Reasoning with Taskless BPMN

Was it Lisbon that attracted me so much or the word Cybernetics in the sub-title or the promise of Alberto Manuel that it would be a different BPM conference? May be all three and more. As it happened, the conference was very well organised and indeed different. The charm of Lisbon was amplified by the nice weather, much appreciated after the long winter. As to Cybernetics, it remained mainly in the sub-title but that’s good enough if it would make more people go beyond the Wikipedia articles and other easily digestible summaries.

My presentation was about using task-free BPMN which I believe, and the results so far confirm, can have serious benefits for modelling of both pre-defined processes and those with some level of uncertainty. In addition, there is an elegant data-centric way to execute such processes using reasoners. Enterprise Architecture (EA) descriptions can be improved if done with explicit semantics. Currently, EA descriptions are isolated from the operational data and neither the former is linked with what happens, nor the latter get timely updates from the strategy. More on this in another post. Here’s the slidedeck1You can watch on YouTube the slides with animations (no voice-over) and a 7 min compilation of the talk.:

On Semantic Technologies

A conversation with Eddy Vanderlinden

Semantic technologies have been some temptation for me for quite a long time. That was mainly due to my growing frustration about the utilisation of data resources both inside corporations and outside, on the Internet. Then all mainstream modelling methods used for analysis or for database design and application development, with all their charms and weaknesses, often leave me with the feeling that they put too much constraints for people to express things and too less for computers to understand them. That and the suspicion about the potential of Semantic Technologies is not new to me. What is new, is the experience of their pragmatic application and the opportunity to see some all too familiar areas through ST lenses. Both of these I owe to Eddy Vanderlinden. In this sort of interview, I asked him a few questions the answers to which might be of interest to the readership of this blog.

Ivo: When was you first meeting with ST?

Eddy: In 2007, when searching for possibilities to avoid the registered dysfunctions.

Ivo: What was the most fascinating thing for you in beginning and how did this change? I mean, which part or capability of the ST is the main driver for you now, after so many years of practice?

Eddy: The most fascinating aspect was the data modelling aspect, so that data became real information. The upgraded function for data towards information covered the common and precise understanding for all stakeholders of concepts through their relation with other concepts. Also the flexibility of the data model was contributing to the great benefit.

There are two main drivers added later on:

  1. The knowledge discovery possibilities through the open-world assumption. Under the condition we adapt our state of mind from comfortably categorising “things” into a state whereby we discover through the ST technique new aspects. In philosophical terms, we should become a bit Tao: flexible, accept change as the only certainty and being attentive to capture change and utilise it for our benefit.

  2. The possibility to convert knowledge models straight into running applications so that unambiguous goals are obtained through commonly understood and mastered methods.

Ivo: There are people over-enthusiastic about Semantic Web, calling it “Web 3.0”, “the next big wave”, “the gigantic …. graph” and so forth. At the same time there are many sceptics and even people that had been once enthusiastic about it, and now seem rather disappointed. What do you think about this? Is ST rather overrated or undervalued or somehow both? And why?

Eddy: ST is to my understanding by far undervalued. Lets see first why one could be disappointed. One reason might be because lots of people betted on the possibility that web publishers would start to use html tagging techniques massively so that slightly adapted search engines could simply exploit the semantics in the text. So, the search engines would not only have keywords (from the html header) and page titles but also tags from within the text as source for semantic searches. The most popular standard which was proposed to execute this is RDFa. Personally I never believed this would be a success. Not only because the user needs to put additional efforts but also because tagging does not provide relationship information between the tags. So, these people may be disappointed but can consider text analysis, spongers for email and spreadsheets as alternative approaches (see IBM‘s DeepQA).

Another group of disappointed people could be the ones who thought that putting all information on a subject into an ontology would solve all their problems. This is by far not the case. Modelling should be done with a purpose in mind. As Dean Allemang writes, it is a craft. Don’t underestimate the power of the models but accept they provide answers to the questions you want answered. If more than that is needed, ST has to be combined with probabilistic methods (see earlier on IBM’s DeepQA project).

Another group of disappointed could be people who want massive amounts of data treated with today’s reasoners. Although reasoners became much more performing, industries with huge amounts of data (like the finance industry) should approach that data in specific ways, sometimes emulating the reasoners with alternative technologies (e.g. SPIN and rules engines of triple store suppliers). Honestly, if we don’t know what the reasoner should deliver, we cannot model effectively. It would probably mean we are not rightly involving ST.

The reason for my conviction of undervaluation is that for the above objections there are far-reaching alternatives which are opening new horizons in all fields of applications. Furthermore, there are new domains starting to adopt semantic technologies. Artificial intelligence is such a domain. To my knowledge, the most impressive result of this is IBM’s DeepQA project. I know the reluctance of ST people being associated with AI because they feel AI did not bring them much, on the contrary, they brought a lot to AI. In my opinion, implementing the probabilistic approaches, besides other AI techniques, with ST brings a lot of added value to ST. Lets not forget ST is strongly domain oriented, while probabilistic approaches may help generalise solutions from combined domains. I expect a lot of input also from the KM community . While this community used ontologies from the beginning (in different ways than web ontologies), the love between the ST an KM communities is not to be called ideal. When the KM community will embrace the possibilities offered by ontologies in Descriptive Logic (OWL-DL compliant), the benefits will contribute both communities. See http://semanticadvantage.wordpress.com/category/semantic-technology/

Ivo: It seems that you don’t much associate yourself with the Semantic Web community. Is that so? What do you think are the main mistakes, of fallacies of the Semantic Web movement that would actually jeopardise or postpone Semantic Technologies getting more tangible traction?

Eddy: First I would like to stress that the solution proposed for the operational dysfunctions owes everything to the SEMWEB community. I cannot thank them enough. If I am less associated today the reason might be laziness. I have to update my vision of their activities again. You see, when these technologies became of strategic importance for USA and for Europe, lots of financial means were directed to the development of standards. In the beginning, the SEMWEB community was mainly busy developing standards complying with Tim Berners-Lee‘s architecture vision.

Later came the tools with pioneers as: Clarks & Parsia (Pellet reasoner), Stanford University (Protégé), Berlin University (software language adaptations for ST), Zurich university (Controlled English communication), HP with Jena Laboratories (triple stores and SPARQL). Very rapidly a vast community of tool producers followed. This was really exciting. I could only participate as tester, commenter in small fields since the main discussion point then was on tool development.

Later commercial players joined the community: Topquadrant with a software development platform including creation of services and web server, OpenLink Software for heavy duty triple and quad stores and a few more. The problem is the applications are mainly being adopted by the academic and scientific world, not yet by business users. I’ll check this for updates again.

Ivo: A recurring pattern in the lessons learnt you share on your site is related to losses when conceptual data models are transformed into physical. And another is related to the missing time dimension. Can you please tell us more about those two and how they are solved by applying ST?

Eddy: On the transformation conceptual to physical, Business analysts have different tools at their disposal to represent the real world operations in a conceptual manner. They are mainly grouped in what we know as the UML diagrams, a collections of diagramming methods. Starting from these conceptualisations, technical analysts, programmers and a collection of other specialised people (data engineers, interface designers, service engineers, …) start developing a functionality involving those concepts. I will not repeat the classic picture of the swing illustrating the difference for the user, anyone can make his own version here. This is solved in ST by software using the ontology as their data source. A perfect example is the Topbraid suite. Another popular tool is provided by Openlinks Software, not to forget the Fresnel lenses.

On the time dimension. There are only few universal laws. Knowledge about facts is merely related to and valid for a portion of time. Meaning, an assertion is true at a certain moment or time-period. E.g. an organisation, the price of an item, the specification of a product. Whoever has been involved in data mining will recognise 2 main challenges: denormalization of related data and time-dimensional information. The purpose is to reconstruct any state of the company at any moment in time. The reason is, we cannot find out cause and effect information if we can’t partition facts into the time dimension. This is done in ST, mainly in a similar way as with conventional data-management systems: adding a time-dimension property to any individual, member of a class.

Ivo: What are currently the best examples of using ST?

Eddy: Cancer research, DBPedia, NASA has database of metrics methods, linked Data analysis, US government enterprise models, search engines.

Ivo: Is there something that you see as a big potential of ST which has not come to fruition, which for some reason nobody was able to realise so far?

Eddy: To me, it is strange the possibilities for application development are not really applied.

Ivo: You have been and are currently involved in BPM activities. What do you think ST can bring to BPM? Do you see it as a flavour of BPM (there is something called Semantic BPM already) or as an alternative approach to what most of the companies use BPM for?

Eddy: I am not in favour of “semantic BPM”. The reason is linked to my answer in the beginning: it requires tagging the models and their objects. I certainly see it as an alternative approach to what most companies use BPM for.

Ivo: You worked in banking for quite a long time. Let’s talk about Semantic Technologies as an investment. It seems that many types of innovation if judged using DCF or other popular methods, look riskier than do-nothing strategies. What type of evaluation would convincingly justify investment in Semantic Technologies today?

Eddy: The answer focuses on semantic technologies as an investment, not as a banker investing in companies with an expected DCF of X EUR/USD. The latter requires a much broader approach than just DCF, RONIC, or similar. Notably what we may understand by “expected” cash flows, introducing Beta factors, sector and currency valuation of future trends, market valuation in economic context,… So working in the banking sector will not help providing an answer here. Prior to the investment selection step, the proposal is to position ST in a strategic perspective analysis. That analysis would answer the questions:

A. On the portfolio management issues: A1. What new products can be offered in our portfolio with ST? A2. How will ST affect existing products in our portfolio? A3. Which new markets can be explored with the products discovered in A1 and A2? A4. How are these new markets evolving in the strategic time perspective? A5. How are the products discovered in A1 and A2 influence our competitive position in the new markets discovered in A3? A6. How is our competitive position evolving compared to A4? Suggested method: portfolio management matrix of the Boston Consulting Group.

B. On the strength of the organisation: B1. How does the application of ST reduce the risk of new competitors coming in the market? Where can the organisation start competing in new markets? B2. How does ST application affect our negotiating power with supplier? B3. How does ST application affect our negotiating power with customers? B4. How is the threat of substitute products in our markets mitigated through ST application? How can we form a defence against substitutes in existing markets? B5. How is our strength affected toward competitors considering the price of our products and services, the quality of our products and the service level? (Inspired by Porter’s 5-forces model.) After a thorough SWOT analysis, when the product/market matrix is finalised, the production simulations ran and the investments are figured out, cash flows can start to be projected on a strategic horizon (3-5 years). Eventually other analysing techniques may be needed. Choosing for semantic technologies gets a completely different perspective when compared to “do nothing” scenario if we make the exercise as mentioned above compared to a pure accounting approach.

Ivo: Now about reasoners. Why there are so many of them? How they differ from each other? How to choose? When to use which?

Eddy: I wished I had a clear-cut answer to that question. When building the finance ontology in 2007-2009, I tested 5 reasoners. On the correctness of the inferences: for an expected inference, I could get 4 different answers. The conclusion at that time was, the best reasoners depended on the inference needed. The reason might have been for a part the shift from OWL 1 to OWL 2 but it makes me test the inferences at each construction of a model with different reasoners. On the speed of the inference: in the beginning some very well performing reasoners went commercial only. Meanwhile some free reasoners upgraded their version and new players entered into the field. Further I would like to remind the relative importance of reasoners in heavy life applications: see higher under SPIN and rule engines.

Ivo: If I start asking about ST languages, the answer might be much bigger that all answers combined so far. May be we can post a separate conversation about that. Still, this one could not do completely without it. About the OWL then… OWL is (arguably) the leader in knowledge representation. Some think that the main advantage is that it is decidable, not probabilistic. Others state that the best thing about OWL is that it’s based on a solid mathematical foundation unlike all OMG standards, which are criticised for lacking it. What do you think are the main strengths of OWL, and the main weaknesses?

Eddy: to me it is the mathematical foundation when this enables advanced inferencing. On the other side, I very much appreciate the profiles with the different syntactic subsets. There is no clear-cut solution and OWL has to be tailored. This strength is also its weakness, together with the fact we cannot reason with classes (for the moment?).

Ivo: Thank you, Eddy! May be there will be some more questions put here from others. Now, instead of a conclusion, and for those wondering why Web Ontology Language is abbreviated as OWL and not WOL, one familiar passage:

He [Owl] could spell his own name WOL, and he could spell Tuesday so that you knew it wasn’t Wednesday, but his spelling goes all to pieces over delicate words like measles and buttered toast.

A. A. Milne, Winnie the Pooh