Big data-based flexible employment business knowledge inference engine construction method

A technology of knowledge reasoning and big data, applied in the field of knowledge reasoning engine construction for flexible employment based on big data, can solve problems such as the inability to objectively and scientifically verify the authenticity of business, and achieve the effect of improving authenticity and accuracy

Inactive Publication Date: 2021-12-10
好活(贵州)网络科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present invention intends to provide a method for building a flexible employment business knowledge reasoning engine based on big data, so as to solve the problem that the authenticity of the business cannot be objectively and scientifically verified from the flexible employment data

Method used

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  • Big data-based flexible employment business knowledge inference engine construction method
  • Big data-based flexible employment business knowledge inference engine construction method

Examples

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Effect test

Embodiment 1

[0029] The construction method of flexible employment business knowledge reasoning engine based on big data, such as figure 1 and figure 2 shown, including the following steps:

[0030] Step 1. Receive employment data from multiple employment platforms. The employment data can be flexible employment data, such as the subject, willingness, contract, business, funds, and bills of the courier rider, and identify named entities in the employment data. Extract the association relationship in the employment data after named entity recognition, extract the attribute information of the named entity in the employment data, specifically, the employment data includes structured data, semi-structured data and unstructured data, and directly identify and name from the structured data Entities, through semantic recognition or dependency syntax analysis, identify named entities, extract association relationships, and extract attribute information from semi-structured and unstructured data....

Embodiment 2

[0070] The difference with Embodiment 1 is that in the step 1, after the named entity is identified, it is first judged whether the target type of the named entity is a name, and the target type of the named entity is judged through existing semantic analysis. When the target type is Name, count the named entities in the same employment data to get the count value, compare the count value with the threshold value, when the count value is equal to the threshold value, determine that the named entity is a place name or name through semantic analysis, when the count value is greater than the threshold value, First judge the location information of the named entity. The location information is judged by the structure of the sentence, such as subject, predicate, object, attributive and adverbial, etc., and then identify the semantics of the adjacent elements of the named entity and judge that the named entity is a place name based on the semantics of the adjacent elements. or name. ...

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PUM

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Abstract

The invention relates to the field of flexible employment data processing methods, in particular to a big data-based flexible employment business knowledge inference engine construction method, which comprises the following steps of: receiving employment data of a plurality of employment platforms, identifying named entities in the employment data, extracting an association relationship in the employment data after the named entities are identified, and extracting attribute information of named entities in the employment data; performing entity linking after recognition, and performing knowledge merging processing on employment data; performing ontology establishment operation according to the named entities and the association relationship to obtain a knowledge graph; reasoning the incidence relation lacked in the knowledge graph according to a preset rule to obtain reasoning knowledge, adding a reasoning identifier to the reasoning knowledge, updating the preset rule, performing reasoning according to the updated preset rule, judging whether the reasoning knowledge conflicts with the preset rule or not, if yes, deleting the reasoning knowledge, and if not, performing reasoning knowledge storing. According to the invention, the business authenticity according to employment data is improved.

Description

technical field [0001] The invention relates to the field of flexible employment data processing methods, in particular to a method for building a flexible employment business knowledge reasoning engine based on big data. Background technique [0002] Flexible employment is to provide professional and flexible employment services for enterprises to effectively cope with the challenges of seasonal, temporary and cyclical development and change of enterprises. From the perspective of the market, enterprise employment includes various forms such as labor relations, part-time employment, labor dispatch, retirement reemployment, internships, job outsourcing, crowdsourcing, business outsourcing, human resource service outsourcing, partnership, self-employment, and platform employment. That is to say, in addition to labor relations, other employment relations can be collectively referred to as the category of flexible employment. Due to the high mobility and large number of flexib...

Claims

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Application Information

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IPC IPC(8): G06F16/2455G06F16/33G06F16/332G06F16/36G06F40/211G06F40/295G06F40/30
CPCG06F16/24564G06F16/367G06F16/3329G06F16/3332G06F40/295G06F40/30G06F40/211
Inventor 陈凤杰
Owner 好活(贵州)网络科技有限公司
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