Re: Semantic Layers (Was Interpretation of RDF reification)

John and Adam,

Please see my commentary on your comments and concerns.

Regards,
Azamat Abdoullaev, EIS Encyclopedic Intelligent Systems Ltd
http://www.eis.com.cy

John Sowa wrote:
<RDF and OWL are too limited, clumsy, and inefficient to support any serious work in knowledge representation and reasoning.>
<My complaint about RDF and OWL is that they are terrible languages for all three categories of humans -- #1, #2, and #3 -- and they are also horribly inefficient for computers.  They do not have a target audience.>

Adam Saltiel responded:

<I understand that things can go in circles in the AI world, as just implicitly mentioned in John's post>
<But if the tools are for the "wrong" language this simply isn't good enough, is it? This in turn will have implications for funding efforts and hopes of success for different projects, so I think the issues should be considered very seriously.>


AA:
Indeed, the matter looks serious, both from the public and scientific sides, beside the technical issues which Andrian tries to point out for a long while. The first issue is concerned with getting huge public funds, promising a sort of magic technology as the Knowledge Society intellectual technologies, without making foundational ontological groundwork, like as SUO or ONTAC or USECS. The price of ignoring the unifying ontology framework for building advanced information systems may amount to many and many millions considering that the European Union initiated multi-billion R&D projects in Information Society Technologies, seemingly somehow to catch up with the like IT programs in the USA. In order to lay down the knowledge infrastructures of the upcoming Information Society the EU’s Research Council and the European Parliament allocated 3.8 billion Euro for Knowledge Technologies within the 6th European Union Framework Programme (FP6) for Research and Technological Development, with a total budget of 17.5 billion Euro. Within the FP6 Programme, all the web-based knowledge technology projects are largely concerned with ontology research, design, learning, and management. To meet the high social and political expectations, the so-called 'networks of excellences' have been forming. So, the Knowledge Web ‘network of excellence’ is engaged to transfer ontology technology from universities to industry; the Data Information and Process Integration group is contracted to contribute to the infrastructure of semantic web services; while the Semantic Knowledge Technologies network is signed to produce ontological software and tools for semantic web services. From the scientific side, what is mostly worrisome is a misinterpretation of the whole matter of SW ontology, apt to result in the public distrust in information and computing science, as it occurrs with another noisy scientific enterprise, the cloning research projects. It is not a deep arcanum that trying to build a [web resource and data] unifying representation language just basing on formal logic tools without ontological foundation and semiotic fundament is misguided. Nevertheless, most EU's semantic web projects are performed under the costly collective delusion that ontology-based semantic web and services technologies can be constructed without having a unified framework ontology providing an integrated representation and reasoning platform. One should not be a visionary to call the outcome: the cost of such an academic head game may be the budget allocated, thus repeating the common error of confusing the public funds spending with the advanced information technology delivery. 

Also, i have to agree with John Sowa what is going on in the SW research projects makes many of us have a sort of AI deja vu. Since the malady of misconceiving the value of Form (logic) and Content (ontology) afflicted the Knowledge Representation area surfaced again, regardless of its being the main cause of the classical AI paradigm fatality. As a fresh lesson of this harmful condition, it is usefull to remind the Vulcan project Halo failing to meet the loudly declared hopes and promises of creating the Digital Aristotle and now silently passing away. 

All this takes place despite of the fact that the lessons ('the best practices') acquired from the  AI R&D are mostly evident and instructive. Even such an infuential AI policy maker as  Randall Davis, a former president of AAAI,  emphasized that ''a KR is a set of ontological commitments'' on <how to view and reason about the world>. The logically-minded researchers were forewarned of the touble that the formal logic languages can bring in:
1. ''Ontologies can of course be written down in a wide variety of languages and notations (e.g., logic, LISP, etc.); the essential information is not the form of that language but the content, i.e., the set of concepts offered as a way of thinking about the world. Simply put, the important part is notions like connections and components, not whether we choose to write them as predicates or LISP constructs.'';
2. ''... all the representation technologies...supply only a first order guess about how to see the world: they offer a way of seeing but don't indicate 
how to instantiate that view. As frames suggest prototypes and taxonomies but do not tell us which things to select as prototypes, rules suggest 
thinking in terms of plausible inferences, but don't tell us which plausible inferences to attend to. Similarly logic tells us to view the world in terms 
of individuals and relations, but does not specify which individuals and relations to use''.[see What is a Knowledge Representation? AI Magazine, 1993]:

Bottom line:
We need to comprehend the meaning and relationships of real ontology and formal logic, their similarities and differences. For both the universal sciences embrace all things, but only from diverse perspectives. Ontology considers the being of everything which exists in the world, material or mental or social, all basic aspects, properties, relationship patterns and uniformities of reality, cutting the body of all things along its joints. By contrary, (formal) logic deals with the formal parts and elements of human knowledge and reasoning, cutting the forms of the universe of discourse from its matter and content. As a consequence, the ontology/logic distinction makes all the difference in building a new class of Knowledge Society intellectual technologies, like the semantic web. 

Though relating to anything, Logic (as a formal science) is only about the formal conceptual elements and patterns (terms, predicates, propositions and inferential rules) of the discourse about anything, all taken without any reference to reality.  Whereas Ontology is about  assigning a real significance (meaning) both to the formal logical constructions, linguistic expressions and communicative acts, within a single framework of fundamental entity classes and relationships applicable to any knowledge domains and sciences. Not to see this cardinal 'division of knowledge power' of real ontology and formal logic with their inherent interplay may be harmful for the whole cause of advanced knowledge systems and reasoning applications.


  ----- Original Message ----- 
  From: adasal 
  To: semantic-web@w3.org 
  Sent: Sunday, March 26, 2006 11:55 PM
  Subject: Re: Interpretation of RDF reification


  I find this very interesting, but also a bit worrying.
  i. I find it incredibly interesting because, many years ago (twenty, perhaps) I shared my flat with someone who was studying Grice. I was interested enough to make sure I had a copy of every paper he had written, some are a bit obscure. My friend pointed out at the time that he was an off the beaten track figure, and so it seems he remained, at least until recently. I don't mean to imply this is where I think his work should be, far from it. I know how ambiguous email communication can be, but I have always been intrigued by his work. I thought there wasn't a formalism capable of capturing his ideas sufficiently for machine exchanges.
  ii. I have read, and I think treasured, Knowledge Representation. And, indeed, there is a bibliographical reference to Grice in it.
  iii. But not finding Grice mentioned in the SemWeb efforts I had assumed it was either irrelevant or subsumed in this effort. If not impossible to incorporate then it was more of the former.
  iv. I understand that things can go in circles in the AI world, as just implicitly mentioned in John's post, but people in the Uni Department I used to work in (Greenwich University) said much the same. I suppose that basically a good idea may not have been fully fleshed out and different implementations have implications for its viability.
  v. This is the worrying bit. John has said that

    RDF and OWL are too limited, clumsy, and inefficient to support
    any serious work in knowledge representation and reasoning.

  I don't know if this is true. I don't know what constitutes "serious work", although John hints that large models make Protege choke, and I assume it is the reasoning chains in the model rather than the number of elements per se, that are the choke points.
  I don't know what constitutes "serious work" because use cases are so thin on the ground.
  Now, to expand on my concerns, where there is a well articulated use case (in the sense of how to use it, not whether it will be used, more on that in a bit) that of work flow modelling, there is an example in a well funded EU project working in this area, WSMO, but the project uses another specialised language that OWL can be translated into, not OWL.
  This may be important in that I had thought that the reason for RDF and OWL was not so much to achieve what couldn't be achieved by other means, so much as to achieve this in an open language where what would be key is the extent of that adoption of this language.
  Just before I go further, perhaps there are some flawed assumptions here.
  Once again, I had thought that the analogy might be with the rise of Java. I thought it was seen that although Smalltalk is like Java, the desirable outcome would be to follow a similar trajectory to Java. One might say that Smalltalk is "better" than Java, but failed to gain acceptance due to its marketing. Although obviously not Open Source, Java was made completely (or sufficiently) available, to gain wide adoption. So, to gain acceptance, open source transparency and availability is a good thing. But this does presuppose that the language in question will cut the mustard otherwise. (I know there are other precedents, in particular and notably the Apache Web Server, but you get the idea.)
  So, perhaps the flawed assumption is that there should be just one language or language set that does for the SemWeb?
  vi. However, judging from the level of activity on the mailing lists for WSMO related issues, this, at least has a long way to go before any sort of wide acceptance. I am not sure that this is because of the limitations of OWL (or its own variant). Nor, even, the obvious fact that a further language fragments the potential user group. This may not apply here anyway. What I think is happening is that the technology has not shown itself to be sufficiently compelling as yet.
  v. So how does a technology prove itself in this way? There is a tension between what can be demonstrated and what potential users are prepared to contemplate by way of adoption. This is complicated by a number of things. What are we trying to do here? It can't be to just promote a single language type solution but rather the ability to do various sorts of reasoning across disparate data pools, to determine the preferred design for those pools, to cope with non-conformant pools and to offer an open means of achieving these ends (this list isn't intended to be comprehensive).
  But the thought remains that there should be a single language for this since this reduces duplication of effort.
  Again, a compelling application might be persuasive, but then that application would have to be used to be compelling, that is have a real user base. And there we are looking at another area of complication.
  For instance the database that Dan wishes for has been implemented in several forms for RDF. These are already compelling applications, although not enough to make a semantic application, as the schema, data and queries are also required.
  So, in sum, have I narrowed it down? Is the issue that were a more expressive language used, there would be more in the user community at work on schema, data and queries utilising that language?
  Is there a particular application that would show the difference between the two languages and prove a compelling case for would be adopters?
  Is there sufficient regard paid to a distinction between different types of possible SemWeb applications? I have mentioned one and several others are mentioned on this list as well as in this thread. In particular there seems to be a sharp distinction between, say, a desktop application that relies on markup to decorate underlying content and P2P to discover info nuggets and building a large and comprehensive ontology in the field of medical discovery. Or again, as i mentioned, an ontology of process that comprehensively handles workflow.
  Why should it be the same language in all cases?
  Is it just to do with a paucity of alternative tools and the desire not to duplicate effort?
  But if the tools are for the "wrong" language this simply isn't good enough, is it?
  This in turn will have implications for funding efforts and hopes of success for different projects, so I think the issues should be considered very seriously.
  Sincerely,
  Adam Saltiel