如何在C#Windows Appliction中使用Wonder Wheel [英] how to use wonder Wheel in C# Windows Appliction

问题描述

google wonder wheel是GOOGLE有趣的功能之一.我想在使用C#的Windwos Applicatin中使用它.我可以在我的应用程序中使用它吗.

google wonder wheel was one of the nice interesting feature of GOOGLE. I want to use it in my Windwos Applicatin using C#. Can I use it in my Application.

推荐答案

DevComponents

[ ^ ]与此类似.

考虑到Wonder Wheel被淘汰了两年，你不能.

最终，它被内容相关广告定位工具 [

Considering Wonder Wheel was taken down two years go, you can''t.

It was eventually replaced by the Contextual Targeting Tool[^].

已故的Google WonderWheel功能以及其他互动功能诸如VirtualThesaurus [ ^ ]之类的工具是表示语义"的图形用户界面的实现.关系."

这些界面中的许多界面都使用字体大小和颜色的变化，以二维模拟三个维度之间的关系.

概念化语义关系"的一种简单方法是，假设在给定特定上下文的情况下，特定单词的含义位于可能的关联单词的庞大网络的中心".这意味着对于每个单词都有一组关联，并且在所有关联的集合中，每个其他单词都有一定的关联概率，即关联的强度".

您可能会发现此CodeProject文章很有用:[ ^ ].

因此，如果我想到蝴蝶"，则很可能将飞蛾"，毛毛虫"，昆虫"这两个词联系得非常紧密，而诸如移民"，"pu， "imago"和"Vladimir Nabokov"可能具有不同程度的关联强度.

如果要实现这样的功能，则至少需要一个键(术语)数据库，该数据库每个键都具有与所有其他键的关联度. >
想象一下，您在语义关系"数据结构中有256个单词(键，术语):每个键都将相互引用一个键，因此:

1.您有256个关键对象.

2.每个键都包含255个对其他键的引用，并带有关联度"因子

3.假设关键对象是字符串，则每个引用的代码为一个字节，并且关联因子存储在两个字节中

那么您的总大小为:

256个键，具有可变长度的字符串和一个1字节的ID码
每个键255个条目，包括:一个字节的ID码和两个字节的关联因子

总大小为:

(所有关键字的长度总和1〜256)+(256 * 255)+(3 *(256 * 255))

(所有关键字的长度总和为1〜256)+ 65280 + 195840

如果我们假设关键字的平均长度为7，那么您将

1792 + 65280 + 195840 = 262912字节

因此，我认为您可以看到为语义关系UI创建数据库不是一件容易的事.虽然300kb以下的文件似乎很小，但如今，有人必须从事输入/创建关联值的工作:具有深厚的语言能力的人.

而且，如果您考虑在搜索中添加深度"，即:如果我单击蝴蝶"，然后单击"imago"，则您要考虑两个术语在语义上的关联度，则得到指数都需要增加数据项的数量.

但是，没有什么能阻止您为自己或最终用户创建自己的设施，以直接行动来创建自己的语义关系数据库...在那里吗?

实际上，如果我要再次教书，我会认为这对于第二级编程课程的学生来说是一项很好的任务.

祝你好运，Bill

The late Google WonderWheel feature, and other interactive tools like the VirtualThesaurus[^], are implementations of graphic user interfaces which express "semantic relations."

Many of these interfaces use variation in font size, and color, to simulate in two dimensions a relationship in three dimensions.

A simple way of conceptualizing "semantic relations" is to imagine that the meaning of a particular word is at the "center of a vast web" of possible associated words given a certain context. That implies that for every word there is a set of associations, and within the set of all associations, each other word has some probability of association, or "strength" of association.

You may find this CodeProject article useful: [^].

So, if I think of "butterfly," it''s highly likely that the words "moth," "caterpillar," "insect" may be very strongly associated, while words, or phrases, like "migration," "pupa," "imago," and "Vladimir Nabokov" may have varying degrees of strength of association.

If you want to implement a feature like this, you are going to need ... at minimum ... a database of keys (terms), which has, for every key, its degree of association with all other keys.

Imagine you had 256 words (keys, terms) in your "semantic relations" data structure: each key would have a reference to each other key so:

1. you have 256 key objects.

2. each key contains 255 references to the other keys with a "degree of association" factor

3. assuming the key objects were strings, the code for each reference is one byte, and the association factor was stored in two bytes

Then you''d have a total size of:

256 keys with a variable length string and a one-byte id code
255 entries per key consisting of: one byte id code, and two-byte association factor

The total size would be:

(Sum of lengths of all keywords 1~256) + (256 * 255) + (3 * (256 * 255))

(Sum of lengths of all keywords 1~256) + 65280 + 195840

If we assume an average keyword length of seven, then you''d have

1792 + 65280 + 195840 = 262912 bytes

So, I think you can see that creating a database for a semantic relation UI is no easy task; while under 300kb may seem like a small size, these days, somebody has to do the work of entering/creating the association values: someone(s) with deep linguistic skills.

And, if you think about adding "depth" to your search, that is: if I click on "butterfly," and then "imago," you want to consider the degree of semantic relatedness of both terms, then you get an exponential increase in the number of data items you need for every additional level of relationship you deal with.

But, there''s nothing stopping you from creating your own facility for yourself, or end-users, to create their own semantic relations database by direct action ... is there ?

In fact, if I were to teach again, I''d consider that as a very good assignment for students in a second-level course on programming.

good luck, bill

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