5 Responses

  1. Till C. Lech
    Till C. Lech December 16, 2009 at 12:07 |

    Ontologies vs. mental models

    Great blog post, Vegard! Just a small comment on the topic on mental models vs ontologies: You do perfectly right in calling the categories in your mind a mental model rather than an ontology.

    While an ontology (along the lines of Gruber’s definition) is a formal, explicit specification of some conceptualization that allows for categorisation, and (if it is any good) some degree of reasoning, our mental models are implicit, flexible and can be re-arranged in an ad-hoc manner any given time. Within our mental models we can “pogo-stick” between our internal tabs in a ridiculously effortless manner. Which is, for example, why we understand jokes (if the joke is any good).

    This, needless to say, is why categorisation is so hard and why it so often fails. Which underlines the importance of your first point: Get into your users’ heads and find out how they organise their world.

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  2. Ted Elvhage
    Ted Elvhage December 16, 2009 at 12:12 |

    Great blog post Vegard! I love your thinking about thinking.

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  3. Jan Høydahl
    Jan Høydahl December 18, 2009 at 14:18 |

    Great article Vegard. The idea of identifying structure and scents is crucial in planning for an enterprise search application.

    However, applying the model to your data is very often a tough job. How to you tag all those documents, intranet pages, articles and other pieces of information with the right category(ies) without relying on too much manual human effort, and without burdening information producers with the boring task of filling in a ton of meta data (which in practice will not be done according to experience)?

    For instance, how do you come up with the disambiguation question for “Dolphins”? Or “Apple”? Manual classification can be a veery time consuming endeavor and even building the rules for automatic classification is most often beyond the clients budgets and expectations.

    I’ve found that, for domains with mainly unstructured data, build only a small category tree for categories which can easily and reliably be deduced from the data itself or metadata such as data source, file path etc. For digging deeper, rely on identifying meaningful entities of information and extract those automatically from the text and offer them as facets or tag clouds. Structured, normalized data soruces are of course much easier to deal with.

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