tagging

问题 I'm currently designing a database schema that's used to store recipes. In this database there are different types of entities that I want to be able to tag (ingredients, recipe issuers, recipes, etc). So a tag has multiple n:m relations. If I use the "three table design", this would result in tables (cross table) for every entity type (recipes, ingredients, issuers) that I have. In other words every time I introduce an entity I have to add a cross table for it. I was thinking of creating one

问题 I would like to me able to set (and get) a custom metadata attribute for any file. What is the best way to do this? Thanks 回答1: The OpenMeta framework is a de-facto third-party standard for adding metadata to OS X files using extended attributes. It is used by a number of third-party applications. 回答2: Custom attribute names work for me: $ xattr -w com.apple.metadata:MyAttribute gfdylvyieo a.txt $ mdls -n MyAttribute a.txt MyAttribute = "gfdylvyieo" $ mdfind gfdylvyieo /private/tmp/a.txt $

问题 in vim, when i press ctrl-] 1) the usual behavior: it goes to the first matching tag with the word under the cursor, after that we need to do :tjump separately to see a list of all matching tags and then jump to the desired tag. 2) what i want is: vim should search the tag file, if there are multiple matches, show me a list of all the matching tags if there is one match, simply jump to the matching tag (just like :tjump does) this behavior(2) already happens when we use g-ctrl-] , but i want

问题 I have multiple texts and I would like to create profiles of them based on their usage of various parts of speech, like nouns and verbs. Basially, I need to count how many times each part of speech is used. I have tagged the text but am not sure how to go further: tokens = nltk.word_tokenize(text.lower()) text = nltk.Text(tokens) tags = nltk.pos_tag(text) How can I save the counts for each part of speech into a variable? 回答1: The pos_tag method gives you back a list of (token, tag) pairs:

问题 How do websites like Digg, Del.icio.us, and StackOverflow implement tagging? I know this other question has an accepted answer of a many-to-many relation with a cross ref table. But how do the "big boys" do it? The same way? How is it scaling? 回答1: Here is the oft-quoted article which breaks down tagging schemas by real performance metrics: http://tagging.pui.ch/post/37027746608/tagsystems-performance-tests The author notes that the founder of delicious explains using an RDBMS for tagging

问题 I have a photo website and i want to support tags as my original category bucketing is starting to fail (some pictures are family and vacations, or school and friends). Is there an agreed tagging db schema? I still want to support having photos as part of an album. Right now i have a few tables: Photos PhotoID PhotoAlbumID Caption Date Photo Album AlbumID AlbumName AlbumDate 回答1: There are various schemas which are effective, each with their own performance implications for the common queries

问题 I have a very large photo gallery with thousands of similar people, objects, locations, things. The majority of the people in the photos have their own user accounts and avatar photos to match. There are also logical short lists of people potentially in the photo based on additional data available for each photo. I allow users to tag photos with their friends and people they know but an automated process would be better. I've used photo tagger/finder from face.com integrating with Facebook

问题 Let's say I have the following tables: TAGS id: integer name: string POSTS id: integer body: text TAGGINGS id: integer tag_id: integer post_id: integer How would I go about writing a query that select all posts that are tagged with ALL of the following tags (name attribute of tags table): "Cheese", "Wine", "Paris", "Frace", "City", "Scenic", "Art" See also: Need help with sql query to find things with most specified tags (note: similar, but not a duplicate!) 回答1: Using IN: SELECT p.* FROM

问题 Chapter 5 of the Python NLTK book gives this example of tagging words in a sentence: >>> text = nltk.word_tokenize("And now for something completely different") >>> nltk.pos_tag(text) [('And', 'CC'), ('now', 'RB'), ('for', 'IN'), ('something', 'NN'), ('completely', 'RB'), ('different', 'JJ')] nltk.pos_tag calls the default tagger, which uses a full set of tags. Later in the chapter a simplified set of tags is introduced. How can I tag sentences with this simplified set of part-of-speech tags?

问题 I havent used lucene. Last time i ask (many months ago, maybe a year) people suggested lucene. If i shouldnt use lucene what should i use? As am example say there are items tagged like this apples carrots apples carrots apple banana if a user search apples i dont care if there is any preference from 1,2 and 4. However i seen many forums do this which i HATED is when a user search apple carrots 2 and 3 have high results while 1 is hard to find even though it matches my search more closely.