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.

conML_cesar-gonzalez-perez_english-image
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.

 

Scatterplots as an interdisciplinary communication tool

Community member post by Erin Walsh

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Erin Walsh (biography)

Scatterplots are used in many disciplines, which makes them useful for communicating across disciplines. They are also common in newspapers, online media and elsewhere as a tool to communicate research results to stakeholders, ranging from policy makers to the general public. What makes a good scatterplot? Why do scatterplots work? What do you need to watch out for in using scatterplots to communicate across disciplines and to stakeholders?

What makes a good scatterplot? Continue reading

Synthesis of knowledge about participatory modeling: How a group’s perceptions changed over time

Community member post by Rebecca Jordan

Rebecca Jordan (biography)

How do a group’s perceptions change over time, when members across a range of institutions are brought together at regular intervals to synthesize ideas? Synthesis centers have been established to catalyze more effective cross-disciplinary research on complex problems, as described in the blog post ‘Synthesis centers as critical research infrastructure‘, by Andrew Campbell.

I co-led a group synthesizing ideas about participatory modeling as one of the activities at the National Socio-Environmental Synthesis Center (SESYNC). We met in Annapolis, Maryland, USA, four times over three years for 3-4 days per meeting. Our task was to synthesize what is known about participatory modeling tools, processes, and outcomes, especially in environmental and natural resources management contexts. Continue reading

What can interdisciplinary collaborations learn from the science of team science?

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Suzi Spitzer (biography)

Community member post by Suzi Spitzer

How can we improve interdisciplinary collaborations? There are many lessons to be learned from the Science of Team Science. The following ten lessons summarize many of the ideas that were shared at the International Science of Team Science Conference in Galveston, Texas, in May 2018.

1. Team up with the right people
On the most basic level, scientists working on teams should be willing to integrate their thoughts with their teammates’ ideas. Participants should also possess a variety of social skills, such as negotiation and social perceptiveness. The most successful teams also encompass a moderate degree of deep-level diversity (values, perspectives, cognitive styles) and include women in leadership roles. Continue reading

CoNavigator: Hands-on interdisciplinary problem solving

Community member post by Katrine Lindvig, Line Hillersdal and David Earle

How can we resolve the stark disparity between theoretical knowledge about interdisciplinary approaches and practical applications? How can we get from written guidelines to actual practices, especially taking into account the contextual nature of knowledge production; not least when the collaborating partners come from different disciplinary fields with diverse expectations and concerns?

For the past few years, we have been developing ways in which academic theory and physical interactions can be combined. The result is CoNavigator – a hands-on, 3-dimensional and gamified tool which can be used:

  • for learning purposes in educational settings
  • as a fast-tracking tool for interdisciplinary problem solving.

CoNavigator is a tool which allows groups to collaborate on a 3-dimensional visualisation of the interdisciplinary topography of a given field or theme. It addresses the contextual and local circumstances and the unique combinations of members in collaborative teams. CoNavigator is therefore short for both Context Navigation and Collaboration Navigation. The process of applying the tool takes around 3 hours.

Using CoNavigator

CoNavigator is composed of writable tiles and cubes to enable rapid, collaborative visualisation, as shown in the first figure below. The tactile nature of the tool is designed to encourage collaboration and negotiation over a series of defined steps.

Making the Tacit Visible and Tangible

Each participant makes a personal tool swatch. By explaining their skills to a person with a completely different background, the participant is forced to re-evaluate, re-formulate, and translate skills in a way that increases their own disciplinary awareness. Each competency that is identified is written onto a separate tool swatch.

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Katrine Lindvig (biography)

line-hillersdal
Line Hillersdal (biography)

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David Earle (biography)

Continue reading

Knowledge mapping technologies

Community member post by Jack Park

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Jack Park (biography)

How can you improve your thinking – alone or in a group? How can mapping ideas help you understand the relationships among them? How can mapping a conversation create a new reality for those involved?

In what follows, I draw on the work of Daniel Kahneman’s (2011) best-selling book Thinking, Fast and Slow, which explains how human thinking occurs at different speeds, from the very fast thinking associated with face-to-face conversation to the very slow thinking associated with assembling information resources into encyclopedias. I use those ideas in my descriptions of knowledge maps.

Three kinds of knowledge maps Continue reading