Is there a way to prioritize Wit.ai entities? For example, sometimes a free-text entity is extracted even though an exact match keyword from another entity is available. Or is training this specific example my only option?
I'm having trouble understanding in which order are the trait, free-text and keyword entities extracted. Is there a document explaining the extraction process?
Related
I'm trying to build an intelligent field mapper and would like to know whether Optaplanner is the correct fit. The requirement is as follows
I have a UI which accept source and target xml field schema.
The source and target schema contains multiple business fields which can be further classified to multiple business groups. There will be certain rules (which we can consider as constraints as per optaplanner) which needs to be considered during the field mapping. The objective of the tool is to find the field mapping ( Each source field needs to find its best fit target field)
Can optaplanner be used to solve this problem ? I'm confused whether this is a mathematical optimzation problem or a machine learning predictive model problem (For this to work, i need to work on building sufficient labelled mapping data )
Any help will be much appreciated.
From the top of my head, there are 3 potential situations to be in, going from easy to hard:
A) You can use a decision table to figure the mapping decision. Don't use OptaPlanner, use Drools.
B) Given two mapping proposals, you can score which one is better, through a formal scoring function. Use OptaPlanner.
C) You don't know your scoring function in detail yet. You have some vague ideas. You want to use training data of historical decisions to build one. Don't use OptaPlanner, use Machine Learning.
I have seen an article in Dzone regarding Post and Post Details (two different entities) and the relations between them. There the post and its details are in different tables. But as I see it, Post Detail is an embeddable part because it cannot be used without the "parent" Post. So what is the logic to separate it in another table?
Please give me a more clear explanation when to use which one?
Embeddable classes represent the state of their parent classes. So to take your example, a StackOverflow POST has an ID which is invariant and used in an unbreakable URL for sharing e.g. http://stackoverflow.com/q/44017535/146325. There are a series of other attributes (state, votes, etc) which are scalar properties. When the post gets edited we have various versions of the text (which are kept and visible to people with sufficient rep). Those are your POST DETAILS.
"what is the logic to separate it in another table?"
Because keeping different things in separate tables is what relational databases do. The standard way of representing this data model is a parent table POST and child table POST_DETAIL with a defined relationship enforced through a foreign key.
Embeddable is a concept from object-oriented programming. Oracle does support object-relational constructs in the database. So it would be possible to define a POST_DETAIL Type and create a POST Table which has a column declared as a nested table of that Type. However, that would be a bad design for two reasons:
The SQL for working with nested tables is clunky. For instance, to get the POST and the latest version of its text would require unnesting the collection of details every time we need to display it. Computationally not much different from joining to a child table and filtering on latest version flag, but harder to optimise.
Children can have children themselves. In the case of Posts, Tags are details because they can vary due to editing. But if you embed TAG in POST_DETAIL embedded in POST how easy would it be to find all the Posts with an [oracle] tag?
This is the difference between Object-Oriented design and relational design.
OO is strongly hierarchical: everything is belongs to something and the way to get the detail is through the parent. This approach works well when dealing with single instances of things, and so is appropriate for UI design.
Relational prioritises commonality: everything of the same type is grouped together with links to other things. This approach is suited for dealing with sets of things, and so is appropriate for data management tasks (do you want to find all the employees who work in BERLIN or whose job is ENGINEER or who are managed by ELLIOTT?)
"give me a more clear explanation when to use which one"
Always store the data relationally in separate tables. Build APIs using OO patterns when it makes sense to do so.
I'm using Azure Search on my e-commerce site, and now want to implement filtering and I can't choose right way to do is. The problem is that I have really different product types, so they have various attributes (properties) and I don't want to create index with 50 attribute fields to faced them all. Another way - I can define few properties (like Attribute1, Attribute2 ... ) and then determine their 'Key' names according to facated values, but it sound not so good too. Is there some common or checked way to build filters on e-commerce sites?
Azure Search will do well if you have 50 fields that have sparse values. Assuming that sparseness comes with relatively low per-facet cardinality, it shouldn’t have a bad performance impact, and by explicitly modeling stuff as fields you get to keep the nice and clean programming model that comes from having explicit fields.
The attribute mapping thing would work assuming all facets are of the same data type or can be grouped easily.
Another thing you can do for string facets is to have a prefix in the facet value (e.g. Cameras/Canon, Memory/MiniSD, etc.). That gives you counts per value within the parent scope. If you also have the parent scope (e.g. Camera, Memory) in a separate field you can filter by the whole scope if needed. This generalizes well into hierarchical facets (as long as they are all strings). Here is a blog post that explores this technique.
Background
I have 2 resources: courses and professors.
A course has the following attributes:
id
topic
semester_id
year
section
professor_id
A professor has the the following attributes:
id
faculty
super_user
first_name
last_name
So, you can say that a course has one professor and a professor may have many courses.
If I want to get all courses or all professors I can: GET /api/courses or GET /api/professors respectively.
Quandary
My quandary comes when I want to get all courses that a certain professor teaches.
I could use either of the following:
GET /api/professors/:prof_id/courses
GET /api/courses?professor_id=:prof_id
I'm not sure which to use though.
Current solution
Currently, I'm using an augmented form of the latter. My reasoning is that it is more scale-able if I want to add in filtering/sorting criteria.
I'm actually encoding/embedding JSON strings into the query parameters. So, a (decoded) example might be:
GET /api/courses?where={professor_id: "teacher45", year: 2016}&order={attr: "topic", sort: "asc"}
The request above would retrieve all courses that were (or are currently being) taught by the professor with the provided professor_id in the year 2016, sorted according to topic title in ascending ASCII order.
I've never seen anyone do it this way though, so I wonder if I'm doing something stupid.
Closing Questions
Is there a standard practice for using the query string vs the resource path for filtering criteria? What have some larger API's done in the past? Is it acceptable, or encouraged to use use both paradigms at the same time (make both endpoints available)? If I should indeed be using the second paradigm, is there a better organization method I could use besides encoding JSON? Has anyone seen another public API using JSON in their query strings?
Edited to be less opinion based. (See comments)
As already explained in a previous comment, REST doesn't care much about the actual form of the link that identifies a unique resource unless either the RESTful constraints or the hypertext transfer protocol (HTTP) itself is violated.
Regarding the use of query or path (or even matrix) parameters is completely up to you. There is no fixed rule when to use what but just individual preferences.
I like to use query parameters especially when the value is optional and not required as plenty of frameworks like JAX-RS i.e. allow to define default values therefore. Query parameters are often said to avoid caching of responses which however is more an urban legend then the truth, though certain implementations might still omit responses from being cached for an URI containing query strings.
If the parameter defines something like a specific flavor property (i.e. car color) I prefer to put them into a matrix parameter. They can also appear within the middle of the URI i.e. /api/professors;hair=grey/courses could return all cources which are held by professors whose hair color is grey.
Path parameters are compulsory arguments that the application requires to fulfill the request in my sense of understanding otherwise the respective method handler will not be invoked on the service side in first place. Usually this are some resource identifiers like table-row IDs ore UUIDs assigned to a specific entity.
In regards to depicting relationships I usually start with the 1 part of a 1:n relationship. If I face a m:n relationship, like in your case with professors - cources, I usually start with the entity that may exist without the other more easily. A professor is still a professor even though he does not hold any lectures (in a specific term). As a course wont be a course if no professor is available I'd put professors before cources, though in regards to REST cources are fine top-level resources nonetheless.
I therefore would change your query
GET /api/courses?where={professor_id: "teacher45", year: 2016}&order={attr: "topic", sort: "asc"}
to something like:
GET /api/professors/teacher45/courses;year=2016?sort=asc&onField=topic
I changed the semantics of your fields slightly as the year property is probably better suited on the courses rather then the professors resource as the professor is already reduced to a single resource via the professors id. The courses however should be limited to only include those that where held in 2016. As the sorting is rather optional and may have a default value specified, this is a perfect candidate for me to put into the query parameter section. The field to sort on is related to the sorting itself and therefore also belongs to the query parameters. I've put the year into a matrix parameter as this is a certain property of the course itself, like the color of a car or the year the car was manufactured.
But as already explained previously, this is rather opinionated and may not match with your or an other folks perspective.
I could use either of the following:
GET /api/professors/:prof_id/courses
GET /api/courses?professor_id=:prof_id
You could. Here are some things to consider:
Machines (in particular, REST clients) should be treating the URI as an opaque thing; about the closest they ever come to considering its value is during resolution.
But human beings, staring that a log of HTTP traffic, do not treat the URI opaquely -- we are actually trying to figure out the context of what is going on. Staying out of the way of the poor bastard that is trying to track down a bug is a good property for a URI Design to have.
It's also a useful property for your URI design to be guessable. A URI designed from a few simple consistent principles will be a lot easier to work with than one which is arbitrary.
There is a great overview of path segment vs query over at Programmers
https://softwareengineering.stackexchange.com/questions/270898/designing-a-rest-api-by-uri-vs-query-string/285724#285724
Of course, if you have two different URI, that both "follow the rules", then the rules aren't much help in making a choice.
Supporting multiple identifiers is a valid option. It's completely reasonable that there can be more than one way to obtain a specific representation. For instance, these resources
/questions/38470258/answers/first
/questions/38470258/answers/accepted
/questions/38470258/answers/top
could all return representations of the same "answer".
On the /other hand, choice adds complexity. It may or may not be a good idea to offer your clients more than one way to do a thing. "Don't make me think!"
On the /other/other hand, an api with a bunch of "general" principles that carry with them a bunch of arbitrary exceptions is not nearly as easy to use as one with consistent principles and some duplication (citation needed).
The notion of a "canonical" URI, which is important in SEO, has an analog in the API world. Mark Seemann has an article about self links that covers the basics.
You may also want to consider which methods a resource supports, and whether or not the design suggests those affordances. For example, POST to modify a collection is a commonly understood idiom. So if your URI looks like a collection
POST /api/professors/:prof_id/courses
Then clients are more likely to make the associate between the resource and its supported methods.
POST /api/courses?professor_id=:prof_id
There's nothing "wrong" with this, but it isn't nearly so common a convention.
GET /api/courses?where={professor_id: "teacher45", year: 2016}&order={attr: "topic", sort: "asc"}
I've never seen anyone do it this way though, so I wonder if I'm doing something stupid.
I haven't either, but syntactically it looks a little bit like GraphQL. I don't see any reason why you couldn't represent a query that way. It would make more sense to me as a single query description, rather than breaking it into multiple parts. And of course it would need to be URL encoded, etc.
But I would not want to crazy with that right unless you really need to give to your clients that sort of flexibility. There are simpler designs (see Roman's answer)
Association mining seems to give good results for retrieving related terms in text corpora. There are several works on this topic including well-known LSA method. The most straightforward way to mine associations is to build co-occurrence matrix of docs X terms and find terms that occur in the same documents most often. In my previous projects I implemented it directly in Lucene by iteration over TermDocs (I got it by calling IndexReader.termDocs(Term)). But I can't see anything similar in Solr.
So, my needs are:
To retrieve the most associated terms within particular field.
To retrieve the term, that is closest to the specified one within particular field.
I will rate answers in the following way:
Ideally I would like to find Solr's component that directly covers specified needs, that is, something to get associated terms directly.
If this is not possible, I'm seeking for the way to get co-occurrence matrix information for specified field.
If this is not an option too, I would like to know the most straightforward way to 1) get all terms and 2) get ids (numbers) of documents these terms occur in.
You can export a Lucene (or Solr) index to Mahout, and then use Latent Dirichlet Allocation. If LDA is not close enough to LSA for your needs, you can just take the correlation matrix from Mahout, and then use Mahout to take the singular value decomposition.
I don't know of any LSA components for Solr.
Since there are still no answers to my questions, I have to write my own thoughts and accept it. Nevertheless, if someone propose better solution, I'll happily accept it instead of mine.
I'll go with co-occurrence matrix, since it is the most principal part of association mining. In general, Solr provides all needed functions for building this matrix in some way, though they are not as efficient as direct access with Lucene. To construct matrix we need:
All terms or at least the most frequent ones, because rare terms won't affect result of association mining by their nature.
Documents where these terms occur, again, at least top documents.
Both these tasks may be easily done with standard Solr components.
To retrieve terms TermsComponent or faceted search may be used. We can get only top terms (by default) or all terms (by setting max number of terms to take, see documentation of particular feature for details).
Getting documents with the term in question is simply search for this term. The weak point here is that we need 1 request per term, and there may be thousands of terms. Another weak point is that neither simple, nor faceted search do not provide information about the count of occurrences of the current term in found document.
Having this, it is easy to build co-occurrence matrix. To mine association it is possible to use other software like Weka or write own implementation of, say, Apriori algorithm.
You can get the count of occurrences of the current term in found document in the following query:
http://ip:port/solr/someinstance/select?defType=func&fl=termfreq(field,xxx),*&fq={!frange l=1}termfreq(field,xxx)&indent=on&q=termfreq(field,xxx)&sort=termfreq(field,xxx) desc&wt=json