While there isn't a widely recognized, standardized ontology specifically designed for software design patterns, the concept of using ontologies to represent and reason about software design patterns is an active area of research.
Here's how ontologies can be applied to software design patterns:
1. Formalizing Pattern Knowledge:
2. Enabling Pattern Discovery and Recommendation:
3. Supporting Automated Reasoning and Code Generation:
4. Facilitating Knowledge Sharing and Collaboration:
Challenges and Considerations:
Potential Benefits:
While there's still much work to be done in this area, the potential benefits of using ontologies for software design patterns are significant. By addressing the challenges and leveraging the opportunities, researchers and practitioners can advance the state of the art in software engineering.
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Hi John,
I’ve used your https://jfsowa.com/talks/eswc.pdf reference to generate an article using following workflow, courtesy of our AI Middleware Layer using OpenAI’s GPT-4o language model API:
Summarize https://jfsowa.com/talks/eswc.pdf.
Redo, but incorporating the following:
Improve by adding a section titled “Contemporary View in the Age of LLM-based Natural Language Processors”:
Redo, but in a markdown block.
Improve by incorporating the quotes in the attached image.
This document explores the integration of logic, cognitive science, and computation in knowledge representation (KR) and its applications to the Semantic Web. It provides insights into foundational principles, challenges, and future directions in building intelligent systems that align with human understanding. Additionally, it discusses the transformative impact of modern tools like Large Language Models (LLMs) on traditional KR workflows, particularly in the domains of ontology generation, semantic integration, and data-driven reasoning.
The process of knowledge representation, much like the creative process in mathematics and physics, is deeply visual and conceptual. Sowa’s ideas align with the insights of mathematician Paul Halmos and physicist Albert Einstein:
Paul
Halmos on Mathematics:
Halmos suggests that the act of doing mathematics is not
inherently deductive but rather imaginative. A
mathematician’s work involves “vague guesses,
visualizing broad generalizations, and jumping to
unwarranted conclusions” to gain insight. He
highlights the distinction between the creative and
deductive stages of reasoning, where proving
rigorously is more of a technical task that follows the
initial creative insight.
“The deductive stage, writing the results down, and writing its rigorous proof are relatively trivial once the real insight arrives; it is more the draftsman’s work, not the architect’s.” — Halmos (1968)
Albert
Einstein on Physics:
Einstein emphasizes that thought, especially his, is largely
non-verbal and visual. He describes ideas
as associative images that are “voluntarily
reproduced and combined”, with words coming only
as a secondary process after visualization and mental
modeling.
“The words or the language, as they are written or spoken, do not seem to play any role in my mechanism of thought… The above-mentioned elements are, in my case, of visual and some of muscular type.” — Quoted by Hadamard (1945)
Relevance to
KR:
Both Halmos and Einstein underline the significance of visualization
and conceptualization in the creative process.
Similarly, in KR, the conceptual graph and ontology
modeling stages often mirror this visual, creative
phase, while the deductive reasoning and querying processes
align with the draftsman’s work of formalization. The interplay
of these two stages is crucial for developing effective KR
systems.
What
is Knowledge Representation (KR)?
KR is a method of encoding information so that both humans
and machines can interpret, reason, and use it.
Why
is KR important for the Semantic Web?
KR enables meaningful integration of data across diverse
domains, supporting reasoning and query answering in
distributed environments.
What are the main challenges in KR?
How
do Conceptual Graphs (CGs) relate to KR?
CGs are a visual, logic-based notation inspired by Charles
Sanders Peirce that facilitates reasoning and data
integration.
What
is the relationship between KR and cognitive science?
KR draws on human cognitive processes to design systems that
reason intuitively and interact effectively with users.
How
do RDF, OWL, and SPARQL fit into KR?
These are tools and languages that implement KR principles
for structuring, querying, and reasoning with web data.
How
do LLMs affect ontology generation?
LLMs automate concept extraction, align schemas, and assist
in iterative refinement, reducing manual effort.
What
reasoning methods are emphasized in KR?
Deductive reasoning is primary, but inductive and abductive
reasoning are also important for dealing with uncertainty
and incomplete data.
What
role does scalability play in KR?
Scalable KR systems can manage the vast data of the Semantic
Web while maintaining reasoning capabilities.
What
are the future directions for KR?
Future research emphasizes hybrid reasoning systems,
commonsense knowledge, and dynamic, context-aware
ontologies.
Building a Knowledge Graph:
Creating Conceptual Graphs (CGs):
Integrating Ontologies:
Reasoning with KR:
Leveraging LLMs for KR:
LLMs simplify and accelerate ontology development by:
LLMs redefine semantic integration workflows by:
LLMs affect practical aspects of data integration by:
Impact: LLM-powered KR systems are more adaptive, scalable, and accessible, enabling organizations to operationalize linked data principles effectively.
The integration of LLMs into KR workflows marks a new era for the Semantic Web. By bridging symbolic reasoning, cognitive insights, and computational efficiency, LLMs empower systems to address real-world challenges with greater accuracy and scalability. Much like Halmos and Einstein’s insights into visualization in creative processes, KR systems increasingly leverage visual and conceptual models to align with human reasoning and foster innovation.
-- Regards, Kingsley Idehen Founder & CEO OpenLink Software Home Page: http://www.openlinksw.com Community Support: https://community.openlinksw.com Social Media: LinkedIn: http://www.linkedin.com/in/kidehen Twitter : https://twitter.com/kidehen
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Hi John,
Ravi, Igor, and Kingsley,
Since Matthew isn't with us, I'll summarize some of his points which we had discussed over many years in different ways.
He had been working at Shell Oil for years, and he had developed a detailed ontology for the oil industry. He later generalized it to develop a more general top level, which I considered quite good. We had discussed issues about generalizing it even farther, but he was reluctant to go farther in levels of abstraction. We agreed that was a reasonable point of view.
But we also agreed that his details for the oil industry might conflict with details for other industries, such as banking and farming. Furthermore, he recognized that different oil companies had different ways of representing the same terms because they had developed different policies and procedures.
I'll also mention another widely used ontology, which evolved over a period of about 70 years, and it is unlikely to change for a long, long time: the ontology for making reservations for airlines, which was later extended to cover anything related to airlines, such as hotels, car rentals, trains, taxis, etc., etc.
And that ontology began in the 1950s with IBM's project SAGE for the airplanes used in the Strategic Air Command over North America. In the 1960s, IBM adapted that ontology for American Airlines. IBM later sold the software to other airlines. And all the other additions were made to conform to the same basic ontology from the 1950s. 70 years later, that top level is so entrenched that it is never going away.
Another world-wide ontology that also developed in the 1960s includes the global weather patterns that were established by the world-wide weather simulation programs. They use a different way of representing the world than the reservation systems.
Fundamental problem: There will never be a single universal ontology for representing anything and everything in the world (or the universe).
That is a fact that any system of knowledge representation must deal with.
John
I completely agree with your perspective on ontologies and practical reality. Hopefully, the rise of LLMs will simplify these concepts by enabling quicker experimentation with ontologies, ultimately reinforcing this crucial point.
Kingsley
From: "Ravi Sharma" <drravi...@gmail.com>
In the software development and maturity considerations we used to talk of design, development and implementation patterns.Would you then build three different ontologies?I would think only one would be applicable for all three types of patterns.
On Wed, Nov 27, 2024 at 5:42 AM 'Kingsley Idehen' via ontolog-forum <ontolo...@googlegroups.com> wrote:Thanks.Ravi
Hi Igor,
On 11/27/24 8:38 AM, 'Igor Toujilov' via ontolog-forum wrote:
> John,
> I agree that visualisation is important in formal reasoning. But I am
> not sure that it works in all cases. I have seen theoretical
> physicists discussing their problems on YouTube for hours without a
> single diagram.
IMHO:
That doesn’t mean they didn’t use a diagram during the early conceptual
stages, or even have one off-camera. As visual beings, any
well-thought-out process typically begins with some form of conceptual
visualization—unless it’s just copying, memorizing, and regurgitating
someone else’s work. :)
--
Regards,
Kingsley Idehen
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