Large-scale collaborative knowledge processing method and system

Abstract

The invention discloses a large-scale collaborative knowledge processing method and a system, wherein the method comprises the following steps: receiving problem information and background information; receiving answer information and evidence information given by participants; receiving the addition and/or modification of the answer information, the evidence information, and/or the background information so as to form different versions of answer information, evidence information and background information; according to the score values of the different versions of answer information, evidence information and background information and the reliability values of scorers of the score values, calculating the reliability values of the different versions of answer information, evidence information and background information, and selecting optimal answer information; and updating the reliability values of participants providing the answer information. According to the method and system disclosed by the invention, the needs of a 'centering-on-problems' data modeling process can be satisfied better, the development mode of the modern information technology can be adapted better, and the rules of modern knowledge base construction can be conformed better.

Description

technical field [0001] The invention relates to the field of knowledge engineering, in particular to a knowledge processing method and system based on a data model oriented to large-scale collaborative knowledge processing. Background technique [0002] Knowledge processing is an important link in knowledge engineering, and collaborative knowledge processing is divided into "large-scale collaborative knowledge processing" and "small-scale collaborative knowledge processing". The difference between "large-scale collaborative knowledge processing" and "small-scale collaborative knowledge processing" lies in whether the scope of collaborative knowledge processing is carried out in an open environment and whether it extends to the long tail of the knowledge chain (The Long Tail). If the scope of collaborative knowledge processing is limited to a closed environment, or only limited to the head of the knowledge chain, then it is called "small-scale collaborative knowledge processi...

AI Technical Summary

Problems solved by technology

[0010] Although the above methods have their own advantages and disadvantages, and each has successful application cases in different situations, they cannot meet the special needs of large-scale collaborative knowledge processing: first, large-scale collaborative knowledge processing is "problem-oriented" Yes, the initiator proposes a "question" and provides background information such as relevant knowledge, original data, and constraints. All topics will be discussed in depth around the "problem", but the above methods are all "data"-oriented data model construction method

Secondly, large-scale collaborative knowledge processing emphasizes the complementarity of man and machine. The data model must not only target the computer subject but also the human subject. However, the above methods are all data model construction methods for the computer subject.

Again, large-scale collaborative knowledge processing emphasizes evolution and emergence, and requires a "data first, model later" approach, but the above methods are all "model first, data later" data models

Finally, large-scale collaborative knowledge processing emphasizes a "pay-as-you-go (pay-as-you-go)" design pattern, which will only be included in the knowledge base when users think it is necessary, but the above methods are all "pay-before-you-go" design pattern

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