Research And Implement Of Web-based CommonSense Knowledge Share Platform

In the international artificial intelligence community, the disposal of common sense knowledge has been recognized as a core problem of artificial intelligence. The so-called common sense is as opposed to professional knowledge. The professional knowledge has been applied to a wide range of expert systems and application software. There is a great deal of professional knowledge which human accumulated. But compared to common sense, professional knowledge is only a trickle. The feature of professional knowledge is very obvious, but the actual requirements will not only solve the problem of the large scope of professional fields, rather, it has wider significance of the specific areas of life. However, over long periods of time, because of the lack of an effective common sense knowledge base support, the use of large-scale application is very limited.This article will explore possible ways to solve this problem, it is based on the identification of an observation——every ordinary person has common sense of the kind we want to give our machines. The advent of the web has made it possible for the different time for thousands or even millions of people to collaborate to construct systems that no single individual or team could build. In the text,the knowledge share platform with Web-based B/S applications model, attract the general public to participate using Internet. Knowledge acquisition could support the distributed construction of a commonsense database by many people in their free time.The full text is divided into seven parts. Part I: introduce the background of common sense acquisition and the status of study in domestic/foreign, put forward main content of the paper and its significance. Part II: introduce the basic theories of common sense acquisition and Web-based B/S application model; describe the composition and structure of the platform. Part III: introduce the detailed design of common sense input module——two input mode (direct input, template input) which are used on the Chinese word segmentation, speech tagging and keyword extraction technology related to a briefing. Part IV: introduce the detailed design of feedback out module——two feedback output (correlation feedback, inference feedback); describe the sentences filtration and three inference methods. Part V: introduce the platform and tools used in the development environment for the operation of structures. Part VI: introduce evaluating the accumulated database and platform system testing. Part VII: sum-up the work done and expect the further goals.