Map & Tree based Navigation

Knowledge Map
Much of  Now & Then's intelligence comes from its extraction and mapping of your knowledge, your vocabulary, and its you-centric meta-knowledge (knowledge about knowledge) into a multi-dimensional matrix or Knowledge Map. Now & Then uses this knowledge about your world and your use of language to make "good guesses" regarding the multiple ways and extent each piece journal information is associated and related to every other piece of journal information.

The Knowledge Map tracks the ever changing associations and relationships between subject matter, topics, co-occurrences, categories, terms, and user customization, feedback, and rules. To further enhance its retrieval accuracy,  Now & Then parses the content and context of every Query using Bayesian statistics and the Intelligent  Calendar.

Because the Knowledge Map is an multi-dimensional rather than hierarchical representation of your journal's knowledge it cannot be visualized and navigated like a flat map, ontology, or Web-like network. While we can visualize in only three dimensions, the Knowledge Map's topology may include thousands of dimensions.

It uses "knowledge agents" to automatically recognize and statistically classify categories, topics, and other thematic clusters and ontologies. These are represented in n-dimensions according to their relative, context dependent, "one-to-many" and "many-to one" associations.

Knowledge Tree
The Knowledge Tree is a three dimensional representation of  the multi-dimensional Knowledge Map. It is a convenient and surprisingly agile tool for navigating your journal's information.

You use the Knowledge Tree to visually and flexibly browse and review your journal information while keeping a sense of (contextual) place and direction. Your Knowledge Tree is virtual rather than static or hierarchal. It graphically represents the query-dependent associations and connections among the information stored in your journal.

The content and context of your Queries determine your "current focus" and your initial Knowledge Tree "branch" or "point of entry". Branches nearest your current focus reference journal information that most closely satisfies your Query. More distant branches represent journal information that is contextually less related to your last Query.

Starting from your entry point or current focus you can navigate through the Knowledge Tree from branch to branch―changing your current focus with each move. You can follow your interests and ideas from branch to branch by pointing and clicking, or by modifying your original Query. Using the visual tree and an evolving Query, you can nudge your search in new directions and follow your interest wherever your muse takes you. Use structure and specificity in your Query parameters, such as with Topic and Category to limit ambiguity in the (tree) branches around your Query's current focus.

Often, your tree will pull together "far flung" journal information. Tree branches, from trunks to twigs, represent a "virtual" collection of journal information that satisfies your Query. Any virtual branch might include combinations of single memoirs, timelines populated according to a Topic, a calendar series, virtual narrative spanning the entirety of your journal, a Web page, a tracking table, etc.

In case you want to backtrack, your navigation history is always available―listed on a dropdown menu. Another dropdown menu provides navigation suggestions based on  Now & Then's understanding of your interests, browsing trends, and its all-knowing perspective of your journal's knowledge, associations, categories, terms, and rules.

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