In the near future, Joe is sitting down in a comfortable chair with fresh iced coffee, lights down low and comfortable, quiet music playing. It’s raining outside, but not too much. He feels good and is happy to have some time to focus on his work. He looks at his beautiful 27” screen starts tabbing through his favourite news websites to engage his brain this early morning. He then looks at his To Do list and remembers he is due to submit a research paper.
Since Joe is a professor at a prestigious university, at the forefront of his mind is the need for the research paper to be a good model for his students. He therefore aims to write as clearly and well thought-through as possible and showing his evidence clearly and with citations to source in ways a reader can instantly check.
This scenario covers basic concept mapping in a dynamic view mode, what Marc Antoine refers to as normal research, instant searches and then enters graph space to follow an argument and returns to the document with the results and adds an element through the hyperGlossary.
Covered Applications include Liquid | Author a word processor, Liquid | Flow, a text utility for instant searches and more and Liquid | View, a concept map application with the ability to serve nodes and to use nodes from other applications through an open API (related to the graph API I would expect).
Joe doesn’t know everything about the subject but he knows a few things so he opens a new workspace in his Liquid | View application and lays out what he knows as short sentences as Post its called ‘nodes’ (same root as knot, so a very apt term for something which is designed to link) quite randomly. Once he has finished typing what he can think of, he starts to group the nodes and then he gives the groups headings (in View the default node is level 2 and headings level 1). This way of brain storming and seeing structure emerge is very much like the KJ-Ho method from Japan.
His screen now has some structure so he opens his Liquid | Author word processor and chooses to Live Import from Liquid | View. Here he has a single column word processor view where the nodes now are headings, and he starts writing prose under each heading, some for himself to remind him why the heading is there, some for the final paper.
He expands and collapses the text to aid his reading of it (to see only sentences with certain keywords, to see headings and names instead of body text or pictures instead of people’s names or company names etc.), looking up terms instantly when he is not sure of their correct use using Liquid | Flow.
He moves around his text document with extreme ease, giving him the conceptual map of his work as it developed to allow him to really focus on following the nub of the issue he is trying to understand and present.
Joe pulls his document to one side and looks at his citation space (no app or name assigned as of yet). Here he searches for articles he has read, by keyword, author, title and so on and stretches the citations out in space across the screen, from old on the left to newer on the right, with less cited on the bottom of the screen and more cited towards the top. He finds a few relevant articles and puts them aside as he follows his intuition to better understand what traditional documents have to offer on the issue.
He cites books, academic articles, web sites, videos and audio with ease, allowing a reader to follow the citation not just to the cited document, but right inside the document to the exact location, to determine it’s relevance, accuracy and importance.
After completing this basic research he delves into the connection space of knowledge graphs to see how others have approached the issue.
- Here he follows interactions as primarily specified by Marc Anthony (my assumption is this would entail large scale interactions).
- He then transfers references across from the connection space into his document as Gyuri scenario (my assumption here is that this would be more detailed or personal interaction.
As he puts the finishing touches on his document, he realises that a few words are used differently by him than would be by a potential reader, so he adds glossary entries for them. As he does so, the graph spaces he has been connected to appears with any relevant other entries, which he can, through simple drag and drop, choose to include or dispute.
Once done, any glossary term will show up in his document for the reader in the way the reader prefers, with the default being as hard brackets after the keywords  which can be clicked on to expand and show the short definition right in the text.
Since he added the glossary entries carefully, including adding explicit relationships to other terms, the reader can choose to see the terms in context in a graph space.
When he is done, Joe Publishes his document by going through a few Publish modules. His main authoring system, Liquid | Author, first does a spell check, grammar check, reading level and plagiarism check to save him any potential embarrassment. He then gets a summary/abstract automatically generated where he can see if he really did communicate what he thought he communicated. If he finds that the abstract was not what he intended, he can click on any sentence to see what parts of the document contributed to that summary segment and edit it. He can also add text to the summary but then he will need to tag the sections in the document which explicitly expand on those points. The end result will be that the summary will be sufficient for most readers, only the very interested will need to delve deep to understand the full document.
He then emails a rich PDF exported document to the review body, of whom some only have the ability to read the surface PDF render, others use their own advanced graph-aware systems and a few have the same main authoring package Joe used, Liquid | Author, and thus have access to the original format .liquid document Joe worked on (minus whatever meta he chose to scrub).
Those reading his document, apart from the PDF only readers, will be able to get a lot of meaning out of the core document and the document will be fully connected to cited sources and graph spaces for full, rich interaction. They will be able to explode the document in a myriad of ways and see relationships with citations and graphs in any granularity they choose–reading the document has become as interactive and almost as fun as playing a video game.