Hi Nitin, I'm assuming you're asking about Latent Semantic Indexing and similar. This may not be the best place to ask about this. Not sure where else to suggest though.
If I understand your quesion correctly, the basic idea is that you take a document (usually text but it could also be an image, video, whatever) and create a meaningful vector from it. With a text document you could create the vector by specifying each word in your vocabulary as a dimension and defining the position in that dimension as the number of occurences of that word. You can then treat the Euclidian distance between documents as a measure of similarity. Similar documents are clustered together. With images you need different techniques to create the vectors. Different vector creation algorithms create different measures of similarity (colour, shape, etc). I wouldn't say that semantic indexing methods are "different" from the semantic web, because I see them as overlapping concepts. From my perspective the semantic web comprises of many approaches, both top-down (centralised interpretation of meaning like LSI) and bottom-up (manual meta-tagging of data through mark-up, linking, microformats, etc.) Does that help at all? If anyone has corrections, or more to add, please pile in... I'm wearing my flame-retardant underpants :) Rich 2009/4/1 nitin gopi <nitdaii...@gmail.com>: > hi all, > I want to know everything about semantic vectors. I want to know how > does it indexes the documents such that the results produced are > semantically better than normal search. I also want to know how it is > different from semantic web, which uses the concept of ontologies and > metadata. It would be very helpful if somebody mail me all the study > material related to it? > > Thanking You > Nitin > -- Richard Marr richard.m...@gmail.com 07976 910 515 --------------------------------------------------------------------- To unsubscribe, e-mail: java-user-unsubscr...@lucene.apache.org For additional commands, e-mail: java-user-h...@lucene.apache.org
