Conceptual modelling of complex topics: ConML as an example / Modelado conceptual de temas complejos: ConML como ejemplo

Community member post by Cesar Gonzalez-Perez

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Cesar Gonzalez-Perez (biography)

A Spanish version of this post is available

What are conceptual models? How can conceptual modelling effectively represent complex topics and assist communication among people from different backgrounds and disciplines?

This blog post describes ConML, which stands for “Conceptual Modelling Language”. ConML is a specific modelling language that was designed to allow researchers who are not expert in information technologies to create and develop their own conceptual models. It is useful for the humanities, social sciences and experimental sciences.

What are conceptual models?

A conceptual model is a formal or semi-formal representation of a topic under investigation, using concepts rather than physical parts. Conceptual models are generally visualised in the form of diagrams plus accompanying text, as shown in the figure below.

A modelling language is an artificial language designed to express models. Since models are usually depicted in the form of diagrams for convenience, modelling languages often incorporate a graphical notation. Like natural languages, modelling languages have:

  • a lexicon, that is, a set of the “words” that exist in the language
  • a syntax, that is, a set of the rules that tell us how we can combine those words in order to compose meaningful “sentences”
  • a semantics, that is, a description of the relationship between each “word” in the lexicon and those things in the world for which it stands.

The “words” of modelling languages are not conveyed through text or sound like those of natural languages, but usually through icons and drawings to help the formal visualisation of the abstract concepts. The syntactic rules of the modelling language tell us how these icons can be connected together to express models, and what each kind of connection means.

ConML contains very few “words”, and its syntax is very simple.

Conceptual modelling was developed within software engineering, and it has been applied to a number of domains beyond that, including business organisation, genomics or archaeology. ConML tries to introduce know-how and techniques that are usually only available to software engineers to specialists in other areas, including the humanities and social sciences.

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Sample ConML diagram showing that complete archaeological objects are often fragmented, and what subtypes exist regarding origin and intentionality. (Source: Gonzalez-Perez 2018).

How can conceptual modelling effectively represent complex topics and assist communication among people from different backgrounds and disciplines?

There are two key issues:

  1. a conceptual model helps you better understand the portion of reality that you are dealing with, since it removes some of detail and complexity that often makes it unmanageable.
  2. a conceptual model constitutes a language in which to communicate statements about that portion of reality, especially when people from different backgrounds and disciplines are involved.

ConML aims to allow complex ideas to be communicated in a simple, meaningful way.

ConML recognises that different people and groups have different subjective views on things, entities change over time, and understandings are not always certain. This means that the information captured in a conceptual model might be, to varying extents, subjective, temporary and uncertain. ConML incorporates support for subjectivity and temporality, as well as basic support for uncertainty.

By incorporating the ability to express subjectivity, temporality and vagueness in models – thus addressing the crucial issues of multivocal, diachronic and imprecise or uncertain knowledge – ConML includes features that are usually absent from other modelling languages, and that make it especially suitable for the humanities and social sciences.

An example

A major application of ConML has been in building CHARM, a Cultural Heritage Abstract Reference Model.

CHARM represents things in terms of concepts, properties of concepts, and relationships between concepts. CHARM represents anything that may be the recipient of cultural value ascribed by any individual. CHARM not only represents the specific entities that might make up cultural heritage, but also other entities which are necessary in order to describe and understand them.

CHARM is a descriptive, rather than prescriptive, model. Users can pick and choose among the elements based on the needs of their organisation and project.

Some final words

What has your experience been with conceptual modelling? Have you had experience with ConML and has it been useful? Have you used other forms of conceptual modelling?

To find out more:

Reference:
Gonzalez-Perez, C. (2018). Information Modelling for Archaeology and Anthropology. Springer: Cham, Switzerland.

Biography: Cesar Gonzalez-Perez is a Staff Scientist at the Institute of Heritage Sciences (Incipit), Spanish National Research Council (CSIC), where he leads a co-research line in software engineering and cultural heritage. The ultimate goal of his work is to develop the necessary theories, methodologies and technologies to understand and assist the knowledge-creation processes that occur in relation to cultural heritage. Previously, Cesar has worked at a number of public and private organisations in Spain and Australia, both in industry and academia, and in the fields of conceptual modelling, metamodelling and situational method engineering. He has started three technology-based companies, served as elected member of the steering committee of the Computer Applications and Quantitative Methods in Archaeology (CAA) association, and authored or co-authored over 100 publications. Cesar’s current major areas of interest are the application of knowledge- and information-modelling techniques in the humanities, and the connection between inference, discourse and ontology evolution.


Modelado conceptual de temas complejos: ConML como ejemplo / Conceptual modelling of complex topics: ConML as an example

An English version of this post is available

¿Qué es un modelo conceptual? ¿Cómo pueden los modelos conceptuales representar temas complejos de forma efectiva y ayudar a la comunicación entre personas de diferentes disciplinas y formación?

Este artículo describe ConML, abreviatura de “Conceptual Modelling Language” (“Lenguaje de Modelado Conceptual”). ConML es un lenguaje de modelado diseñado para que los investigadores no expertos en tecnologías de la información puedan crear y desarrollar sus propios modelos conceptuales. Se puede aplicar en las humanidades, ciencias sociales, y ciencias experimentales.

¿Qué es un modelo conceptual?

Un modelo conceptual es una representación formal o semiformal de un tema de investigación, y que utiliza conceptos en vez de elementos materiales. Los modelos conceptuales suelen ser visualizados en forma de diagramas y texto complementario, como se muestra en la figura más adelante.

Un lenguaje de modelado es un lenguaje artificial, diseñado para expresar modelos. Ya que los modelos suelen mostrarse en forma de diagramas para mayor comodidad, los lenguajes de modelado suelen incorporar una notación gráfica. Igual que los lenguajes naturales, los lenguajes de modelado poseen:

  • un léxico, es decir, un conjunto de “palabras” que existen en el lenguaje
  • una sintaxis, es decir, un conjunto de reglas que nos dicen cómo se pueden combinar dichas palabras para componer “oraciones” con sentido
  • una semántica, es decir, una descripción de qué relación existe entre cada “palabra” del léxico y las cosas que representa

Las “palabras” de los lenguajes de modelado no se transmiten mediante texto o sonido como en los lenguajes naturales, sino que, habitualmente, lo hacen mediante iconos y dibujos, para ayudar a la visualización formal de los conceptos abstractos. Las reglas sintácticas del lenguaje de modelado nos dicen cómo se pueden conectar dichos iconos para expresar modelos, y qué significa cada tipo de conexión.

ConML contiene muy pocas “palabas”, ya que su sintaxis es muy simple.

El modelado conceptual fue desarrollado dentro de la disciplina de la ingeniería de software, y ha sido aplicado a muchos campos más allá de la misma, como la organización de empresas, la genómica o la arqueología. ConML pretende introducir conocimiento y técnicas que habitualmente solo están disponibles para los ingenieros de software a los especialistas de otros campos, incluyendo las humanidades y ciencias sociales.

Diagrama ConML de ejemplo, que muestra el hecho de los objetos arqueológicos completos suelen estar fragmentados, así como los subtipos que existen de ellos dependiendo de su origen e intencionalidad. (Fuente: Gonzalez-Perez 2018)

¿Cómo pueden los modelos conceptuales representar temas complejos de forma efectiva y ayudar a la comunicación entre personas de diferentes disciplinas y formación?

Existen dos asuntos clave:

  1. Un modelo conceptual nos puede ayudar a comprender mejor la porción del mundo con la que estamos tratando, ya que elimina parte del detalle y complejidad que a menudo la hacen inmanejable.
  2. Un modelo conceptual constituye un lenguaje mediante el cual podemos comunicar afirmaciones sobre dicha porción del mundo, especialmente en esas situaciones que involucran personas de diferentes disciplinas y formación.

El objetivo de ConML es comunicar ideas complejas de forma simple y efectiva.

ConML reconoce que distintas personas y grupos poseen a menudo diferentes puntos de vista subjetivos, que el mundo cambia a lo largo del tiempo, y que nuestro conocimiento de este no siempre es seguro. De este modo, la información que se recoge en un modelo conceptual puede ser, en menor o mayor grado, subjetiva, temporal e incierta. ConML incorpora mecanismos que facilitan la expresión de subjetividad y temporalidad y, hasta cierto punto, de vaguedad.

Al incorporar la capacidad de expresar subjetividad, temporalidad y vaguedad en los modelos – abordando de este modo los problemas habituales de multivocalidad, diacronía e imprecisión o incertidumbre – ConML permite expresar cosas que otros lenguajes no permiten, resultando así especialmente apropiado para las humanidades y ciencias sociales.

Ejemplo

Una de las mayores aplicaciones de ConML ha sido el desarrollo de CHARM, el Modelo de Referencia Abstracto del Patrimonio Cultural.

CHARM representa cosas en términos de conceptos, propiedades y relaciones. CHARM representa cualquier cosa que pueda ser receptora de valor cultural otorgado por cualquier individuo. CHARM no solo representa las entidades que de forma específica pueden componer el patrimonio, sino también otras cosas que son necesarias para describir y comprender las primeras.

CHARM es un modelo descriptivo, no prescriptivo. Los usuarios pueden escoger de entre los elementos que se ofrecen según las necesidades de su organización o proyecto.

Palabras finales

¿Qué experiencia ha tenido con el modelado conceptual? ¿Posee experiencia con ConML y, si es así, ha resultado útil? ¿Ha utilizado otras formas de modelado conceptual?

Para más información:

Reference:
Gonzalez-Perez, C. (2018). Modelado de Información para Arqueología y Antropología. Springer: Cham, Switzerland.

Biografía: César González-Pérez es Científico Titular en el Instituto de Ciencias del Patrimonio (Incipit) del Consejo Superior de Investigaciones Científicas (CSIC), donde lidera una línea de coinvestigación en ingeniería de software y patrimonio cultural. El objetivo último de su trabajo es desarrollar las teorías, metodologías y tecnologías necesarias para comprender y asistir los procesos de creación de conocimiento que ocurren en relación al patrimonio cultural. Previamente, César ha trabajado en diversas organizaciones públicas y privadas, en España y en Australia, tanto en la empresa como en la academia, y en los campos de modelado conceptual, metamodelado e ingeniería situacional de métodos. Ha fundado tres empresas tecnológicas, ha servido como miembro electo del comité directivo de la asociación Computer Applications and Quantitative Methods in Archaeology (CAA), y es autor o coautor de más de 100 publicaciones. Las áreas de mayor interés de César en la actualidad son la aplicación de técnicas de modelado de la información y el conocimiento en humanidades, y la conexión entre inferencia, discurso y evolución ontológica.

 

A checklist for documenting knowledge synthesis

Community member post by Gabriele Bammer

Gabriele Bammer (biography)

How do you write-up the methods section for research synthesizing knowledge from different disciplines and stakeholders to improve understanding about a complex societal or environmental problem?

In research on complex real-world problems, the methods section is often incomplete. An agreed protocol is needed to ensure systematic recording of what was undertaken. Here I use a checklist to provide a first pass at developing such a protocol specifically addressing how knowledge from a range of disciplines and stakeholders is brought together.

KNOWLEDGE SYNTHESIS CHECKLIST

1. What did the synthesis of disciplinary and stakeholder knowledge aim to achieve, which knowledge was included and how were decisions made? Continue reading

Are more stakeholders better?

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Eleanor Sterling (biography)

Community member post by Eleanor Sterling

Participatory modeling, by definition, involves engaging “stakeholders” in decision making. But determining which stakeholders to involve, when, and how is a delicate balance. Early writings on stakeholder engagement methods represent engagement along a linear continuum from non-participatory to citizen-controlled decision making.

Non-participatory methods could include stakeholders passively receiving pre-set information, with no input to content or delivery (eg., public information campaigns). Fully collaborative partnerships (eg., participatory action research projects) involve co-creation of knowledge, co-identification of issues, and co-framing of and implementation of solutions. Continue reading

Sharing mental models is critical for interdisciplinary collaboration

Community member post by Jen Badham and Gabriele Bammer

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Jen Badham (biography)

What is a mental model? How do mental models influence interdisciplinary collaboration? What processes can help tease out differences in mental models?

Mental models

Let’s start with mental models. What does the word ‘chair’ mean to you? Do you have an image of a chair, perhaps a wooden chair with four legs and a back, an office chair with wheels, or possibly a comfortable lounge chair from which you watch television? Continue reading

Managing deep uncertainty: Exploratory modeling, adaptive plans and joint sense making

Community member post by Jan Kwakkel

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Jan Kwakkel (biography)

How can decision making on complex systems come to grips with irreducible, or deep, uncertainty? Such uncertainty has three sources:

  1. Intrinsic limits to predictability in complex systems.
  2. A variety of stakeholders with different perspectives on what the system is and what problem needs to be solved.
  3. Complex systems are generally subject to dynamic change, and can never be completely understood.

Deep uncertainty means that the various parties to a decision do not know or cannot agree on how the system works, how likely various possible future states of the world are, and how important the various outcomes of interest are. Continue reading

Good practices in system dynamics modelling

Community member post by Sondoss Elsawah and Serena Hamilton

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Sondoss Elsawah (biography)

Too often, lessons about modelling practices are left out of papers, including the ad-hoc decisions, serendipities, and failures incurred through the modelling process. The lack of attention to these details can lead to misperceptions about how the modelling process unfolds.

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Serena Hamilton (biography)

We are part of a small team that examined five case studies where system dynamics was used to model socio-ecological systems. We had direct and intimate knowledge of the modelling process and outcomes in each case. Based on the lessons from the case studies as well as the collective experience of the team, we compiled the following set of good practices for systems dynamics modelling of complex systems. Continue reading