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Information browsing and retrieval method based on semantic entity-relationship model and visualized recommendation

A technology of entity relationship and information browsing, applied in the service field

Active Publication Date: 2010-05-12
SUZHOU ANGERAY ELECTRONICS TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The current search engine technology cannot realize these three functions, so it is difficult to obtain good results in these fields

Method used

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  • Information browsing and retrieval method based on semantic entity-relationship model and visualized recommendation
  • Information browsing and retrieval method based on semantic entity-relationship model and visualized recommendation
  • Information browsing and retrieval method based on semantic entity-relationship model and visualized recommendation

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Experimental program
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Embodiment

[0026] Such as figure 1 As shown, it is a flow chart of an information browsing and retrieval method based on the semantic entity relationship model and visual recommendation provided by the present invention, the steps of which are as follows:

[0027] Step 1. Regularly collect data from the Internet or private databases. The data that the user is interested in may come from the Internet or a private database, or a combination of the two, so first of all, new data is regularly obtained from the Internet or a private database through an automatic data collection device.

[0028] Step 2, extracting semantic entities and relations from the document data, audio data or visual data obtained in step 1, so as to convert the data into a form represented by semantic entities and relations. This step can be divided into the extraction of semantic entities and the extraction of relations.

[0029] For the extraction of semantic entities:

[0030] A semantic entity is defined as any e...

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Abstract

The invention provides an information browsing and retrieval method based on a semantic entity-relationship model and visualized recommendation, comprising the following steps: first collecting data from the internet at regular time, then extracting the semantic entity and relationship, converting the obtained data into the original semantic entity-relationship model Dr and adding the original semantic entity-relationship model Dr into a historical database after time delay, generating a user knowledge model KU presenting the known knowledge of the user after the data in the historical database and a learning / forgetting curve of the user are subjected to convolution and using the user knowledge model KU to predict the data in the original semantic entity-relationship model Dr. The method has the following advantages: 1. the users can check the information which the users are interested in; 2. relatively reasonable recommendation can be obtained without any input; 3. both the written information and the multimedia information such as videos, images and the like can be inquired, and cross-media inquiry is also available; and 4. the unstructured information can be checked intuitively.

Description

technical field [0001] The invention relates to a novel massive information browsing and retrieval technology based on a semantic entity relationship model and visual recommendation, which is used to realize services such as browsing and retrieval of massive unstructured information. Background technique [0002] There is a wealth of information hidden in massive unstructured data (eg: Internet). This information can provide valuable intelligence to the owner of the data in a number of ways. For example, national security departments can analyze their true attitudes towards my country from news reports from other countries, and companies can detect abnormal transactions from their own operating data to prevent losses from expanding, and so on. However, this information is deeply hidden in a large amount of data. To obtain this information, users must browse a large amount of data and dig out the parts they are interested in. Because the amount of data is so large, it is i...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06F17/27
Inventor 罗迒哉范建平
Owner SUZHOU ANGERAY ELECTRONICS TECH
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