We know that in OWL 2, ∃p.C denotes the class of individuals that have a value for the property p that is of type C.
When we write ∃r.∃s.C(a), what will be the best way to read this in plain English?
On a similar note, how do we read ∀r.∃s.C(a) that I can explain even to my grandmother?
Some real-life examples would be nice.
Please answer in detail analyzing each part.
Related
I am using apache arrow golang library to read parquet. No-repeated column seems straight forward, but how can I read repeated field?
For reading repeated fields in Parquet there's really two answers: a complex way and an easy way.
The easy way is to use the pqarrow package and just read directly into an Arrow list array of some kind and let the complexity be handled for you. (https://pkg.go.dev/github.com/apache/arrow/go/v10#v10.0.1/parquet/pqarrow)
To read them the complex way, you have to understand repetition and definition levels and how Parquet uses them. Instead of trying to explain them here, I'm going to point you to the excellent write-up on the Apache Arrow blog here: https://arrow.apache.org/blog/2022/10/08/arrow-parquet-encoding-part-2/ which explains how to decode definition and repetition levels (yes it's in the context of the Rust implementation of Parquet, but the basic concepts are the same for the Go implementation).
All of the ColumnChunkReader types allow you to retrieve those Definition and Repetition levels in their ReadBatch methods. For an example have a look at https://pkg.go.dev/github.com/apache/arrow/go/v10#v10.0.1/parquet/file#Float32ColumnChunkReader.ReadBatch
When you call ReadBatch you can pass an []int16 for the definition levels and the repetition levels to be filled in alongside the data, and then you can use those to decode the repeated field accordingly. Personally, I prefer to use the pqarrow package which does it for you, but sometimes you do need the granular access.
I have users that have authenticated with a social media site. Now based on their last X (let's say 200) posts, I want to map how much that content matches up with a finite list of keywords.
What would be the best way to do this to capture associated words/concepts (maybe that's too difficult) or just get a score of how much, say, my tweet history maps to 'Walrus' or 'banana'?
Would a naive Bayes work here to separate into 'matches' and 'no match'?
In Python I would say NLTK can easily do it. In Ruby maybe gem called lda-ruby will help you. Whole LDA concept is well explained here - look at Sarah Palin's email for example. There's even the example of an app (not entirely in Ruby, but still) which did that -> github.com/echen/sarah-palin-lda
Or maybe I just say stupid things and that can't help you at all. I'm not an expert ;)
A simple bayes would work in this case, it is highly used to detect if emails are spam or not so for a simple keyword matching it should work pretty well.
For this problem you could also apply a recommendation system where you look for the top recommended keyword for a user (or for a post).
There are a ton of ways for doing this. I would recommend you to read Programming Collective Intelligence. It is explained using python but since you know ruby there should be not problem to understand the code.
I have tags on my website, and I input them one by one when I create a blog post. I love gmail's new feature, that ask you if you want to include X in a mail, if you type Y's name and that you often include both of them in the same messages.
I'd like to do something similar on my website, but I don't know how to represent the tags "related-ness" in an object or database ... thoughts ?
It all boils down to create associations between certain characteristics of your posts and certain tags, and then - when you press the "publish" button - to analyse the new post and propose all tags matched with your post characteristics.
This can be done in several ways from a "totally hard-coded" association to some sort of "learning AI"... and everything in-between.
Hard-coded solutions
This are the simplest algorithms to implement. You should first decide what characteristics of your post are relevant for tagging (e.g.: it's length if you tag them "short" or "long", the presence of photos or videos if you tag them "multimedia-content", etc...). The most obvious is however to focus on which words are used in posts. For example you could build a mapping like this:
tag_hint_words = {'code-development' : ['programming',
'language', 'python', 'function',
'object', 'method'],
'family' : ['Theresa', 'kids',
'uncle Ben', 'holidays']}
Then you would check your post for the presence of the words in the list (the code between [ and ] ) and propose the tag (the word before :) as a possible candidate.
A common approach is to give "scores", or in other word to put a number that indicates the probability a given tag is the right one. For example: if your post would contain the sentence...
After months of programming, we finally left for the summer holidays at uncle Ben's cottage. Theresa and the kids were ecstatic!
...despite the presence of the word "programming" the program should indicate family as the most likely tag to use, as there are many more words hinting.
Learning AI's
One of the obvious limitations of the above method is that - say one day you pick up java beside python - you would probably need to change your code and include words like "java" or "oracle" too. The same applies if you create new tags.
To circumvent this limitation (and have some fun!!) you could try to implement a learning algorithm. Learning algorithms are those who refine their outcome the more you use them (so they indeed... learn!). Some algorithm requires initial training (many spam filters and voice recognition programs need this initial "primer"). Some don't.
I am absolutely no expert on the subject, but two common AI's are: the Naive Bayes Classifier and some flavour of Neural network.
Although the WP pages might look scary, they are surprisingly easy to implement (at least in Python). Here's the recording of a lecture at PyCon 2009 on the subject "Easy AI with Python". I found it very informative and even somehow inspiring! :)
HTH!
You should have a look at this post :
Any suggestions for a db schema for storing related keywords?
If you're looking for a schema for storing related tags it will help.
Relevancy searches where multiple agents play a part are usually done using Collaborative filtering. You might want to give that a look see.
Look up Clustering (Machine Learning algorithm). Don't be intimidated by math, it's a pretty straightforward algorithm. Check out Machine Learning for Hackers for simpler explanations of many Machine Learning algorithms and methods.
I need to develop an application that will index several texts and I need to search for people’s names inside these texts. The problem is that, while a person’s correct name is “Gregory Jackson Junior”, inside the text, the name might me written as:
- Greg Jackson Jr
- Gegory Jackson Jr
- Gregory Jackson
- Gregory J. Junior
I plan to index the texts on a nightly bases and build a database index to speed up the search. I would like recommendation for good books and/or good articles on the subject.
Thanks
Check these related questions.
Algorithm to find articles with similar text
How to search for a person's name in a text? (heuristic)
Your question is incorrectly phrased. The examples do not indicate misspelling but change in the form of writing a full name.
And,
would your search expect to match on words like son with reference to the example?
would it expect to match bob when looking for a name called Robert?
Are you looking for things like this and this?
Ok, reading your comment suggests you do not want to venture into that.
For the record. Use a Bayesian filter. You may use mechanical truck for initializing your algorithm.
I would like to represent data that gives an overview but allows them to drill down in an inline fashion - so if you had a grouping of say 6 objects the user could expand the data and it would show the 6 objects immeadiately below it before any more high level data.
It would appear that MSHFlexgrid gives this ability but I can't find any information about actually using it, or what it's limitations are (can you have differing number of fields and/or can they have different spacing, what about column headers, indentation at for the start, etc).
I found this site, but the images are broken (in ie8 and ff3.5). Google searches show people just using the flat data representation but nothing using the hierarchical properties). Does anyone know any good tutorials or forums with a good discussion about pitfalls?
Due to lack of information about using it, I am thinking of coding my own version but if anyone has done work in this area I haven't found it - I would of thought it would be a natural wish for data representation. If someone has coded a version of this (any language) then I wouldn't mind reading about it - maybe my idea of how to do it wouldn't be the best way.
You might want to check out vbAccelerator. He has a Multi-Column Treeview control that sounds like what you may be looking for. He gives you the source and has some pretty decent samples.
The MSHFlexGrid reference pages and the "using the MSHFlexGrid" topic in the Visual Basic manual?
Sorry if you've already looked at these!