I'm just starting to learn about the semantic web. can I perform CRUD operations using EasyRDF or other libraries?
previously I saw other libraries like Sparqllib in PHP. there is very simple and has no commands to create, update, delete
Looking at the documentation, it seems you can. Read here. There are some abtracted methods for clearing named graphs, and updating specified triples too.
Assuming you have the DB permissions, and the ability to execute SPARQL, you should be able to perform CRUD operations.
Does it have to be EasyRDF? If you were considering python SPARQLWrapper is lightweight and very intuitive as a starting point.
Related
I am trying to build a federated RDF application based on rdf4j and FedX. What I need is to be able to:
Optimize the querying plan and joining strategies.
To expose different and heterogeneous databases (A timeseries or a relational DB for example) in a federated fashion.
I went a little bit through the rdf4j documentation and I got a grasp. And therefore I have some little questions:
Is there any documentation that explains how to implement the SAIL API? I tried to debug and follow the flow of execution of an example query using a RDF memory store and I got lost.
Suppose I want to expose a relational database in my datacenter, Should I implement a SPARQL repository or an HTTP repository? should I in anyway implement the SAIL api?
Concerning fedX, how can I make it possible to use the SERVICE and VALUES terms as proposed in the SPARQL 1.1 federated queries? How can I change the Joning strategies? the query plan?
I know that this can be answered if I dive deeply into the code but I wonder if someone has already exposed some kind of a database using the rdf4j API or even worked and tuned RDF4J.
Thanks to you all!
Is there any documentation that explains how to implement the SAIL API? I tried to debug and follow the flow of execution of an example
query using a RDF memory store and I got lost.
There is a basic design draft but it's incomplete. A more comprehensive HowTo has been in the planning for a while but it never quite gets the priority it needs.
That said, I don't think you need to implement your own SAIL for what you have in mind. There's plenty of existing implementations that can do what you need.
Suppose I want to expose a relational database in my datacenter, Should I implement a SPARQL repository or an HTTP repository?
I don't understand the question. HTTPRepository is a client-side proxy for an RDF4J Server. SPARQLRepository is a client-side proxy for a (non-RDF4J) SPARQL endpoint. Neither has anything to do with relational database.
should I in anyway implement the SAIL api?
It depends on your use case, but I doubt it - at least not right at the outset. I'd probably use an existing R2RML library that is compatible with RDF4J, like for example the R2RML API, or CARML - either a live mapping or an offline batch mapping between the relational data and your triplestore may solve your problem.
Concerning fedX, how can I make it possible to use the SERVICE and VALUES terms as proposed in the SPARQL 1.1 federated queries?
You don't need to "make it possible" to do that, FedX supports this out of the box.
How can I change the Joning strategies? the query plan?
You can't (at least not easily), nor should you want to. Quite a lot of research and development went into RDF4J's and FedX query planning strategies. I'm not saying either is perfect, but you're unlikely to do better.
I have a non-standard way of storing and representing semantic data, and I was looking into some possibilities of supporting SPARQL queries. It seems that the best solution is to implement a so-called driver of a standard API framework, such as Apache Jena, but at least for Jena it's not so clear how can this be done. The following image taken from the official documentation suggests that I should implement the Store API, however I couldn't find documentation concerning this. Furthermore, the Java docs of TDB, Jena's native triple store, implies that there is no Store API.
A secondary question is whether there is a Python alternative to Jena (which is written in Java)?
The AWS documentation is seemingly endless, and different pages tell me different things. For example, one page tells me that AWSDynamoDBObjectMapper is the entry point to working with DynamoDB, while another tells me that AWSDynamoDB is the entry point to working with DynamoDB. Which class should I be using? Why?
EDIT: One user mentioned he didn't understand the question. To be more clear, I want to know, in general, what the difference is between using AWSDynamoDB and AWSDynamoDBObjectMapper as entry points to interfacing a DynamoDB.
Doc links for both:
AWSDynamoDB
AWSDynamoDBObjectMapper
Since both can clearly read, write, query, and scan, you need to pick out the differences. It appears to me that the ObjectMapper class supports the concept of mapping an AWSDynamoDBModel to a DB vs. directly manipulating specific objects (as AWSDynamoDB does). Moreover, it appears that AWSDynamoDB also supports methods for managing tables directly.
I would speculate that AWSDynamoDB is designed for managing data where the schema is pre-defined on the DB, and AWSDynamoDBObjectMapper is designed for managing data where the schema is defined by the client.
All of this speculation aside though, the most important bit you can glean from the documentation is:
Instead of making the requests to the low-level DynamoDB API directly from your application, we recommend that you use the AWS Software Development Kits (SDKs). The easy-to-use libraries in the AWS SDKs make it unnecessary to call the low-level DynamoDB API directly from your application. The libraries take care of request authentication, serialization, and connection management. For more information, go to Using the AWS SDKs with DynamoDB in the Amazon DynamoDB Developer Guide.
I would recommend this approach rather than worrying about the ambiguity of class documentation.
I am working on a project that has over 2000 integration tests that full circles the database. I want to speed up the process so I thought why not fake the database.
We are using Fluent NHibernate as our ORM which is probably why I am having such trouble. We have already implemented this concept in a program that does not use NHibernate but rather basic CRUD operations.
Basically on any CRUD operation I would like to save the object in memory to say a dictionary list. This will speed up the process of our tests which will hopefully lessen our build times. Not to mention the cool factor.
I have looked into having two separate sessions and having the session factory use one or the other but I would have to implement many methods/properties that I really don't care about.
I also considering using a go between in class that does the translating myself but it would probably include me changing A LOT of existing code. I am trying to limit the impact on the rest of the project as much as possible so regression testing will not be a HUGE factor.
Please let me know of any other suggestions anyone has!
Thanks!
Use a SQLite in-memory database. Here's the blog post that originally showed me how...
http://jasondentler.com/blog/2009/08/nhibernate-unit-testing-with-sqlite-in-memory-db/
I am developing a demo of semantic web-based Information System, which just uses SPARQL instead of traditional SQL to manipulate dataset. How the application can demonstrate Semantic Web benefits.
I did steps as below:
The client gets parameters from web UI.
Requests a web service.
The service generates a SPARQL command according to given parameters.
The service uses Jena/SDB API to execute the SPARQL command.
Retrieves or persists data from or to MySQL.
Parsing returned result set.
Responses a JSON object to the client.
The client uses Javascript + html to display data.
Currently, the application just has CRUD operations. Only one difference to the traditional IS, which is using SPARQL instead of SQL. It seems that cannot see obviously semantic features. I'm just thinking of two points:
To demonstrate data federating through SPARQL. From this point, can I imagine that the system can be broken down into several subsystem and work on their independent dataset but they can communicate with each other by SPARQL, which because they work on the RDF specification.
Reasoning over datasets. I use Ontologies to describe data schema, should my reasoning operation need to based on them. In my application, I try to get a RDF model, and use Pellet to do inferences. Is that corrent way?
Basically, if the application can demostrate data federating and reasoning, which can be seen as a semantic web-based application. Do I understand it right?
Hopefully, the application can combine services together automatically through semantic description. Furthermore, any other third party data sources may be communicate with the system and work immediately.
Yes ,you are right.the benefit with semantic web being you can write separate set of ontologies which will describe the domains(e.g. product,user) and then combine them using inference ,reasoning and make the data seem much more useful(r.g. product types and user preferences).
The difference being the rules for the data are now written with the data and not in the business logic layer.
Hope this helps .:)