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‘There’s a million ways of being short of breath’ Dave Randall, Wes Sharrock, Yuwei Lin, Rob Procter, John Rooksby. computer applications with structured classifications, a set of possible relationships and an inference engine
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‘There’s a million ways of being short of breath’Dave Randall, Wes Sharrock, Yuwei Lin, Rob Procter, John Rooksby computer applications with structured classifications, a set of possible relationships and an inference engine a possible solution to a number of problems around the real-world business of classification. an ontology provides: A single uniform and controlled classification scheme within a domain. A way of dealing with complex ordering procedures. A way of overcoming terminological multiplicity/confusion
Protégé/OWL Protégé developed at Stanford OWL at Manchester 2 different assumptions: Closed World assumption (CWA): “every proposition is either false or true”, Open World assumption (OWA), “in which this is not necessarily the case. Everything a database tells you is true and everything that it does not is unknown.”
NCess A scoping ethnography to identify possible ways in which ontology-based design processes can be moved forward into the ‘next generation’ 2 examples used here: ‘Doctors and Nurses’ The Ontogenesis network
Doctors and Nurses a collaboration between a large technology company with a longstanding interest in healthcare matters specifically concerned with ‘ordering’
The Ontogenesis Network a collaboration between various domain experts, ontologists and computer scientists not concerned with a specific ontology but with the business of developing, integrating and using a set of biological ontologies
Putative Advantages: Re-use ” ‘semantic heterogeneity’ characterised its origins. Ontologies are the latest attempt to rectify that. They need to be long term, and hence deal with conceptual change. That means that it’s not just that the ontology has to change … the science has to change …” “We design the ontology so the computer can reason with it. It is entirely plausible that you might get unanticipated results- you can discover relationships that you otherwise wouldn’t discover … with the gene and plant ontologies, people saw very quickly what the uses might be.”
The Local and the Global “Doctors own their own practices … they’re not willing to be told … licensing is between the State and physicians, so hospitals are in no position to dictate … hospitals are on the hook financially and have no clout. You want to support existing behaviour but at the same time change behaviour …” “the problem we’re trying to address is that clinicians, nurses, respiratory support … we want them all to use structured info in such a way that it allows decisions to be made … but its got to allow autonomy, flexibility and ease of use as well … The interface is the point at which we want decisions to be made …”
Knowledge transfer “We want to import knowledge from medical publishers and the like to influence clinical decisions … making and maintaining a knowledge base … we intend to work with textbook publishers to provide that knowledge base. But uptake in general practice has been slow … you take something like type 2 diabetes, the idea is that these folks could analyse the literature … it could be the input for structured info that allows them to make decisions AND makes their work easier not harder. That means that customers should be able to customise locally even with standardized terminology …” “We’d like an interface which is predictive, based on heuristic rules … on the other hand, what are the underlying structures, the ones the institution would like you to use … it’s a kind of ‘best practice’ problem …”
The real-world “So you see, even if you have best practices … specified for a condition … patients often have many conditions … I rarely see a patient with just one … and its very difficult to specify time and severity … what do I do at the point where a patient is beginning to spill protein into their urine? … and the existing software can only deal with one condition at a time …” “ In essence it generates on the fly a template for this patient … if this patient is diabetic and is stable, then … but if the patient comes in with a headache … or is female, and maybe pregnant … we should create documentation that would fit the patient … we realised that how to generate hospital ‘orders’ was non-trivial because they seldom come presenting only one problem.”
The real-world cont’d “How do people sequence tasks? How are hand-offs done? What do you need to communicate? What is relevant at that point? There’s also different domain knowledges to contend with. Our templates are shortcuts, allowing for free text, which is just another way of saying, ‘sorry we didn’t anticipate your needs’. We need to be able to integrate three different things- ontologies, templates and free text (for the exceptions)” “lets use an example … abdominal pain … what gets presented depends on things like, male/female, age, etc. usually we do this through rules but it’s very hard because there would be thousands and you can’t deal with conflicts. This way [ontologies] you can contain the info in concepts … in the attributes … e.g. teratogenic medicines; pregnant, etc.”
Allocation of function Q: Does the model have domain knowledge and presentational knowledge? A: “yes, but not purely. Its in the templates. What’s interesting is that you could, for instance, distinguish a PC and a \mobile device. Or, if I’m going to write an order for a drug, does whether my patient is female and pregnant or not … is that written in the order? Most often that’s ridiculous, but sometimes … So, one of the issues is, is this model sufficient or do you push some things to the physician? We need to model in a way that is consistent with the real world. We need to be able, maybe, to model that ‘it is Tuesday, 11 o’clock at night, in the paediatric ward. The inferencing in OWL could be very useful here.”
Problems and opportunities What’s an ontology (again?) A: “An ontology is one person’s view of domain knowledge …” N: “Oh, I think M. might have something to say about that … M.? M: “An ontology is a shared specification of knowledge” [general laughter]
What’s an ontology (again?) A: “The basic purpose of an ontology is to set a design standard … to enforce in some way the use of a standard set of terms.” M: “I wouldn’t agree with that … ontologies are term-independent … it’s the concepts, not the terms, that define an ontology.” A: “Well, OK. It’s true that domain experts often have a very confusing array of terms (and many duplicates). A plant expert might, for instance, identify a mutant phenotype which he might describe as ‘hairy leaves’- ie using more or less free text description- but might in addition map this description to the ontology through annotation. But in the end, I think you probably do want to get people to use the same set of terms … in the end.”
The Division of Labour “No precise definition of an ontology has ever been given, and they are very difficult to compare unless they are in the same language and used for the same purpose. There’s a lot of confusion about terms and concepts. I think domain terms are best developed by domain experts, perhaps through methods such as text mining … in fact, I’m developing a tool to help them do exactly that … I don’t want to specify these terms, I want to organize them.”
The Division of Labour “I seldom e-mails domain experts with queries because I know the stuff … I’m a biologist … but part of the difficulty is that they don’t always fully appreciate this … one guy came over and said about the books I had on my desk, ‘why are you reading those?’ “
And what’s it for? “Tagging or annotation allows that, but the problem is that many users don’t understand that … they think ontologies are about terminologies but they’re not … the terminology’s irrelevant, it’s the concepts behind them …” N: “[requirements?] we don’t manage it very well … it tends to be very local … there’s a community of existing users … and they hold conferences, workshops, etc and you tend to get a feel for what’s important. At the moment, the biggest things are modularisation and versioning. But we don’t really have the manpower. There’s a few thousand people using it a lot and we’re a four person team trying to develop tools … and we’re reactive …”
The problem of community “I like to think of the structure as a pyramid … the ontology is at the apex … and at the four corners are the people who contribute to it, the ontologists, the computer scientists, the domain experts and the end-users. But all the problems are to do with the communications between them.” “X wanted to develop a system where you could send the ontology into the community and they could find out what they could do with it … but the average biologist is not interested in the details of what you can do, and there’s a problem with this huge mass of data ….”
The problem of community “Even if biologists aren’t writing the ontology, they should be proofing it. We need validation of what’s true …” “At the higher level, I’m going to make decisions- that they won’t understand or be interested in. I’m making knowledge distinctions. Anyone else who produces an ontology has to go through the same process.” “The problem is that I go, ‘here’s what I’ve done, and they go, ‘it’s all rubbish … Either I’m only bothered about what’s going on at my level or everyone feels they can do my job … I see this as more like computer programming … it’s code … and at some point there’ll be arguments about the output … the biologists want it … they want bragging rights … but at some point they also say, ‘that’s all tosh …’’
Modularization as an example … “there are a lot of poor quality ontologies out there- any individual can write one. And one of the problems associated with that is they can compete with each other. If you think about something like an ontology for human disease, we don’t know whether one ontology or several is the right solution … one large ontology is incredibly hard to maintain. There are already something like 50-60,000 concepts out there in OBO [Open Biological Ontology Resource]. “ And re-use is low …
Modularization 2 a trade-off between the size, scope and complexity of an ontology and its ease of use and usefulness. Reflected in problems of querying (and getting ‘just so’ answers), maintenance, the division of labour, etc.
Modularization 3 “ …but with several smaller ones, they can conflict with each other and you have no idea how you maintain consistency … OBO FOUNDRY will choose the promising ontologies … we need to evolve real standards …but if you’re going to do that, there’ll be assurances needed. They need to be correct, complete, let me see …evidence-based … robust, and they have to be maintained”
Unresolved issues Differences in perspective concerning possible use for some proportion of users, ontologies are, ‘things you browse through …’ The degree to which standards can and should be made to apply remains unresolved, who exactly in any given community might benefit The problem of versions The problem of querying ‘Partonomics’ The collaborative user interface?
