Ontology Based Method Engineering

We need conceptual modelling languages to gain domain knowledge in the requirements engineering and analysis phases of an IS development project. These languages should serve an IS expert as means of communication between him or her and the domain expert. Many different modelling languages have been used for conceptual modelling. Consequently, questions relating to the quality of these languages have arisen. Wand, Weber and others have evaluated these languages using an ontology. Each of the languages was found to contain certain deficits. Because our aim is to construct a language without such deficits, we propose the opposite technique. We develop an ontologically clear modelling language for process modelling with the help of the BWW representational model. In addition to this modelling language, we introduce a process model which guides model creation. Both components form a conceptual modelling method

Implementation of a Port-graph Model for Finance

In this paper we examine the process involved in the design and implementation of a port-graph model to be used for the analysis of an agent-based rational negligence model. Rational negligence describes the phenomenon that occurred during the financial crisis of 2008 whereby investors chose to trade asset-backed securities without performing independent evaluations of the underlying assets. This has contributed to motivating the search for more effective and transparent tools in the modelling of the capital markets. This paper shall contain the details of a proposal for the use of a visual declarative language, based on strategic port-graph rewriting, as a visual modelling tool to analyse an asset-backed securitisation market.Comment: In Proceedings TERMGRAPH 2018, arXiv:1902.0151

System simulation by SEMoLa

SEMoLa is a platform, developed at DISA since 1992, for system knowledge integration and modelling. It allows to create computer models for dynamic systems and to manage different types of information. It is formed by several parts, each dealing with different forms of knowledge, in an integrated way: a graphical user interface (GUI), a declarative language for modelling, a set of commands with a procedural scripting language, a specific editor with code highlighting (SemEdit), a visual modelling application (SemDraw), a data base management system (SemData), plotting data capabilities (SemPlot), a raster maps management system (SemGrid), a large library of random number generators for uncertainty analysis, support for fuzzy logic expert systems, a neural networks builder and various statistical tools (basic statistics, multiple and non-linear regression, moving statistics, etc.). The core part of the platform is the declarative modelling language (SEMoLa; simple, easy to use, modelling language). It relies on System Dynamics principles and uses an integrated view to represent dynamic systems through different modelling approaches (state/individual-based, continuous/discrete, deterministic/stochastic) without requiring specific programming skills. SEMoLa language is based on a ontology closer to human reasoning rather than computer logic and constitutes also a paradigm for knowledge management. SEMoLa platform permits to simplify the routinely tasks of creating, debugging, evaluating and deploying computer simulation models but also to create user libraries of script commands. It is able to communicate with other frameworks exchanging - with standard formats - data, modules and model components

Specifying multimedia configurations in Z

In this paper we illustrate how the formal specification language Z can be used to reason about the temporal and throughput constraints associated with multimedia flows of information. In particular we show how it is possible to specify issues related to maximum delays, throughputs and jitter of information flows and how control of these flows can be achieved. What makes our work particularly interesting is that we deal with temporal aspects of systems without the use of a temporal logic. Rather, we highlight the versatility of the Z language in modelling systems with real time constraints

Multilingual Multi-Figurative Language Detection

Figures of speech help people express abstract concepts and evoke stronger emotions than literal expressions, thereby making texts more creative and engaging. Due to its pervasive and fundamental character, figurative language understanding has been addressed in Natural Language Processing, but it's highly understudied in a multilingual setting and when considering more than one figure of speech at the same time. To bridge this gap, we introduce multilingual multi-figurative language modelling, and provide a benchmark for sentence-level figurative language detection, covering three common figures of speech and seven languages. Specifically, we develop a framework for figurative language detection based on template-based prompt learning. In so doing, we unify multiple detection tasks that are interrelated across multiple figures of speech and languages, without requiring task- or language-specific modules. Experimental results show that our framework outperforms several strong baselines and may serve as a blueprint for the joint modelling of other interrelated tasks.Comment: Accepted to ACL 2023 (Findings

Multilingual Multi-Figurative Language Detection

Figures of speech help people express abstract concepts and evoke stronger emotions than literal expressions, thereby making texts more creative and engaging. Due to its pervasive and fundamental character, figurative language understanding has been addressed in Natural Language Processing, but it's highly understudied in a multilingual setting and when considering more than one figure of speech at the same time. To bridge this gap, we introduce multilingual multi-figurative language modelling, and provide a benchmark for sentence-level figurative language detection, covering three common figures of speech and seven languages. Specifically, we develop a framework for figurative language detection based on template-based prompt learning. In so doing, we unify multiple detection tasks that are interrelated across multiple figures of speech and languages, without requiring task- or language-specific modules. Experimental results show that our framework outperforms several strong baselines and may serve as a blueprint for the joint modelling of other interrelated tasks.</p