ORB Visualization
(soon)
2/29/2004 8:58 PM
new memetic
technology
Design document from Nathan
The Ontology Referential Base (ORB) represents a new memetic technology. The ORB encodes all co-occurrence of words (where these words are from a controlled vocabulary) and is covered by a provisional patent. We are not interested in restricting the use of ORBs - unless someone is unwilling to empower the core sciences.
http://www.bcngroup.org/area2/KSF/Notation/notation.htm
The encoding of these ORBs is in two forms. One is a declarative logic over a set
{ < a, r, b > }
and the other is as a graph similar to NdCore™’s CCM model.
The graph of the ORB is visualized to facilitate human understanding of the information processed into the ORBs. These visualizations are then used to assist in searching over an inverted index, created over a controlled vocabulary, for data retrieval at the conceptual/memetic level.
The declarative logic is unlike OWL since it is not a first order predicate logic. It is a logic where inference steps MUST be made by a human inductive (cognitive) process.
This entire memetic visualization, retrieval, and categorization system represents the core of the distributed Virtual Memetics Laboratory (VML), which, once developed, can be implemented into any existing Groove space to accomplish any number of memetic related technological requirements within a distributed, secure, and easy-to-use environment. The VML is part of a Peer to Peer MUD (multiple user domain), and has a separate (for any platform) distributed P2P operating system call the KOS (Knowledge Operating System).
Within the context of operating within Groove, the ORB Groove Tool development will create a tool with the following process flow:
1) Harvest text from Groove space
1.1) ORBs are produced using the parsing and convolution methods discussed in the notational paper
2) Humans interaction with visual patterns to identify significant concepts
2.1) A controlled vocabulary is created to correspond to these visual patterns
3) Create an index on controlled vocabulary over entire text set
4) Visualizations inventoried for significant neighborhoods
4.1) The visual presentation of neighborhoods is used to search inverted index
5) Update Process
5.1) Monitor for new text within space
5.1.1) New text is processed to create new neighborhoods into ORB, and indexed for controlled vocabulary accordingly
5.1.2) Re-populate the ORB with the new neighborhoods for that file
5.2) New significant neighborhoods are developed as discovered in new/updated files, and controlled vocabulary expanded
5.3) The update process is continually repeated
This creates a situation whereby a constant index across the controlled vocabulary is kept, while visualizations of all concepts is available to managers, and the significant concepts are available to all members to facilitate in better understanding the text set as a whole.
The ORBs can also be exported for visualization and processing within the OntologyStream SLIP Browser technology.