I would like to visualize an Ontology model created through Jena in Java. Is there an API which can help me out in this task ? Or at least a tutorial on the matter?
Thanks in advance.
You can use the JUNG API (http://jung.sourceforge.net/) to visualize the ontology as a graph.
Cytoscape (www.cytoscape.org/) is a great tool for graph visualization, and has been widely used by linked data repositories.
Related
Hello Stack overflow community;
I am working in a scholar project using Neo4j database and i need help from members which are worked before with neo4j gds in order to finding a solution for my problem;
i want to apply a community detection algorithm called "Newman-Girvan" but it doesn't exist any algorithm with this name in neo4j gds library; i found an algorithm called "Modularity Optimization", is it the Newman-Girvan algorithm and just the name is changed or it is a different algorithm?
Thanks in advance.
I've not used the newman-girvan algorithm, but the fact that it's a hierarchical algorithm with a dendrogram output suggests you can use comparable GDS algorithms, specifically Louvain, or the newest, Leiden. Leiden has the advantage of enforcing the generation of intermediary communities. I've used both algorithms with multigraphs; I believe this capability was just introduce with gdg v 2.x.
The documentation on the algorithms is at
https://neo4j.com/docs/graph-data-science/current/
https://neo4j.com/docs/graph-data-science/current/algorithms/alpha/leiden/
multigraph:
https://neo4j.com/docs/graph-data-science/current/graph-project-cypher-aggregation/
I have been recently introduced to the applications of ML in Cybersecurity, and I was interested in working on an application of GANs to generate data for sparse datasets
(Something like this https://becominghuman.ai/deep-learning-can-tackle-the-problem-of-sparse-data-by-augmenting-available-data-e9a4e0f1fc92)
However, I am not aware of the sort of datasets that can be used for this purpose. Could someone guide me through a few example datasets I can use to train a GAN on and to generate data? Are text datasets any good for GAN related generation?
My objective here is to simply understand how this whole process should work. Any help would be appreciated.
Have you come across this repository? I guess it contains datasets and more!
I decided to take a dip into ML and with a lot of trial and error was able to create a model using TS' inception.
To take this a step further, I want to use their Object Detection API. But their input preparation instructions, references the use of Pascal VOC 2012 dataset but I want to do the training on my own dataset.
Does this mean I need to setup my datasets to either Pascal VOC or Oxford IIT format? If yes, how do I go about doing this?
If no (my instinct says this is the case), what are the alternatives of using TS object detection with my own datasets?
Side Note: I know that my trained inception model can't be used for localization because its a classifier
Edit:
For those still looking to achieve this, here is how I went about doing it.
The training jobs in the Tensorflow Object Detection API expect to get TF Record files with certain fields populated with groundtruth data.
You can either set up your data in the same format as the Pascal VOC or Oxford-IIIT examples, or you can just directly create the TFRecord files ignoring the XML formats.
In the latter case, the create_pet_tf_record.py or create_pascal_tf_record.py scripts are likely to still be useful as a reference for which fields the API expects to see and what format they should take. Currently we do not provide a tool that creates these TFRecord files generally, so you will have to write your own.
Except TF Object Detection API you may look at OpenCV Haar Cascades. I was starting my object detection way from that point and if provide well prepared data set it works pretty fine.
There are also many articles and tutorials about creating your own cascades, so it`s easy to start.
I was using this blog, it helps me a lot.
Can some refer me to a good image dataset with multiple classes and has images for training and testing data at the same place ?
Thanks
It would be good to know, what's your intention. Here are some recommendations.
ALOI: http://staff.science.uva.nl/~aloi/
Microsoft Research Cambridge Object Recognition Image Database: http://research.microsoft.com/en-us/downloads/b94de342-60dc-45d0-830b-9f6eff91b301/default.aspx
The PASCAL Visual Object Classes Homepage: http://pascallin.ecs.soton.ac.uk/challenges/VOC/
Hi the aim is to parse a sizeable corpus like wikipedia to generate the most probable parse tree,and named entity recognition. Which is the best library to achieve this in terms of performance and accuracy? Has anyone used more than one of the above libraries?
I use in my experiments the standford tagger but it really depends on the quality of your articles from wikipedia. Here you will find a comparison of different part-of-speech taggin implmentations - PoS on aclweb.
I'm currently using Enju HPSG parser which seems to be better than the others.
Refer to this paper: http://nlp.stanford.edu/pubs/lrecstanforddeps_final_final.pdf