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?

In his 1983 magnum opus, The Visual Display of Quantitative Information, statistician Edward Tufte outlined nine principles of excellence and integrity in data visualisation:

  1. Show the data
  2. Induce the viewer to think about the substance rather than about methodology, graphic design, the technology of the graphic production or something else
  3. Avoid distorting what the data have to say
  4. Present many numbers in a small space
  5. Make large datasets coherent
  6. Encourage the eye to compare different pieces of data
  7. Reveal the data at several levels of detail, from a broad overview to a fine structure
  8. Serve a reasonably clear purpose: description, exploration, tabulation or decoration
  9. Be closely integrated with the statistical verbal descriptions of a dataset.

Noting “Graphics reveal data” (1983: 13), Tufte presented the classic case of Anscombe’s Quartet (Anscombe 1973) as an example of successful application of these principles. X, Y, and the relationship between X and Y in Anscombe’s four datasets are numerically indistinguishable (sharing a mean, variance, and correlation). Viewed as pure numbers, it is difficult to see any difference between the sets:

(Data generated by Erin Walsh in accordance with Anscombe’s Quartet (Anscombe 1973))

Striking differences become immediately obvious once they are displayed as scatterplots.

(Source: Erin Walsh)

This demonstrates the importance of data visualisation in a broad sense, and more specifically shows the power of the commonplace scatterplot.

Emerging late in nineteenth century, scatterplots are ubiquitous in the modern data visualisation landscape. Whether a simple monochrome display with two axes, or enhanced through colour, interactivity, motion, or the addition of a third dimension, scatterplots are in widespread use.

Why do scatterplots work?

So, what makes scatterplots so versatile? Scatterplots are remarkably accessible because their interpretation leverages the universal human capacity for pattern recognition. Apophenia is the unprompted awareness of connections and meaningfulness of phenomena.

Such heuristics are evolutionarily vital for making sense of ever-changing complex visual input that may represent important predator, prey or social interaction information. A more subtle, but equally pervasive example of apophenia is the tendency to connect points to find lines, trends, and patterns. Scatterplots convey perceptually simple information, points within a field, which is straightforward to encapsulate neutrally and perceptually. The combination of perceptual simplicity and bootstrapping of apophenic tendencies provide what appears to even lay viewers as conceptual simplicity and straightforward meaning extraction. This underlies the scatterplot’s appeal for conveying knowledge both within, across and beyond disciplinary boundaries.

What do you need to watch out for in using scatterplots to communicate across disciplines and to stakeholders?

  • For cross-disciplinary communication:
    • Be aware of differences in conventions that underpin the data or topic (eg., in chemistry beta means something very different from beta in psychology).
  • In the context of a single plot:
    • Try to always keep Tufte’s principles of excellence and integrity in data visualisation in mind.
    • Give yourself time to properly generate the plot (too many people leave it to the last-minute).
    • Honest mistakes:
      • Too much data/overcrowding points.
      • Trying to say too much at once (multiple groups denoted by size and shape and colour…).
      • Too little (poor axis labels) or too much (caption takes more space than the figure) context.
    • Signs of nefarious intent:
      • Truncated axes without disclosure.
      • Aspect ratio distorted to exaggerate trends.
      • Plotting things which don’t make sense.
  • In the context of the larger communication, if multiple plots:
    • Use a consistent aesthetic across plots (so the eye focuses on meaning, not wondering why the fonts on the axes are different, or the colour scheme has changed).
    • Don’t use too many plots (only important things need a figure; nobody will properly read 10+).

When have you found scatterplots helpful for either obtaining or sharing knowledge? Are there circumstances where they got in the way of information exchange?

References:
Anscombe, F. J. (1973). Graphs in Statistical Analysis. The American Statistician, 27: 17-21

Tufte, E. and Graves-Morris, P. (1983). The visual display of quantitative information. Graphics Press: Connecticut, United States of America.

Biography: Erin Walsh PhD is a postdoctoral fellow at the Centre for Research on Ageing, Health and Wellbeing, Research School of Population Health, The Australian National University in Canberra, Australia. She is also a freelance scientific illustrator with over ten years of experience converting scientific ideas, data, and excitement into visual form. Her primary research interest is the impact of blood glucose on the ageing brain, which she investigates with an eclectic cross-disciplinary range of concepts and statistical techniques, spanning the fields of animal biology, psychology, geography, computer science and population health.

Erin Walsh is a member of blog partner PopHealthXchange, which is in the Research School of Population Health at The Australian National University.

Developing a ‘capabilities approach’ for measuring social impact

Community member post by Daniel J. Hicks

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Daniel J. Hicks (biography)

Why do familiar metrics of impact often seem “thin” or to miss the point of research designed to address real-world problems? Is there a better way to measure the social impact of research?

In a recent paper (Hicks et al., 2018), my coauthors and I identified a key limitation with current metrics and started to look at how concepts from philosophy — specifically, ethics — can help us explain the goals of our research, and potentially lead to better metrics.

What’s the problem?

To understand the limitations of current metrics for measuring the social impact of research, it is useful to understand two distinctions, between resources and goals and between inward-facing and outward-facing goals for research. Continue reading

Research impact in government – three crucial elements you will need for success

Community member post by Anthony Boxshall

anthony-boxshall
Anthony Boxshall (biography)

What is the less visible ‘stuff’ that helps (or hinders) the uptake of research findings into government policy?

As a researcher it can be frustrating to have a great idea, connected to a seemingly important need, and even good networks, and yet still not be able to help your research have impact in the daily life of the relevant public sector decision-makers.

From more than 20 years of being involved in and with the senior decision-making levels of public sector environment agencies and running a business all about increasing the impact of science into public sector decision-making, I offer three insights that you should look for to see if the time and place are right for the uptake of your research. If these three elements exist, your research stands a good chance for uptake. Continue reading

Transforming transdisciplinarity: Interweaving the philosophical with the pragmatic to move beyond either/or thinking

Community member post by Katie Ross and Cynthia Mitchell

Katie Ross (biography)

Can a dive into the philosophical depths of transdisciplinarity provide an orientation to the fundamental purpose and need for transdisciplinarity?

The earlier philosophers of transdisciplinarity – such as Erich Jantsch (1980), Basarab Nicolescu (2002), and Edgar Morin (2008) – all aim to stretch or transcend the dominant Western paradigm, which arises in part from Aristotle’s rules of good thought. Aristotle’s rules of good thought, or his epistemology, state essentially that to make meaning in the world, we must see in terms of difference; we must make sense in terms of black and white, or dualistic and reductive thinking. Continue reading

Skilful conversations for integration

Community member post by Rebecca Freeth and Liz Clarke

Rebecca Freeth (biography)

Interdisciplinary collaboration to tackle complex problems is challenging! In particular, interdisciplinary communication can be very difficult – how do we bridge the gulf of mutual incomprehension when we are working with people who think and talk so very differently from us? What skills are required when mutual incomprehension escalates into conflict, or thwarts decision making on important issues?

It is often at this point that collaborations lose momentum. In the absence of constructive or productive exchange, working relationships stagnate and people retreat to the places where they feel safest: Continue reading