I have studied ontologies for some time and one thing is still not clear to me.
What is the difference between taxonomical and non-taxonomical relationships in the ontology? Could you please give me some example?
Non-taxonomic:
Foot is part of the Leg.
Earth is bigger than Mercury
Pearls before Swine
Taxonomic:
Man is a Person
Car is a Vehicle
Taxonomy is an Ontology
Related
I have a question regarding training conversations, the context is that the next statement is not necessarily a function of the previous statement but also of any statement in the body of conversation for example:
person1: what is your favorite food and restaurant
person 2: my favorite food is burger and McDonald is my fav restaurant
Person1: why do you like burger
person 2: because i dont care about the health aspect while eating
person1: why do you like mcdonalds when there are so many places where you can buy a burger
now as we can see the last question was derived from an answer received 3 steps before...
In this context how do i train an lstm so that it remembers all the previous contexts..
essentially i am looking for an approach to create my training data and output sentence..
Not sure that LSTM will give you sufficient long-term memory for the example conversation you posted. You will likely need to use some sort of transformer memory network to maintain context. Take a look at approaches for the "persona chat" problem as well as this recent paper on handling conversational context.
I've made my way over to this community because I'm planning on building a widget to help medical students with understanding the effects of various autonomic medications on cardiovascular metrics like heart rate (HR), BP (systolic, diastolic, and mean) and peripheral resistance (SVR). Some background - I'm a 3rd year med student in the US without fluency in any programming languages (which makes this particularly difficult), but am willing to spend the time to pick up what I need to know to make this happen.
Regarding the project:
The effects of autonomic medications like epinephrine, norepinephrine, beta-blockers, and alpha-blockers on the cardiovascular system is of great interest to physicians because these drugs can be used to resuscitate, to prep for surgery, to slow the progression of cardiovascular and respiratory disease, and even as antidotes for certain toxicities. There are four receptor types we are primarily concerned with - alpha1, alpha2, beta1, beta2. The receptor selectivity profile of any given drug is what governs its effects on the CV system. The way these effects are taught and tested in med school classrooms and by the United States board exams is in the form of graphs.
The impetus for this project is that me and many of my classmates struggled with this concept when we were initially learning it, and I believe a large part of that arises from the lack of a resource which shows the changes in the graphs from baseline, in real time.
When being taught this info, we are required to consider: a) the downstream effects when the receptor types listed above are stimulated (by an agonist drug) or inhibited (by an antagonist); b) the receptor specificities of each of the autonomic drugs of interest (there are about 8 that are very important); c) how to interpret the graphs shown above and how those graphs would change if multiple autonomics were administered in succession. (Exams and the boards love to show graphs with various points marked along it, then ask which drugs are responsible for the changes seen, just like the example above.)
The current methods of learning these three points is a mess, and having gone through it, I'd like to do what I can to contribute to building a more effective resource.
My goal is to create a widget that allows a user to visualize these changes with up to 3 drugs in succession. Here is a rough sketch of the goal.
In this example, norepinephrine has strong alpha1 agonist effects which causes an increase in systolic (blue line), diastolic (red line), and mean BP, as well as peripheral resistance. Due to the increased BP, there is a reflexive decrease in HR.
Upon the administration of phentolamine, a strong alpha1 antagonist, the BP and SVR decline while HR increases reflexively.
Regarding the widget, I would like the user to be able to choose up to 3 drugs from a drop down menu (eg. Drug 1, Drug 2, Drug 3), and the graphs to reflect the effects of those drugs on the CV metrics while ALSO taking into account the interactions of the drugs with themselves.
This is an IMPORTANT point: the order in which drugs are added is important because certain receptors become blocked, preventing other drugs from having their primary effect so they revert to their secondary effect.
If you're still following me on this, what I'm looking for is some help in figuring out how best to approach all the possibilities that can happen. Should I try to understand if-then statements and write a script to produce graphs based off those? (eg. if epi, then Psys = x, Pdia = y, MAP = z). Should I create a contingency table in excel in which I list the 8 drugs I'm focusing on and make values for the metrics and then plot those, essentially taking into account all the permutations? Any thoughts and direction would be greatly appreciated.
Thank you for your time.
What is the name for the ordered relation between nodes?
For example: A color ontology represented in a trie has ordered color objects such that the marginal node between yellow and blue is green, and node between blue and green is teal, etc. I called this indexical.
I found that the term indexical is owned by linguistics (en.wikipedia.org/wiki/Indexicality). I had used the term indexical in academic presentations with a civil engineering audience that has more of a computer science awareness than usual -- nobody questioned my definition.
Searching online I found 'edit distance' and 'ordered.' Neither has the meaning I want.
In my presentation, I use the spork, the spoon and fork, as an example of a marginal object that requires a new node between spoon and fork (www.youtube.com/watch?v=ruJ76-o5lxU).
A broad example: Take every product in a grocery store and line them up, arrange those items with a closeness that represents their similarity. So, oranges and apples will be closer together than beef and fish. Which will both be closer together than to paper towels.
EDIT1: Revised the examples to any position between two points.
EDIT2: Simplified question.
Trying to design an ER diagram from a given brief for a university project.
I'm confused how I should handle this problem:
The items sold in the Food Truck can be of different types: burritos and
beverages. Every item have an ID, a description and a price. Assume that every
Food Truck has infinite stock of each item (i.e. we do not need to track stock levels
in each Food Truck).
All Burritos come with rice, a type of bean, a filling, and a set of optional
toppings. Burritos are priced by size (Mini, Regular, and Grande). Bean types will
vary. Chipp will start by offering two types: black beans and red beans. Burrito
fillings will vary (depending on the season). There are at least 3 types of Burrito
fillings and there should be a vegetarian option.
A Burrito may optionally have toppings: lettuce, tomato, and mild and hot
salsa. Toppings are free, but Chipp will also offer guacamole as a topping for which
there is an extra charge.
The Food Truck also sells different types of refreshing beverages, both
alcoholic and non-alcoholic. All beverages have a size measured in milliliters (just in
case Chipp takes his Food Truck business over the Channel to mainland Europe).
The solution I have got to so far is by making two weak entities, both with a relationship, like this:
Is this the correct way to handle the problem?
Chen's original notation had no symbols for subtyping. A weak entity set without a weak key produced the same result. Your approach is correct within that framework. However, in the same original notation, weak entity sets were associated with identifying relationships (double-bordered diamond) and total participation was indicated with a double line between the entity set and relationship, rather than the (min,max) style of cardinality indicator. This isn't a recommendation to stick to the original notation, but it may be a good idea to verify your answer against your textbook on these points.
A number of different extension notations have been developed to represent subtyping, and to indicate disjointness, which the original notation couldn't. If any of these are covered in your curriculum, I suggest you use them as they're more expressive.
Note also the extra charge requirement on guacamole, which your diagram doesn't include yet. Finally, you indicated price as a derived attribute of item, but I don't see any other attributes it could be calculated from.
Lets consider one case, we have services at three levels
Rose
-red rose
- red rose by A dealer
- red rose by B dealer
-yellow rose
- yellow rose by A dealer
- yellow rose by B dealer
So what would be the ideal scenario for creating database relations between tables:
I want to create db structure for product and service/flavours. for example lets say rose is my product then red rose, yellow rose, black rose are my services and further red rose by A dealer and red rose by B dealer is further my services...in this scenario how I will make collections in mongodb.
Your data seems relational. MongoDB is not a relational database. That means you can't create relations in your data. If you want to use MongoDB you'll have to create the relations between the data yourself. If it's worth it, that's up to you. Most people choose MongoDB for speed, but when your data is very dependent on relationships, MongoDB might not help you at all.
So if you would create this strucuture in MongoDB, you would have three different collections: roses, flavors and dealers. If you want to get a rose for a certain flavor and a certain dealer, you'll have to execute 3 separate queries, and then join the 3 results together, where in mysql you can do 1 query where you get 1 result. Note that this difference gets bigger with every layer of relationships.
It could still be possible that doing the separate MongoDB queries is faster in the end, if your collections are really large, and maybe if you need to do complex filtering on it. You should just remember that it's not a free lunch, you'll have a completely different setup compared to a relational database and you should really think about it.
For speed, you can also consider adding a cache layer on top of your relational database, which you could in fact use MongoDB for.