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A method to create multi-dimensional tag cloud to improve navigatability based on social data

IP.com Disclosure Number: IPCOM000180616D
Original Publication Date: 2009-Mar-13
Included in the Prior Art Database: 2009-Mar-13
Document File: 3 page(s) / 25K

Publishing Venue

IBM

Abstract

In this invention, a new method is created to improve the navigatability of tag cloud. The method scans the webpages, not only finding out the associated tags, but also the metadata and social data of the content. For example, metadata can be the created date, the author(s), etc. The social data can be the social profile of the author(s), the people who comments, the people who tagged it, the people who blog it, the social profile of those associated people, etc. These data are analyzed and divided into many categories, for example, location, age, date, geography, nation, race, company, university, department, etc. When creating a tag cloud, one or more of these categories are help to divide the big tag cloud into multiple small clouds, each associating with a certain value (or a value pair) of the related categories.

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-dimensional tag cloud to improve navigatability based on social data

dimensional tag cloud to improve navigatability based on social data

In this document, a new method is created to organize the tags to form a more meaningful and more navigatable tag cloud. It performs some mining on the social data of the tagged documents and the mining results are used to re-organize the tags to create a multi-dimensional tag cloud.

A tag is a keyword or term assigned to a piece of information. On website, a tag is a keyword given bythe user to a certain webpage. It helps the user to clarify the page and helps them to quickly navigate back to the pagein future. Tag cloud is a widely used visualization method to display the navigatable tags. Usually tags are organized in ONE cloud. The only difference between tags in a tag cloud is the color and the size of the tag, which only illustrate the number of occurrence of the tag.

No other

A method to create multi -

information is provided in the tag cloud. So the common tag cloud can be thought as 1-dimensional tag cloud. Such tag cloud does not provide much help for webpage navigation.

In this document, a new method is created to improve the navigatability of tag cloud. The method scans the webpages, not only finding out the associated tags, but also the metadata and social data of the content. For example, metadata can be the created date, the author(s), etc. The social data can be the social profileof the author(s), the people who comments, the people who tagged it, the people who blog it, the social profile of those associated people, etc. These data are analyzed and divided into many categories, for example, location, age, date, geography, nation, race, company, university, department, etc. When creating a tag cloud, one or more of these categories are help to divide the big tag cloud into multiple small clouds, each associating with a certain value (ora value pair) of the related categories.

Here is an example:
There are hundreds of thousands of bookmarks in Dogear, an IBM enterprise social bookmarking service. Dogear organizes all tags in a single tag cloud. When you click on one tag (say "connections") in the tag cloud, usually thousands of bookmarks are returned and flooded in your browser. Most of the returned bookmarks do not make sense to you. With this invention, the biggest tag cloud is separated into several tag clouds, each representing a set of tags from a certain department and a certain nation, e.g., a cloud for tags from China development department, a cloud for tags from US business department, a cloud for tags from India HR department, etc. You can also change the way the tag clouds are organized at runtime. If the current category does not meet your expectation,

you can choose to use other categories to separate the

original tag cloud. For example,

you can choose to use brands to re-organize

                                             the tags. Then the tags will group by brands, e...