I'm not sure what you're asking for here...
Prediction needs to train a machine learning (ML) model. Training an ML
model, needs to think about relevant features first, then feed the data
into the ML algorithm, then use the model to predict things on new data.
Why can't you define the relevant fe
Hello Lorenz,
I also have same thinking i.e to make (data property) values (like 1,2,3)
> for each feature and then fetch them . For example, we have dataset of a
> Student and want to predict their performance, with features, name,
> midExam-score, finalExam-score, lecture-attendance, address, et
no no no ...
SPARQL is a query language and not a machine learning algorithm.
RDF dataset D -> SPARQL query Q -> Q(D) = part of the data by means of
graph pattern matching. there is no induction, confidence values, etc.
define "most appropriate", then write the query to select those
"features"
Hello Lorenz sorry for inconvenience.
Suppose we have a dataset about software effort estimation which have about
100 attributes (line of code, complexity etc). To extract the subset of
features (as all 100 attributes are not important), we usually used some
data mining algorithms (Genetic algorit
Javed...I don't know how many mails you wrote to this list (and the
Protege Users list), but shouldn't you already have learned that you
have to provide more information, data, code, queries, use case, etc. in
order to get help?!
What do you expect as an answer on a question "can we ..." - my answ
Hello
For instance, we have to select some features ( for data mining) , can we
do it using Semantic Web technologies like Ontology and SPARQL. ?
Thanks
