Hey all, a question on possible search paths/structure. If we have a text document, and we have run our magic over it and come away with Topics and Entities (Like, Barack Obama and Apple Inc.) and we have a relevancy score for each one, what would be the best way to store and query against them?
we currently are trying a parent/child relationship, where the children are the terms with their relevancy score and the scoring of the parent text document gets done from the relevancy scores of the children. That works. Just worried about speed of parent/child against millions of documents. Another way we could think of was, build our own scorer/analyzer. If we are reading in tokens like BarackObama.93345|AppleInc.0034 where it has the topic and the relevancy score to the document in it, i can build an analyzer to read those sorts of tokens, but is there any way to build a scorer that can use that token match data to score? and third, is there any other way to normalize this data into one document so we can score on it. That seems like it would be the fastest way to query, but my #2 option here is the only way I can think of doing it. Anyone else tagging their documents with relevancy scores to topics, on the document and then letting people search for those topics and pulling back the relevant docs based on the per document relevancy scores? Thanks, Scott -- You received this message because you are subscribed to the Google Groups "elasticsearch" group. To unsubscribe from this group and stop receiving emails from it, send an email to elasticsearch+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/elasticsearch/9434db79-363f-4470-bf91-b960908c2de6%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
