Hi regarding:
On Tue, Aug 27, 2013 at 9:41 AM, Dileepa Jayakody <dileepajayak...@gmail.com> wrote: >> > >> > - Can I use 'topic' enhancement engine in Stanbol to provide >> > fise:TopicAnnotations required in the 3rd module? >> > >> > If you can train a model based on your data it should work. The engine >> would give your topics for the parsed text and your disambiguation engine >> would compare the topics referenced of possible FOAF profiles with the one >> detected by the TopicEngine for the text. >> >> But I am wondering where you can get the trainings data for such a model. >> You would need a set of documents for all the categories used by FOAF >> files. >> > > I think I haven't yet grasped the topic-annotation concept in stanbol > properly. I was expecting to use the topic-engine configured in the > enhancement chain, and retrieve TopicAnnotations out of the box and use > those TopicAnnotations to match against foaf:primaryTopic, foaf:interest > properties in my engine...to train a model can I use an existing model > rather than training it with foaf site I have implemented? Forgive me if > this is a stupid question :) > There is no existing model for general purpose topics. ogrisel had started with such (based on dbpedia categories) but never finished his work. So you would need to train your own model. >> >> > - Does SentimentAnalysis engine work? if so will >> > fise:SentimentAnnotations be useful for Topic based matching? >> > >> > The sentiment engines do work, but I do not see how they can improve >> topic >> based matching. Can you maybe explain your intensions. >> > I was initially thinking that, topics and sentiment-summaries can be > co-related, therefore use thse sentimentAnnotations to map with > foafi:primaryTopic/interest in above suggested 3rd module. > Maybe this is something not so practical :) > The sentiment engines do select sections of the text and provide you with a sentiment value [-1 .. +1] for those. The topic engine provides you categories for the whole document. I do not se how you could possible co-relate those information. If you would also run the topic engine on smaller sections of the text you could possible detect if a "topic" is mentioned in a positive or negative context. best Rupert > I will start implementing with a simple model to use several entity-linking > engines to propose entityAnnotations as much as possible and use the 2nd > module's approach to co-ref foaf relationships. > Will update the thread with my progress.. > > Thanks a lot for your valuable insight. > > Regards, > Dileepa > >> best >> Rupert >> >> >> > >> > >> > Would like your suggestions, ideas as much as possible to improve my FOAF >> > co-reference based disambiguation engine. >> > >> > Below is a block diagram of the workflow. >> > >> > [image: Inline image 1] >> > >> > source : >> > >> http://creately.com/diagram/example/hjs4yd0e1/FOAF_Disambiguation_WorkFlow >> > >> > Thanks, >> > Dileepa >> > >> > Reference : >> > 1. "Computing FOAF Co-reference Relations with Rules and Machine >> > Learning",Jennifer Sleeman and Tim Finin, University of Maryland, >> Baltimore >> > County, In proceedings of The Third International Workshop on Social Data >> > on the Web, November 2010 >> > >> > >> > >> > >> > >> > >> > >> >> >> -- >> | Rupert Westenthaler rupert.westentha...@gmail.com >> | Bodenlehenstraße 11 ++43-699-11108907 >> | A-5500 Bischofshofen >> -- | Rupert Westenthaler rupert.westentha...@gmail.com | Bodenlehenstraße 11 ++43-699-11108907 | A-5500 Bischofshofen
