Browse Prior Art Database

Dynamically-growing sub-network-structured interaction-driven knowledge base in a social network

IP.com Disclosure Number: IPCOM000214916D
Publication Date: 2012-Feb-13
Document File: 4 page(s) / 52K

Publishing Venue

The IP.com Prior Art Database

Abstract

Described is a dynamically-growing sub-network-structured interaction-driven knowledge base in a social network.

This text was extracted from a PDF file.
This is the abbreviated version, containing approximately 28% of the total text.

Page 01 of 4

Dynamically- social network

A dynamically growing knowledge base model based on the interaction pattern in a social network is presented here. The knowledge base can be dynamically created when a sub-social network is detected or established. The interaction within the sub-network becomes knowledge that is stored in the knowledge base. This knowledge is dynamically improved and changed as more and more interactions happen. The knowledge bases from different sub-networks will coalesce or merge with each other as the interactions between sub-networks happening. Furthermore, as the interactions among the same sub-network differ and categorize into more specific directions, the knowledge base can split into smaller knowledge bases to capture the specific characteristics of the interactions.

    WikiPedia* defines social network as a social structure made up of individuals (or organizations) called "nodes", which are tied (connected) by one or more specific types of interdependency, such as friendship, kinship, common interest, financial exchange, dislike, or relationships of beliefs, knowledge, or prestige. [*]. By this definition, most people belong to a set of social networks. A person can belong to multiple work-related networks, a college alumni network, a church network, to name a few.

    Another characteristic of this social network is the sub-network. A big social network could consist of a set of sub-networks, with sub-networks connected to each other. Sub-networks could consist of sub-networks. For example, the world-wide WebSphere** eXtreme Scale user community could consist of WebSphere eXtreme Scale user groups in each city. The most granular level, could be a three-person team in Bank ABC that tries to deploy WebSphere eXtreme Scale in their latest on-line application.

    Within the same network, members normally work on similar tasks with the same attributes and purposes. Take a product team as an example. In this team, most members leverage the same development tools/environment, require to install the same product, and run the same tests. Another example is a user community, for example, the WebSphere eXtreme Scale user community. In this community, most of the members install eXtreme Scale, run the same default samples, program with the APIs, run tests, check the same message output logs, etc. The members in the same network require the similar knowledge to perform their tasks.

    Nodes in a social network can provide useful information to other nodes in different ways, such as collaboration, knowledge sharing, etc. This is not new. On-line help center and central knowledge base are two of the examples. By registering to an on-line help center, a user establishes a link to the on-line help center. Implicitly, it makes the user linked to other help centers, so he/she can read the Q&A from other users, and/or get help from the other users. Central knowledge base is similar. By subscribing the central knowledge base, a user particip...