration Based Approach

This paper addresses the problem of transforming natural language business specifications into formal models suitable for use in information systems. In particular, the Semantics of Business Vocabulary and Business Rules (SBVR) standard is used as a starting point for both the natural language specifications and the formal representation. In recent years, SBVR-based approaches have been proposed for transforming natural language business rules into models, such as UML Class Diagrams, however, most focus on the transformations performed after the SBVR models have been created and, therefore, simplify or entirely neglect the natural language to SBVR transformation. There are a number of difficulties in transforming natural language into formal models, such as ambiguity and inconsistency. This paper presents a solution based on a unique combination of techniques from Cognitive Linguistics and Knowledge-based Configuration in order to transform natural language business specifications into SBVR models. We present a comparative survey of state-of-the-art approaches and argue that current solutions do not fulfil the criteria necessary to meet our goals. We demonstrate our approach and show how it improves translation of natural language business specifications into formal models and increases the level of automation for Model-Driven Engineering.

Semantic Interpretation of Requirements through Cognitive Grammar and Configuration

Many attempts have been made to apply Natural Language Processing to requirements specifications. However, typical approaches rely on shallow parsing to identify object-oriented elements of the specifications (e.g. classes, attributes, and methods). As a result, the models produced are often incomplete, imprecise, and require manual revision and validation. In contrast, we propose a deep Natural Language Understanding approach to create complete and precise formal models of requirements specifications. We combine three main elements to achieve this: (1) acquisition of lexicon from a user-supplied glossary requiring little specialised prior knowledge; (2) flexible syntactic analysis based purely on word-order; and (3) Knowledge-based Configuration unifies several semantic analysis tasks and allows the handling of ambiguities and errors. Moreover, we provide feedback to the user, allowing the refinement of specifications into a precise and unambiguous form. We demonstrate the benefits of our approach on an example from the PROMISE requirements corpus.

Enabling Information Interoperability through Multi-domain Modeling

Flexible integration of information systems with heterogeneous data structures and interfaces has been an important IT research goal for decades. It is a fundamental requirement for enterprise transformation that the business knowledge captured in form of data and business processes can be integrated and adapted within and across enterprise boundaries. In this paper we present results of a model-driven interoperability approach in the asset management domain. The approach builds on multi-domain modeling principles and has been applied in three large use cases over the last 5 years. We show how information interoperability and enterprise transformation can benefit from multi-domain modeling and how it fits together with a design science approach.

Automated Techniques for Generating Behavioural Models for Constructive Combat Simulations

Constructive combat simulation is widely used across Defence Science and Technology Group, typically using behavioural models written by software developers in a scripting or programming language for a specific simulation. This approach is time-consuming, can lead to inconsistencies between the same behaviour in different simulations, and is difficult to engage military subject matter experts in the elicitation and verification of behaviours. Therefore, a representation is required that is both comprehensible to non-programmers and is translatable to different simulation execution formats. This paper presents such a representation, the Hierarchical Behaviour Model and Notation (HBMN), which incorporates aspects of existing business process and behaviour representations to provide a hierarchical schema allowing an incremental approach to developing and refining behaviour models from abstract partial models to concrete executable models. The HBMN representation is combined with automated processes for translating written military doctrine texts to HBMN and from HBMN to executable simulation behaviours, providing a cohesive solution to modelling combat behaviours across all stages of development.

Towards an open-standards based framework for achieving condition-based predictive maintenance

The advent of Industrial Internet of Things (IIoT) technology has significantly optimized the industrial operations management by connecting industrial assets with information systems and, hence, with business processes. The IIoT forms the backbone for materializing the Industry 4.0 initiative. Actionable insights obtained from industrial analytics are one of the pivotal means for achieving intelligent operations and maintenance. Intelligence refers to making optimal decisions for both automated and human-in-the-loop decision making. Condition-based predictive maintenance (CBPdM), also known as Maintenance 4.0, is among the major focus points of the Industry 4.0 and IIoT. In this paper, we discuss the existing standards related to condition-based maintenance and the potential of the Open Industrial Interoperability Ecosystem (OIIE), a MIMOSA led initiative, as a framework which extends previous open standards for achieving CBPdM. We illustrate how the framework addresses the requirements of Industry 4.0 and CBPdM.