Henson, Understood and agree. I only added the point about conceptual and social realities because in many of the systems and customer sets I worked with there was a tendency to rely heavily on sensor technology to create a representation of operational reality (as in “Common Operational Picture), and then complain about the lack of adequate/accurate representation of what are really conceptual/social realities. Hans From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx [mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of henson
Sent: Thursday, February 20, 2014 7:12 PM
To: [ontolog-forum]
Subject: Re: [ontolog-forum] Making use of ontologies I agree that conceptual and social realities are important. I view them as part of physical reality, but be that as it may. My concern is that the physical realities that I mentioned do not appear to have received much discussion, and they are critically important for many applications of ontology to be successful. Sent: Thursday, February 20, 2014 5:49 PM Subject: Re: [ontolog-forum] Making use of ontologies Henson, I wouldn’t limit what needs to be described in ontologies to the physical world. Much of what we do concerns itself with conceptual and social realities, not just physical realities. For example, the existence and structure and relationships of organizations, governments, social institutions and cultures can’t really be detected as such in the physical world, although there might be some physical manifestations of these conceptual/social realities. In your closing down a bank example, for instance, there’s a whole lot more involved than shutting the bank building, and much of that involves non-physical relationships and non-physical “money” (unless you consider bits and bytes on a computer as the physical reality of the money that the bank manages). Of course, these kind of realities can cause physical actions, and may be impacted by physical actions, but you can’t rely simply on physics/chemistry to determine what those actions and impacts might be. You have to interact with people or representations of people created knowledge and constructs to get a complete picture of all the operational realities involved in whatever you are trying to accomplish, like trying to determine what food processing standards the plant in question might need to comply with. Hans In my experience, we and our machines use ontologies to describe and interact with the physical world. This means that for ontologies to be useful they have to have some accompanying physical semantics, not just a translation into some other concepts or words, or even an axioms to describe their meaning. I am of course strongly in favor of axiomatic semantics for ontologies. But even with axioms there need to be procedures for constructing or recognizing if something is in a specific category. One might call this an operational semantics. This is model theory in the sense of logic, but it does not come for free, even when there is an axiomatic semantics. Taking “if it walks like a duck, …” a bit further, a vehicle design description may use an ontology with categories such as physical object, with specializations for some particular kind of steel. The physical semantics is generally a procedure to recognize or test for that kind of steel. A big problem for replacement parts for commercial aircraft is the presence of counterfeit parts. Often these recognition procedures, as well as procedures for performing tests and analyzing test results, are quite complex, with their own ontologies to describe them. However, these ontology applications are only successful when the physical semantics can be agreed on in the community where the ontology is used. Practically the successful use of an ontology depends on having a well-defined explicit operational semantics. It is the operational semantic issues that cause the tears, and litigation. This does not necessarily mean that everyone agrees with the definition, but they understand how the concepts are being used in a given context. I presume that the customer who takes possession of an oil refinery checks to see if it has all of the equipment which the customer is expecting. I know this is the case with other large expensive industrial products. If some regulatory agency is faced with closing down a bank, or a food processing plant, the issue is likely to be how do we measure the conditions which trigger action. I am not sure that this aspect of ontology use has gotten much attention in the recent discussions Henson Graves Sent: Wednesday, February 19, 2014 7:23 PM Subject: Re: [ontolog-forum] Good ontologies without good tools are useless Hi Henson and all,
We are finding similar requirements in finance, as I'm sure Mike Bennett would echo. The ontologies that are needed to support compliance and understanding the nature, risk and opportunities related to complex securities, annuities, and other financial instruments are necessarily far richer and more rigorous in their definition than what I have seen over time from a linked data / big data perspective (although the systems that do or will use them are quite large and the data volume certainly qualifies as big data). The services that are needed to support both regulators and bankers require reasoning of various sorts, including but not limited to rule-based inference, classification, and machine learning. Systems to support fraud detection, anti-money laundering, insider trading detection, and many other processes have been using rule-based reasoning for decades.
Use of UML for software engineering has been fairly widespread in banking and insurance for many years, and retraining information architects in those industries to use it for ontology development (with ODM stereotypes) is not as big a leap as attempting to get them to use Protege or other ontology tools. Another advantage is that the UML models, which can be exported to OWL or some other rule language, are available immediately for reuse in the software applications folks are developing. This minimizes reinvention of the vocabulary used in development. Use of the same ontologies in rule-based systems, again rather than reinventing the vocabulary, also dramatically reduces error, especially if the methodology includes analyzing the ontologies to ensure they are logically consistent prior to reuse.
For those that are interested, we are in the process of publishing a new version of the ODM at the OMG, which addresses a number of deficiencies we've found in implementation over the last several years. ODM 1.1 supports most of RDF 1.1 and provides better support for OWL 2, although the work there is incomplete. We plan to have a 1.2 version of the specification by sometime this summer that will fix the remaining known problems and provide complete coverage of OWL 2. Both specifications and related artifacts will be available from the OMG site once they are approved by the membership. That process is largely complete for ODM 1.1, with a couple more process steps remaining, but the spec should be public soon. If I had to guess, I would say the 1.2 version will be available by this fall, if not sooner. We considered waiting to publish until we were "done", but so many changes had already been made that we really needed a new baseline to work from.
For OMG members, the Ontology Development Metamodel (ODM) 1.1 Revision Task Force (RTF) report, documents, and all of the model artifacts are already available on the ODM 1.1 RTF work in progress page. They should become public when the process hoops are complete, possibly by sometime next month.
Members of the ODM revision task force include some of the same folks who have been working on the SysML specification, and who really understand the value proposition, as Henson puts so well below. The task force also includes a couple of the major UML vendors -- No Magic and Sparx -- and there are beta tools for both No Magic's MagicDraw and Sparx EA available.
Best regards,
Elisa
On 2/18/2014 8:52 AM, henson wrote: I work with engineers who have a day job and recognize that they need ontologies to do their work. [I have a lot of experience with being in their shoes.] The ontologies are needed to model, i.e., describe and specify systems and their operations in the real world. The models are used to design and analyze vehicles, aircraft, etc. The recognition of the need for ontology is that the operating environment descriptions need to be much more complex and are much more changeable than they were 50 years ago. The use of ontology also increasingly applies to some of the systems being built. They use ontologies to process the enormous volume of data that they ingest at operation time, and use some inference to take actions. The software of some of these systems contains a model of the system as well as its environment which it uses at runtime for flight control and threat avoidance. To be of any use the ontologies have to be imported into the development tools which they use. The mostly UML based tools (including SysML) are now robust, ordinary engineers can and do use them to develop large complex systems. By and large these folks cannot or will not use traditional logic syntax. Also there are not commercial grade tools that have been proven in the industrial context. This doesn’t mean that these language don’t need a formal semantics, and need extensions to handle the applications that they are being used for. They do. I have been arguing for a long time that the formal methods folks should focus on retrofitting and evolving these tools, rather than attempting to develop new ones. As a practical note, I believe that the SysML community is more receptive to something such as William Frank advocates than the UML community. The reason being that sociologically the engineering community has to deal with a much broader scope of applications than the UML community. If the battery fire on a commercial aircraft causes a crash then the manufacturer will certainly be sued and will be asked to produce the analysis and test results that were used to declare the aircraft safe to fly. If these results are not compelling the manufacture is in big trouble. Engineers are beginning to get this. As a comment on Ron Wheeler’s comment the ontologies I see in the big data world if they deserve the name ontologies, are currently much simpler than the kind of ontologies mentioned here. However, if these systems are to be used for medical diagnosis, and drug design and analysis then they will have to have the complexity of the ones I am talking about. I do not know of anywhere within a university context material relevant to this discussion is being taught. There presently doesn’t even seem to be any traditional departments willing to pick this up, in my limited experience. By the way, I am passing along John’s slides to a group I am working with which is in desperate need of an upper ontology. Sent: Tuesday, February 18, 2014 9:15 AM Subject: Re: [ontolog-forum] Good ontologies without good tools are useless Yes,
UML based on a many-sorted higher order predicate calculus with Henkin semantics, and a simple upper ontology represented by the sorts - this is what we (Joauquin Miller, Kevin Tyson, and I) proposed for UML 2, in Clear, Clean, Concise (3C) UML Communications of the ACM, Nov 2002, volume 45, no. 11 pages 79 - 81.
"The Clear, Clean, Concise (3C) UML2 proposal makes the language
easier to understand and enables it to describe a broader
range of systems, from Web agents and services to entire business
communities."
This was never going to happen, at that point. The agenda was set by Oracle and IBM, with no regard for the benefits to the long term future of systems engineering. Also, I misunderstood people so much that instead of referencing the science, I simply explained the BENEFITS, and showed how simple it was to use this language, so I now suspect they thought this was some new off-the-wall approach invented by us three. I agree with all you say below, John, but add that an equally important feature integrated into a cosistent set of of UML models, along with the 4 you list, are state - transition models, the backbone, in my opinion, for precise behavior specifications. Myself, I find UML diagrams, with my OWN simple simple common logic semantics, the most effective way to ensure a system consistency, because of all these integrated models. On Tue, Feb 18, 2014 at 9:11 AM, John F Sowa <sowa@xxxxxxxxxxx> wrote: John M, Henson, et al.,
....
Just imagine how things might have developed if Guha -- who had been
the associate director of Cyc and later the chief designer of RDF --
had adopted UML in the mid 1990s.
Guha said that the reason why he designed RDF is that CycL was too
difficult for most users. I agree with him. But software developers
in the 1990s were happily using UML diagrams. The UML notations,
tools, and methodologies can support
1. Type hierarchies (the backbone of every ontology),
2. ER diagrams (logical signatures and cardinality constraints),
3. Activity diagrams (links between the logic and the procedures),
4. Controlled natural languages (more readable than OCL for stating
rules and constraints that go beyond #1, #2, and #3).
I admit that I'm making these criticisms with 20-20 hindsight.
In fact, I blame myself even more than I blame Guha or anybody else
-- because in the mid 1990s I was participating in ISO working
groups on standards for a conceptual schema (i.e., ontology).
At that time, I was proposing logic as the foundation. If I had
proposed UML and points #1, #2, #3, #4 defined in FOL, an ISO standard
for ontology (AKA conceptual schema) might be mainstream IT today.
John
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