More stuff: relating stuffs and amounts of stuff to their parts and portions

With all the protests going on in South Africa, writing this post is going to be a moment of detachment of it (well, I’m trying), for it concerns foundational aspects of ontologies with respect to “stuff”. Stuff is the philosophers’ funny term for those kind of things that cannot be counted, or only counted in quantities, and are in natural language generally referred to by mass nouns. For instance, water, gold, mayonnaise, oil, and wine as kinds of things, yet one can talk of individual objects of them only in quantities, like a glass of wine, a spoonful of mayonnaise, and a litre of oil. It is one thing to be able to say which types of stuff there are [1], it is another matter how they relate to each other. The latter is described in the paper recently accepted at the 20th International Conference on Knowledge Engineering and Knowledge management (EKAW’16), entitled “Relating some stuff to other stuff” [2].

Is something like that even relevant, when students are protesting for free education, among other demands? Yes. At the end of the day, it is part and parcel of a healthy environment to live in. For instance, one should be able to realise traceability in food and medicine supply chains, to foster practices, and check compliance, of good production processes and supply chains, so that you will not buy food that makes you ill or take medicines that are fake [3,4]. Such production processes and product logistics deal with ‘stuffs’ and their portions and parts that get separated and put together to make the final product. Current implementations have only underspecified ‘links’ (if at all) that doesn’t let one infer automatically what (or who) the culprit is. Existing theoretical accounts from philosophy and in domain ontologies are incomplete, so they wouldn’t help you further either. The research described in the paper solves this issue.

Seven relations for portions and stuff-parts were identified, which have a temporal dimension where needed. For instance, the upper-half of the wine in your wine glass is a portion of the whole amount of wine in the glass, yet that amount of wine was a portion of the amount of wine in the bottle when you opened it, and yet it has as part some amount of alcohol. (Some reader may not find this example nice, for it being with alcohol, but Western Cape, where Cape Town is situated, is the wine region of the country.) The relations are structured in a little hierarchy, as informally depicted in the figure below.

Section of the basic taxonomy of part-whole relations of [5] (less and irrelevant sections in grey or suppressed), extended with the stuff relations and their position in the hierarchy.

Section of the basic taxonomy of part-whole relations of [5] (less and irrelevant sections in grey or suppressed), extended with the stuff relations and their position in the hierarchy.

Their formal definitions are included in the paper.

Another aspect of the solution is that it distinguishes between 1) the extensional and intensional level—like, between ‘an amount of wine’ and ‘wine’—because different constraints apply (following from that latter can be instantiated the former cannot), and 2) the amount of stuff and the (repeatable) quantity, as one can have 1kg of many things.

Just theory isn’t good enough, though, for one would want to use it in some way to indeed get those benefits of traceability in the supply chains. After considering the implementation options (see paper for details), I settled for an extension to the Stuff Ontology core ontology that now also imports a special purpose module OMmini of the Ontology of Units of Measure (see also the Stuff Ontology page). The latter sounds easier than that it worked in praxis, but that’s a topic of a different post. The module is there, and the links between the OMmin.owl and stuff.owl have been declared.

Although the implementation is atemporal in the end, it is still possible to do some automated reasoning for traceability. This is mainly thought availing of property chains to approximate the relevant temporal aspects. For instance, with scatteredPortionOf \circ portionOf \sqsubseteq scatteredPortionOf then one can infer that a scattered portion in my glass of wine that was a portion of bottle #1234 of organic Pinotage wine of an amount of wine, contained in cask #3, with wine from wine farm X of Stellar Winery from the 2015 harvest is a scattered portion of that amount of matter (that cask). Or take the (high-level) pharmaceutical supply chain from [4]: a portion (that is on a ‘pallet’) of the quantity of medicine produced by the manufacturer goes to the warehouse, of which a portion (in a ‘case’) goes to the distribution centre. From there, a portion ends up on the dispensing shelf, and someone buys it. Then tracing any customer’s portion of medicine—i.e., regardless the actual instance—can be inferred with the following chain: scatteredPortionOf \circ scatteredPortionOf \circ scatteredPortionOf \sqsubseteq scatteredPortionOf

Sure, the research presented hasn’t solved everything yet, but at least software developers now have a (better) way to automate traceability in supply chains. It also allows one to be more fine-grained in the analysis where a culprit may be, so that there are fewer cases of needless scares. For instance, we know that when there’s an outbreak of Salmonella, then we only have to trace where the batch of egg yolk went (typically in the tiramisu served in homes for the elderly), where it came from (which farm), and got mixed with in the production process, while the amounts of egg white on your lemon merengue still would be safe to eat even when it came from the same batch that had at least one infected egg.

I’ll be presenting the paper at EKAW’16 in November in Bologna, Italy, and hope to see you there! It’s not a good time of the year w.r.t. weather, but that’s counterbalanced by the beauty of the buildings and art works, and the actual venue room is in one of the historical buildings of the oldest university of Europe.

 

References

[1] Keet, C.M. A core ontology of macroscopic stuff. 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW’14). K. Janowicz et al. (Eds.). 24-28 Nov, 2014, Linkoping, Sweden. Springer LNAI vol. 8876, 209-224.

[2] Keet, C.M. Relating some stuff to other stuff. 20th International Conference on Knowledge Engineering and Knowledge Management EKAW’16). Springer LNAI, 19-23 November 2016, Bologna, Italy. (accepted)

[3] Donnelly, K.A.M. A short communication – meta data and semantics the industry interface: what does the food industry think are necessary elements for exchange? In: Proc. of Metadata and Semantics Research (MTSR’10). Springer CCIS vol. 108, 131-136.

[4] Solanki, M., Brewster, C. OntoPedigree: Modelling pedigrees for traceability in supply chains. Semantic Web Journal, 2016, 7(5), 483-491.

[5] Keet, C.M., Artale, A. Representing and Reasoning over a Taxonomy of Part-Whole Relations. Applied Ontology, 2008, 3(1-2):91-110.

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Considering some stuff—scientifically

Yay, now I can say “I look into stuff” and actually be precise about what I have been working on (and get it published, too!), rather than just oversimplifying into vagaries about some of my research topics. The final title of the paper I settled on is not as funny as proposing a ‘pointless theory’ [1], though: it’s a Core Ontology of Macroscopic Stuff [2], which has been accepted at the 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW’14).

The ‘stuff’, in philosophical terms, are those things that are in natural language indicated typically with mass nouns, being those things you can’t count other than in quantities, like gold, water, whipping cream, agar, milk, and so on. The motivation to look into that was both for practical and theoretical reasons. For instance, you are working in the food industry and thus have to be concerned with traceability of ingredients, so you will have to know which (bulk) ingredients originate from where. Then, if something goes wrong—say, an E. coli infection in a product for consumption—then it would be doable to find the source of the microbial contamination. Most people might not realize what happens in the production process; e.g., some quantity of milk comes from a dairy farm, and in the food processing plant, some components of a portion of the milk is separated into parts (whey separated from the cheese-in-the-making, fat for butter and the remainder buttermilk). To talk about parts and portions of such stuffs requires one to know about those stuffs, and how to model it, so there can be some computerized tracking system for swift responses.

On the theoretical side, philosophers were talking about hypothetical cases of sending molecules of mixtures to Venus and the Moon, which isn’t practically usable, in particular because it was glossing over some important details, like that milk is an emulsion and thus has a ‘minimum portion’ for it to remain an emulsion involving many molecules. Foundational ontologies, which I like for their modeling guidance, didn’t come to the rescue either; e.g., DOLCE has Amount of Matter for stuffs but stops there, BFO has none of it. Domain ontologies for food, but also in other areas, such as ecology and biomedicine, each have their own way of modelling stuff, be this by source, usage, or whatever, making things incompatible because several criteria are used. So, there was quite a gap. The core ontology of macroscopic stuff aims to bridge this gap.

This stuff ontology contains categories of stuff and is formalised in OWL. There are distinctions between pure stuff and mixtures, and differences among the mixtures, e.g., true solutions vs colloids among homogeneous mixtures, and solid heterogeneous mixtures vs. suspension among heterogeneous mixtures, and each one with a set of defining criteria. So, Milk is an Emulsion by its very essence, regardless if you want to assign it a role that it is a beverage (Envo ontology) or an animal-associated habitat (MEO ontology), Blood is a Sol (type of colloid), and (table) Sugar a StructuredPureStuff. A basic alignment of the relations involved is possible with the stuff ontology as well regarding granules, grains, and sub-stuffs (used in cyc and biotop, among others).

The ontology both refines the DOLCE and BFO foundational ontologies and it resolves the main type of interoperability issues with stuffs in domain ontologies, thereby also contributing to better ontology quality. To make the ontology usable, modelling guidelines are provided, with examples of inferences, a decision diagram, outline of a template, and illustrations solving the principal interoperability issues among domain ontologies (scroll down to the last part of the paper). The decision diagram, which also gives an informal idea of what’s in the stuff ontology, is depicted below.

Decision diagram to select the principal kind of stuff (Source: [2])

Decision diagram to select the principal kind of stuff (Source: [2])

You can access the stuff ontology on its own, as well as versions linked to DOLCE and BFO. I’ll be presenting it in Sweden at EKAW late November.

p.s.: come to think of it, maybe I should have called it smugly “a real ontology of substance”… (substance being another term used for stuff/matter)

References

[1] Borgo S., Guarino N., and Masolo C.. A Pointless Theory of Space Based On Strong Connection and Congruence, in L. Carlucci Aiello, J. Doyle (eds.), in Proceedings of the Fifth International Conference on Principles of Knowledge Representation and Reasoning (KR’96), Morgan Kaufmann, Cambridge Massachusetts (USA), 5-8 November 1996, pp. 220-229.

[2] Keet, C.M. A Core Ontology of Macroscopic Stuff. 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW’14). 24-28 Nov, 2014, Linkoping, Sweden. Springer LNAI. (accepted)