P.S. to my previous post: (01)
The bottom line on RELATIONS (AS VERBS) is
that as a result of analysis involving "algebraic" operations you will get:
1) additive relations, such as parts and a whole, where interface is important
when you do synthesis. Otherwise your chunking is not appropriate.
2) Productive relations, such as mental operations, including abstraction,
isolation, formalization, etc. where nothing is dissected in reality, but new
ideas are created all the time and where semantic primitives (objects,
properties and relations) may be used to show the facet required in producing
the related propositions. As the result of the synthsis you get back where you
started from: Object - Existence/Non-existence, etc. The steps in this
procedure
may be numerically dentified so that you can find your way back. This does not
look like a tree or a forest, but as a cycle, an infinite number of rings in
SPACE and TIME as opposed to the mazes of knots in 2D (02)
By the way, I have just received a draft paper written still along the lines
you
tend to think about the subject (03)
If you are interested, contact the author as his paper is detached from here. (04)
________________________________ (05)
Hi, (06)
There goes a draft paper, comments are welcome! (07)
Semantic Data Aggregation through Semiotics
Facilitating querying and inferencing (08)
Sebastián Samaruga
http://xama.dev.java.net
xama@xxxxxxxxxxxx (09)
Abstract (010)
Trying to fill the gap between real business (intelligence) domain applications
and semantics through extensive data aggregation and a functional approach to
knowledge representation through semiotics. (011)
Introduction (012)
Given Semiotics, and Semantics, which is a branch of Semiotics, regarding
Peirce, along with Syntax / Grammar, and Pragmatics, the relationship arises
that given three entities regarded as: Sign – Concept – Object, considering
(Sowa[1]) “A sign has three aspects: it is (1) an entitythat represents (2)
another entityto (3) an agent” that our underlying model can be composed of
three classes, namely: (013)
* Type
* Value
* Name (014)
Given this basic 'units' of knowledge, we should model our data according to
some rules so we can make useful things given this arrangement. The first step
is to find a common 'meta – meta – model” for the model stated before so
we can
'import' data from disparate sources into it. The data is ultimately aggregated
into this three structures given meaningful parsing of it (and configuration
files). (015)
Meta Meta Model (016)
The underlying common model for entities coming from diverse data sources
should
allow to covert from and to the 'model' easyly. Let's begin considering what a
data structure could become after decomposing it a little. We should consider,
for example, rows, or statements (from RDF), predicates or columns from a
relational database and tables or types (rdf:Type) for example, from these two
kind of data sources (RDBMs and RDF). (017)
Lets arrange them into objects of different classes. The name in the left is
the
name of the class, and the three value tuple named 'statements' is the
arrangement of statements about other entities the object has: (018)
Mapping:
Statements: <Context, Entity, Role>
Entity:
extends Mapping. Statements: <Context, Mapping, Role>
Context:
extends Entity, Statements: <Entity, Mapping, Role>
Role:
extends Context, Statements: <Context, Mapping, Entity> (019)
So, the Statements part is the references the object has to other objects in
the
data space, in the form of 3-tuples. The inheritance relationship is for
allowing reification and further composition. The correspondences between these
objects and a data source are roughly this: (020)
A Mapping represents a row (in a database table) or an RDF statement.
An entity represents a value in a table cell or an RDF object.
A context represents a table in a database or rdf:Type value of statement in
RDF.
A role represents a database column or a RDF predicate. (021)
The population of the model should allow for triadic relationships to be stated
over the model, and to be accessible for querying in a meaningful way. (And the
use of configuration mapping files for population of upper models) (022)
For example, in a Value x, let's say (200Km), we could 'operate' semiotically
on
it and 'ask' it for a reference to its related Type object, given a Name, let's
say ('Distance'), and once we have the Type we arrived from the value,
regarding
it as that name, ask the Type object for a Value named ('Speed') and get
(100Km/h). If we query using the same mechanism for a Value named ('Time') we
should get (2h). (023)
Architecture (024)
The idea is building level over level based on mapping configurations files in
XML that describe how entities in a lower level populates entities in an upper
level. This should give us the layers of metametamodel (for data load), model
(for inference, semantics) and later a business and agent layer to ease to
provide user interface, reporting and interaction layers. (025)
The whole system should provide, through the use of accessory packages, such as
a framework for FCA (FCA[2]) and information gathering and retrieval
(Watson[3])
for the build up of a kind of Business Intelligence (2.0) application
framework,
with dimensional and aggregated views of semantically integrated data. (026)
The project page of the ongoing development effort for this framework is online
and available at: http://xama.dev.java.net (027)
References (028)
1. JF Sowa,
“Ontology, Metadata and Semiotics”
http://users.bestweb.net/~sowa/peirce/ontometa.htm (029)
2. Formal concept analysis:
http://en.wikipedia.org/wiki/Formal_concept_analysis (030)
3. Mark Watson ,
“Practical Artificial Intelligence Programming With Java , Third Edition” (031)
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