Associated search method and system of chemical knowledge

Abstract

The invention provides an associated search method and system of chemical knowledge. The method takes a chemical resource bundle file (8) as the base and comprises the following steps: receiving the keywords to be searched which are input by a user; searching the keywords and obtaining an index address of a chemical substance matched with the keywords; obtaining the chemical substance content according to the index address of the chemical substance and displaying the chemical substance content such as the name of the chemical substance on a result list; taking the chemical substance on the result list as a center knowledge object; obtaining reactants associated with the chemical substance, resultants associated with the chemical substance or other reactants capable of reacting with the chemical substance, and then taking the reactants, the resultants or the other reactants as divergent knowledge objects; constructing a dynamic relation network chart composed of the center knowledge object and the divergent knowledge objects and displaying the knowledge content relative to the objects in the dynamic relation network chart. According to the associated search method and system of the chemical knowledge, the semantic associated search is supported, and the chemical substances and associated knowledge points which the user wants to learn can also be searched so that the study efficiency can be obviously improved.

Description

technical field [0001] The invention relates to a search engine and a search method, in particular to a chemical knowledge correlation search method and system. The chemical knowledge correlation search system of the present invention can be applied to electronic learning equipment, such as learning machines, electronic dictionaries, early education machines, learning pen etc. Background technique [0002] E-learning equipment is often used for the study of chemical knowledge, such as the inquiry and study of chemical substances. Traditional handheld electronic learning devices generally store chemical resource package files, chemical laboratory modules and chemical search engine modules. There are three main ways to learn chemical knowledge. The first is a catalog-based text browsing learning method, the second is a One is the virtual chemistry laboratory learning method, and the third is the keyword search learning method. [0003] Catalog-style text browsing learning me...

AI Technical Summary

Problems solved by technology

[0003] Catalog-style text browsing learning method: classify the knowledge points in the chemical search library according to categories or students’ grade textbooks, etc., and set up directory links that can be expanded level by level (by knowledge point categories or different grade textbooks). When learning, Users click on the level-by-level directories to find the knowledge points they want to learn step by step; the disadvantage of this method is that the directories can only be expanded level by level. When there are many directories, the operation is too cumbersome and it is not easy to find The knowledge points you want to learn; the virtual chemistry laboratory learning method only conducts simulation experiments for experimental examples, and cannot expand more chemical knowledge learning; the keyword search learning method introduces a search engine, when the user enters key words such as chemical substances After the word, the system puts the keyword in the chemical resource package file for matching. If it matches the keyword or its related knowledge point, it will be displayed for the user to learn. This method solves the problem of catalog text browsing. The defect of expanding knowledge points at a certain level improves the search efficiency to a certain extent, but it is mainly for test questions and text-based learning of relevant knowledge points corresponding to test questions

[0004] Integrating the above-mentioned learning methods of the prior art, there are still two major defects as follows: First, there is no correlation between the knowledge points that constitute the chemical resource package file. After the user inputs the chemical substance to be searched, only the chemical substance containing the chemical substance is displayed. The test questions, for users to answer questions, view the answers to the test questions and the analysis of the test questions, and view the relevant content of the knowledge points involved in the current test question, so the search results are relatively simple, and the user can only learn the corresponding properties of the chemical substance currently searched for each search, and cannot roam to For other chemical substances in the chemical search library, if you want to search and learn the next chemical substance, you need to exit the learning interface of the chemical substance step by step and enter the search main interface again to re-enter the search keyword, which is very inconvenient to use. Therefore, it is not convenient for the user to operate and master more content, and it is impossible for the user to quickly learn and master more related divergent knowledge content. For example, when the user enters "H 2 When searching for the keyword "O", the existing technology can only search for the chemical substance "H" that directly matches the keyword 2 The resource corresponding to O" is fed back to the user. If the user wants to learn, he can use "H" 2 O" chemically reacted "CO 2 " or " CaCO 3 " and other related chemical substances, you have to re-enter the associated keywords to search, which reduces the learning efficiency; second, it is impossible to search for semantic associations, that is, when the user enters different names representing the same chemical substance, it will appear that it cannot be found. In the case of this chemical substance, for example, when the user enters different names of "NaOH" such as sodium hydroxide, caustic soda, caustic soda, and caustic soda, the chemical substance "NaOH" may not always be found, and there is a lack of intelligence and humanization. defect

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