SUO: Re: Critique Of Non-Functional Reason
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I will arrange the remains of my say according to the following desiderata:
| A. Automated Reasoning (AR)
|
| The standard will be suitable for automated logical inference
| to support knowledge-based reasoning applications.
| B. Inter-Operability (IO)
|
| The standard will provide a basis for achieving Inter-Operability
| among various software and database applications.
Remarks having to do with A or B will be tagged "A" or "B".
Comments on the integration of A and B will be tagged "AB".
In my own frame of reference, I think of AR as having to do with providing
computational support for the concept-driven, inferential, or "rational"
aspects of human intelligence, while I think of IO as having to do with
the data-driven, experiential, or "empirical" aspects of our full human
capacities for learning and reasoning about the world that we describe.
I began my own work in AI trying to implement basic capacities for describing
simple universes of discourse, for reasoning with these descriptions (AR, in
the paradigm that was called "mechanized mathematical reasoning"), and for
learning conceptual descriptions from the rawer forms of description that
are typically known as "data" (IO, in the sense of an acronymic pun that
I think I will choose to make intentional). In the process, I learned
a bit about why the rational and the empirical faculties are so hard
to integrate over a common core of resources and utilities, and the
rub lies only partly in the shape of the models and the tools that
we have come to build, but more in the nature, or maybe just the
incidentally acquired character, of ourselves, the creatures
who built these technologies in our own present image.
And there lies the toughest part of the problem,
for it has come round to ourselves, once again,
as every so often it recurrently does, and the
circumstance is that developing the technology
will now take an effort to change that image.
Jon Awbrey
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IEEE Standard Upper Ontology
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Scope & Purpose
Scope of Proposed Project:
The Scope describes what is being done,
including the technical boundaries of the project.
This standard will specify an upper ontology that will
enable computers to utilize it for applications such as
data interoperability, information search and retrieval,
automated inferencing, and natural language processing.
An ontology is similar to a dictionary or glossary, but
with greater detail and structure that enables computers
to process its content. An ontology consists of a set of
concepts, axioms, and relationships that describe a domain
of interest. An upper ontology is limited to concepts that
are meta, generic, abstract, and philosophical, and therefore
are general enough to address (at a high level) a broad range
of domain areas. Concepts specific to given domains will not be
included; however, this standard will provide a structure and a
set of general concepts upon which domain ontologies (e.g. medical,
financial, engineering, etc.) could be constructed.
Purpose of Proposed Project:
A. AUTOMATED REASONING.
The standard will be suitable for automated logical inference
to support knowledge-based reasoning applications.
B. INTER-OPERABILITY.
The standard will provide a basis for achieving Inter-Operability
among various software and database applications.
1. Application developers can define new data elements
in terms of a common ontology, and thereby gain some
degree of interoperability with other conformant systems.
2. Applications based on domain-specific ontologies that are compliant
with this standard will be able to interoperate (to some degree) by
virtue of the shared common terms and definitions.
3. The SUO will play the role of a neutral interchange format whereby
owners of existing applications will be able to map existing data
elements just once to a common ontology. This provides a degree
of interoperability with other applications whose representations
conform to SUO. This entails the SUO being able to be mapped to
more restricted forms such as XML, database schema, or object
oriented schema.
C. APPLICATION AREAS.
1. E-commerce applications from different domains
that need to interoperate at both the data and
semantic levels.
2. Educational applications in which students learn concepts
and relationships directly from, or expressed in terms of,
a common ontology. This will also enable a standard record
of learning to be kept.
3. Natural language understanding tasks in which a knowledge-based
reasoning system uses the ontology to disambiguate among likely
interpretations of natural language statements.
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