CN114519131B - Knowledge fusion processing method and device for heterogeneous resources - Google Patents
Knowledge fusion processing method and device for heterogeneous resources Download PDFInfo
- Publication number
- CN114519131B CN114519131B CN202111641087.7A CN202111641087A CN114519131B CN 114519131 B CN114519131 B CN 114519131B CN 202111641087 A CN202111641087 A CN 202111641087A CN 114519131 B CN114519131 B CN 114519131B
- Authority
- CN
- China
- Prior art keywords
- resource
- data
- module
- independent
- package
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/40—Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
- G06F16/43—Querying
- G06F16/432—Query formulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/40—Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
- G06F16/43—Querying
- G06F16/438—Presentation of query results
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/40—Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
- G06F16/45—Clustering; Classification
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Quality & Reliability (AREA)
- Mathematical Physics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The application discloses a knowledge fusion processing method and device for heterogeneous resources, wherein the method comprises the following steps: capturing resource data to be processed from a public network channel; processing the resource data to be processed into at least one independent resource package after classification; extracting knowledge points from the at least one independent resource package; classifying and packaging according to the extracted knowledge points to form at least one independent knowledge point file package; and fusing the resource data, the knowledge points, the at least one independent resource package and the at least one independent knowledge point file package, wherein the fused data is used as basic data for retrieval. The method and the device solve the problem that the prior art is single in various resource processing capacity and therefore does not provide good basic data for retrieval, thereby providing basic data for retrieval and improving the retrieval experience of users.
Description
Technical Field
The application relates to the field of data processing, in particular to a knowledge fusion processing method and device for heterogeneous resources.
Background
Along with the continuous promotion of the urban process, various resources are continuously emerging, and resource fusion and interaction management become more important as important steps of information processing, but the conventional resource fusion and interaction management system is difficult to carry out knowledge fusion on massive heterogeneous resources, and the information processing capability of the system is poor, so that the later-stage data summarization, processing and evaluation are influenced, the use experience of users is influenced, and the practicability of the system is reduced.
Disclosure of Invention
The embodiment of the application provides a knowledge fusion processing method and device for heterogeneous resources, which at least solve the problem that the retrieval is not provided with good basic data due to single processing capacity of various resources in the prior art.
According to one aspect of the present application, there is provided a knowledge fusion processing method for heterogeneous resources, including: capturing resource data to be processed from the disclosed network channel, wherein the resource data comprises at least one of the following: pictures, documents, and data; processing the resource data to be processed into at least one independent resource packet after classification, wherein each independent resource packet in the at least one independent resource packet is different in classification; extracting knowledge points from the at least one independent resource package; classifying and packaging according to the extracted knowledge points to form at least one independent knowledge point file package; and fusing the resource data, the knowledge points, the at least one independent resource package and the at least one independent knowledge point file package, wherein the fused data is used as basic data for retrieval.
Further, processing the resource data to be processed into at least one classified independent resource package includes: classifying the resource data to be processed to obtain at least one file package; and classifying the at least one file package to obtain the at least one independent resource package, wherein each resource package comprises at least one file package.
Further, fusing at least the resource data, the knowledge points, the at least one independent resource package, and the at least one independent knowledge point file package includes: and storing the at least one file package, the resource data, the knowledge points, the at least one independent resource package and the at least one independent knowledge point file package.
Further, the classification related in the method is to classify the first content according to the characteristics and/or elements of the first content to be classified to obtain second content, wherein the first content is the resource data to be processed, and the second content is at least one file package; and/or the first content is the at least one file package, and the second content is the at least one independent resource package; and/or the first content is a knowledge point, and the second content is at least one independent knowledge point packet; wherein the features and/or elements are the basis of classification.
Further, the first content to be classified is set in a cache by the classification related to the method, and the second content after the classification is obtained by classifying the content to be classified in the cache.
Further, processing the resource data to be processed into at least one classified independent resource package includes: processing the resource data to be processed, wherein the processing comprises at least one of the following: deleting repeated data, deleting invalid data and deleting outdated data; and classifying the processed resource data to obtain the at least one independent resource package.
Further, the method further comprises the following steps: receiving a query request of a user, wherein the query request corresponds to a query statement; analyzing the query statement to obtain the query intention of the user, and obtaining the topic category of the user query according to the query intention, wherein the user query intention and the topic category of the user query are obtained according to the content stored in a historical log record, and the historical query statement used by the user and the evaluation of the retrieval result obtained by the user on the historical query statement are stored in the log record; searching in basic data used as retrieval according to the topic category to obtain a search result; and returning the search result to the user.
According to another aspect of the present application, there is also provided a knowledge fusion processing apparatus for heterogeneous resources, including: the system comprises a grabbing module, a storage module and a processing module, wherein the grabbing module is used for grabbing resource data to be processed from a public network channel, and the resource data comprises at least one of the following: pictures, documents, and data; the first classification module is used for processing the resource data to be processed into at least one independent resource packet after classification, wherein each independent resource packet in the at least one independent resource packet is different in classification; an extraction module for extracting knowledge points from the at least one independent resource package; the second classification module is used for classifying according to the extracted knowledge points and packaging to form at least one independent knowledge point file package; and the fusion module is used for fusing the resource data, the knowledge points, the at least one independent resource package and the at least one independent knowledge point file package, wherein the fused data is used as basic data for retrieval.
Further, the first classification module is configured to: classifying the resource data to be processed to obtain at least one file package; and classifying the at least one file package to obtain the at least one independent resource package, wherein each resource package comprises at least one file package.
Further, the fusion module is configured to: and storing the at least one file package, the resource data, the knowledge points, the at least one independent resource package and the at least one independent knowledge point file package.
In an embodiment of the present application, capturing resource data to be processed from a public network channel is adopted, where the resource data includes at least one of the following: pictures, documents, and data; processing the resource data to be processed into at least one independent resource packet after classification, wherein each independent resource packet in the at least one independent resource packet is different in classification; extracting knowledge points from the at least one independent resource package; classifying and packaging according to the extracted knowledge points to form at least one independent knowledge point file package; and fusing the resource data, the knowledge points, the at least one independent resource package and the at least one independent knowledge point file package, wherein the fused data is used as basic data for retrieval. The method and the device solve the problem that the prior art is single in various resource processing capacity and therefore does not provide good basic data for retrieval, thereby providing basic data for retrieval and improving the retrieval experience of users.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, illustrate and explain the application and are not to be construed as limiting the application. In the drawings:
FIG. 1 is a platform framework diagram according to an embodiment of the present application
Fig. 2 is a system flow diagram according to an embodiment of the present application.
Fig. 3 is a flowchart of a fusion interaction processing method for multi-source heterogeneous resources according to an embodiment of the present application.
In the figure: 1. a resource acquisition module; 2. a processing module; 3. a knowledge organization module; 4. a transmission calling module; 5. a knowledge service module; 201. a processing receiving module; 202. a temporary storage module; 203. a pruning and sorting module; 204. a feature analysis module; 205. an element extraction module; 206. a resource processing module; 207. a knowledge extraction module; 208. a knowledge definition module; 209. a composite processing module; 210. a new concept prediction module; 211. a knowledge prediction module; 301. a resource store storage module; 302. extracting a calling module; 303. a fusion analysis module; 304. a fusion processing module; 305. a fusion calling module; 306. a general management module; 307. an analysis definition module; 308. an organization receiving module; 309. a background input module; 310. an alternation and adjustment module; 311. adjusting a calling module; 501. a retrieval analysis module; 502. an intelligent question-answering module; 3011. an original resource module; 3012. an independent resource module; 3013. a knowledge resource module; 3014. combining resource modules; 3015. a predictive knowledge module; 5011. retrieving an input module; 5012. an analysis processing module; 5013. an audit calling module; 5014. an analog push module; 5015. a main body receiving module; 5016. an comparison analysis module; 5017. an auxiliary evaluation module; 5018. a joint output module; 5021. a subject input module; 5022. a knowledge point extraction module; 5023. a resource request module; 5024. a solution analysis module; 5025. and a solution output module.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
The method in this embodiment may be applied to a system as shown in fig. 1, where a knowledge fusion and intelligent interaction management platform for massive multi-source heterogeneous resources is studied, and the system includes a resource acquisition module 1, a processing module 2, a knowledge organization module 3, a transmission calling module 4 and a knowledge service module 5, where an output end of the resource acquisition module 1 is controlled to connect with an input end of the processing module 2, an output end of the processing module 2 is controlled to connect with an input end of the knowledge organization module 3, an output end of the knowledge organization module 3 is controlled to connect with an input end of the transmission calling module 4, an input end of the transmission calling module 4 is controlled to connect with an output end of the knowledge service module 5, an output end of the transmission calling module 4 is controlled to connect with an input end of the knowledge service module 5 and an input end of the knowledge organization module 3, and an output end of the knowledge organization module 3 is controlled to connect with an input end of the processing module 2.
The processing module 2 is composed of a processing receiving module 201, a temporary storage module 202, a pruning module 203, a feature analysis module 204, an element extraction module 205, a resource processing module 206, a knowledge extraction module 207, a knowledge definition module 208, a composite processing module 209, a new concept prediction module 210, and a knowledge prediction module 211.
The input end of the processing receiving module 201 is respectively connected with the input ends of the temporary storage module 202 and the pruning and sorting module 203 in a control manner, the output end of the pruning and sorting module 203 is connected with the input end of the feature analysis module 204, the arranged pruning and sorting module 203 is used for facilitating the pruning and sorting of the data transmitted by the processing receiving module 201, the repetition and invalidation parts are eliminated, so that the analysis speed of the feature analysis module 204 for the data feature analysis is improved, the output end of the feature analysis module 204 is connected with the input end of the element extraction module 205, the output end of the element extraction module 205 is connected with the input end of the resource processing module 206, the output end of the resource processing module 206 is respectively connected with the input end of the knowledge extraction module 207 and the input end of the temporary storage module 202, the input end of the temporary storage module 202 is connected with the input end of the knowledge prediction module 211, the input end of the knowledge prediction module 211 is connected with the output end of the new concept prediction module 210, the input end of the new concept prediction module 210 is controlled and connected with the output end of the knowledge extraction module 207, the output end of the knowledge definition module 207 is controlled with the input end of the knowledge definition module 208, and the input end of the composite prediction module 209 is arranged for the processing system is improved.
The knowledge organization module 3 is composed of a repository storage module 301, an extraction and call module 302, a fusion analysis module 303, a fusion processing module 304, a fusion and call module 305, a general management module 306, an analysis and definition module 307, an organization receiving module 308, a background input module 309, an alternation and adjustment module 310 and an adjustment and call module 311.
The input end of the resource library storage module 301 is in control connection with the output end of the adjustment calling module 311, the input end of the adjustment calling module 311 is respectively in control connection with the output ends of the resource library storage module 301 and the adjustment module 310, the arranged adjustment module 310 is used for adjusting timeliness of the interior stored in the resource library storage module 301, the effectiveness of storage materials in the resource library storage module 301 is ensured, the input end of the adjustment module 310 is in control connection with the output end of the background input module 309, the output end of the background input module 309 is in control connection with the input end of the organization receiving module 308, the output end of the organization receiving module 308 is in control connection with the input end of the analysis defining module 307, the output end of the analysis defining module 307 is in control connection with the input end of the universal management module 306, the output end of the universal management module 306 is in control connection with the input end of the resource library storage module 301, the output end of the resource library storage module 301 is respectively in control connection with the input end of the fusion calling module 305, the output end of the fusion analysis module 303 is in control connection with the input end of the fusion processing module 304, the output end of the fusion processing module 304 is not in control connection with the storage module 302 of the resource library storage system, and the efficiency of the storage system is not established by the input end of the storage module 301 is improved.
The resource library storage module 301 is composed of an original resource module 3011, an independent resource module 3012, a knowledge resource module 3013, a combined resource module 3014 and a predicted knowledge module 3015, and is beneficial to storing the fusion resources processed by the fusion processing module 304 through the set combined resource module 3014.
The knowledge service module 5 is composed of a search analysis module 501 and an intelligent question and answer module 502, and the intelligent question and answer module 502 is composed of a topic input module 5021, a knowledge point extraction module 5022, a resource request module 5023, a answer analysis module 5024 and an answer output module 5025.
The output end of the subject input module 5021 is in control connection with the input end of the knowledge point extraction module 5022, the output end of the knowledge point extraction module 5022 is in control connection with the input end of the resource request module 5023, the output end of the resource request module 5023 is in control connection with the input end of the solution analysis module 5024, the output end of the solution analysis module 5024 is in control connection with the input end of the solution output module 5025, the set resource request module 5023 is utilized to receive the knowledge point extracted by the knowledge point extraction module 5022, the retrieval analysis module 501 is favorable for sending a call application of data to the resource inventory module 301 according to the extracted knowledge point, the retrieval analysis module 501 is composed of the retrieval input module 5011, the analysis processing module 5012, the audit calling module 5013, the comparison pushing module 5014, the main body receiving module 5015, the comparison analysis module 5016, the auxiliary evaluation module 5017 and the joint output module 5018, the output end of the comparison module 5016 is connected with the input end of the analysis module 5012, the output end of the comparison module 5016 is connected with the input end of the main body module 5018, and the output end of the comparison module 5016 is connected with the auxiliary module 5018.
Based on the above system, in this embodiment, a knowledge fusion processing method for heterogeneous resources is provided, fig. 3 is a flowchart of a fusion interaction processing method for multi-source heterogeneous resources according to an embodiment of the present application, and the steps of the method are described below with reference to fig. 3.
Step S302, capturing resource data to be processed from a public network channel, wherein the resource data comprises at least one of the following: pictures, documents, and data.
Step S304, processing the resource data to be processed into at least one classified independent resource packet, where each independent resource packet in the at least one independent resource packet is classified differently.
In this step, at least one package (may also be referred to as a data package) is obtained after classifying the resource data to be processed; and classifying the at least one file package to obtain the at least one independent resource package, wherein each resource package comprises at least one file package. After the package is obtained, the package may be stored as basic data for retrieval.
Preferably, the data may also be processed accordingly, for example, the resource data to be processed is processed, wherein the processing includes at least one of the following: deleting repeated data, deleting invalid data and deleting outdated data; and classifying the processed resource data to obtain the at least one independent resource package.
Step S306, extracting knowledge points from the at least one independent resource package.
Step S308, classifying and packaging according to the extracted knowledge points to form at least one independent knowledge point file package.
And step S310, fusing the resource data, the knowledge points, the at least one independent resource package and the at least one independent knowledge point file package, wherein the fused data is used as basic data for retrieval.
The method solves the problem that the prior art is not provided with good basic data for retrieval due to single processing capacity of various resources, thereby providing basic data for retrieval and improving retrieval experience of users.
Optionally, the classification related to the method includes classifying the first content according to the features and/or elements of the first content to be classified to obtain second content, where the first content is the resource data to be processed, and the second content is at least one file package; and/or the first content is the at least one file package, and the second content is the at least one independent resource package; and/or the first content is a knowledge point, and the second content is at least one independent knowledge point packet; wherein the features and/or elements are the basis of classification.
Optionally, the first content to be classified is set in a cache by the classification related to the method, and the second content after the classification is obtained by classifying the content to be classified in the cache.
In obtaining basic data for retrieval, the present embodiment also provides a retrieval method, which includes: receiving a query request of a user, wherein the query request corresponds to a query statement; analyzing the query statement to obtain the query intention of the user, and obtaining the topic category of the user query according to the query intention, wherein the user query intention and the topic category of the user query are obtained according to the content stored in a historical log record, and the historical query statement used by the user and the evaluation of the retrieval result obtained by the user on the historical query statement are stored in the log record; searching in basic data used as retrieval according to the topic category to obtain a search result; and returning the search result to the user.
In connection with the system of fig. 1, the flow in an alternative embodiment will be described, and as shown in fig. 2, when the system is used, first, initial data or knowledge points are entered in a background entry module 309 in the knowledge organization module 3, then the initial data or knowledge points are transmitted to an organization receiving module 308, then the organization receiving module 308 transmits the initial data or knowledge points to an analysis defining module 307, the analysis defining module 307 classifies the initial data or knowledge points, then the classified data or knowledge points are transmitted to a general management module 306, the general management module 306 generates data or knowledge points based on corresponding features and elements, then the data or knowledge points based on corresponding features and elements are transmitted to a resource library storage module 301, then the data based on corresponding features and elements are stored in an independent resource module 3012, and the knowledge points based on corresponding features and elements are stored in a knowledge resource module 3013.
In the platform operation process, the resource collection module 1 collects massive multi-source pictures, documents and data, then the massive multi-source pictures, documents and data are transmitted to the processing receiving module 201 in the processing module 2, then the processing receiving module 201 respectively transmits the received massive multi-source pictures, documents and data to the temporary storage module 202 and the deletion and arrangement module 203, the temporary storage module 202 temporarily stores the massive multi-source pictures, documents and data, then the temporary storage module 202 transmits the massive multi-source pictures, documents and data to the resource storage module 301, the original resource module 3011 stores the massive multi-source pictures, documents and data as original data, the deletion and arrangement module 203 arranges the massive multi-source pictures, then the duplicated and invalid parts are deleted, the independent data file package is primarily generated, the deletion and arrangement module 203 transmits the independent data file package to the feature analysis module 204, the feature of the data file package is analyzed and extracted, then the independent data file package is transmitted to the extraction module 205, the element extraction module 205 extracts the independent data file package, the independent data file is transmitted to the feature information package 206 and the corresponding to the independent data storage module 307, the feature and the independent data file is defined by the independent data package storage module 307, the feature and the independent data file is transmitted to the independent data file storage module 307 is correspondingly arranged, and the feature information package is transmitted to the independent data file storage module is divided and the independent data package is stored by the independent data package storage module 307, generating individual resource packages based on the corresponding features and elements by the generic management module 306, then transmitting the individual resource packages based on the corresponding features and elements to the repository storage module 301 for storage by the individual resource module 3012, while the resource processing module 206 transmits the individual data packages to the knowledge extraction module 207, extracts the corresponding knowledge points by the knowledge extraction module 207, then transmits the knowledge points to the knowledge definition module 208, the knowledge definition module 208 defines the knowledge points, then transmits the knowledge points and definition results to the composite processing module 209, the composite processing module 209 generates the individual knowledge packages in combination with the definition results, then transmits the individual knowledge packages to the organization receiving module 308, then uploads the individual knowledge packages to the analysis definition module 307, classifies the individual knowledge packages by the analysis definition module 307, the independent knowledge file package is then transmitted to the universal management module 306, the universal management module 306 generates the independent knowledge file package based on the corresponding features and elements, the independent knowledge file package based on the corresponding features and elements is then transmitted to the repository storage module 301, the knowledge resource module 3013 stores the independent knowledge file package, the knowledge extraction module 207 transmits the independent data file package incapable of extracting knowledge points to the new concept prediction module 210, the new concept prediction module 210 extracts the corresponding new concept, the new concept and the independent data file package are then transmitted to the knowledge prediction module 211, the knowledge prediction module 211 generalizes the predicted knowledge points, the predicted knowledge points are transmitted to the temporary storage module 202 for temporary storage, the temporary storage module 202 transmits the predicted knowledge points to the repository storage module 301, and stored by predictive knowledge module 3015; when knowledge is fused, the fusion invoking module 305 receives all independent resource packages based on corresponding features and elements and independent knowledge file packages based on corresponding features and elements and knowledge points based on corresponding features and elements in the knowledge resource module 3013, then transmits all independent resource packages based on corresponding features and elements, independent knowledge file packages based on corresponding features and elements and knowledge points based on corresponding features and elements to the fusion analyzing module 303, and the fusion analyzing module 303 classifies the independent resource packages based on corresponding features and elements, the knowledge points based on corresponding features and elements, the independent knowledge file packages based on corresponding features and elements and the knowledge points based on corresponding features and elements according to the features and elements, and then transmits the classified independent knowledge file packages based on corresponding features and elements to the fusion processing module 304, and then generates fusion knowledge and knowledge points based on corresponding features and elements by the fusion processing module 304, and then transmits the fusion knowledge and knowledge points based on corresponding features and elements to the resource storage module 301.
The fusion processing module 304 generates fusion knowledge and data based on corresponding characteristics and elements, knowledge fusion of massive multi-source heterogeneous resources is achieved, data is collected, processed and evaluated, information processing capacity of a system is improved, the combined resource module 3014 in the resource library storage module 301 is used for storing, in the platform operation process, the background input module 309 inputs data exchange information, the data exchange information is transmitted to the exchange adjustment module 310, the exchange adjustment module 310 invokes all data in the resource library storage module 301, the exchange adjustment module 310 marks the outdated data according to the input data exchange information, the mark is transmitted to the resource library storage module 301, the resource library storage module 301 deletes all outdated data according to the mark, the outdated data in the system is cleaned through the set exchange adjustment module 310, and the outdated data is eliminated, so that timeliness of the data in the system is ensured.
When the user uses, the knowledge service module 5 enters the system, then the search analysis of the data is carried out in the search analysis module 501, firstly, the search input module 5011 inputs the search information, then the search information is transmitted to the analysis processing module 5012, the analysis processing module 5012 analyzes the search keywords, then the analysis processing module 5012 transmits the analysis result to the verification calling module 5013, the verification calling module 5013 transmits the calling application to the transmission calling module 4, the transmission calling module 4 transmits the calling application to the extraction calling module 302, then the extraction calling module 302 calls the corresponding data and the related data from the resource library storage module 301 according to the calling application, then the transmission calling module 4 transmits the corresponding data and the related data to the verification calling module 5013, then the verification calling module 5013 transmits the corresponding data to the main body receiving module 5015 and the comparison analysis module 5016, the comparison analysis module 5014 and the main receiving module 5015 respectively transmit the corresponding data to the comparison analysis module 5014 and the comparison analysis module 5016, the transmission module 5014 and the main receiving module 5018 respectively transmit the corresponding data to the combined analysis module and the comparison module 5018, the combined analysis module and the correlation analysis module 5018 are combined with the auxiliary analysis module and the comparison module 5018 are combined to generate the combined analysis result, the combined analysis result is displayed by the auxiliary analysis module and the combined analysis module is used to the combined with the analysis module and the auxiliary analysis module is used to be better to be compared with the user.
In this embodiment, there is provided an electronic device including a memory in which a computer program is stored, and a processor configured to run the computer program to perform the method in the above embodiment.
The above-described programs may be run on a processor or may also be stored in memory (or referred to as computer-readable media), including both permanent and non-permanent, removable and non-removable media, and information storage may be implemented by any method or technique. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
These computer programs may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks and/or block diagram block or blocks, and corresponding steps may be implemented in different modules.
Such an apparatus or system is provided in this embodiment. The device is called a fusion interaction processing device facing to multi-source heterogeneous resources, and comprises: the system comprises a grabbing module, a storage module and a processing module, wherein the grabbing module is used for grabbing resource data to be processed from a public network channel, and the resource data comprises at least one of the following: pictures, documents, and data; the first classification module is used for processing the resource data to be processed into at least one independent resource packet after classification, wherein each independent resource packet in the at least one independent resource packet is different in classification; an extraction module for extracting knowledge points from the at least one independent resource package; the second classification module is used for classifying according to the extracted knowledge points and packaging to form at least one independent knowledge point file package; and the fusion module is used for fusing the resource data, the knowledge points, the at least one independent resource package and the at least one independent knowledge point file package, wherein the fused data is used as basic data for retrieval.
The apparatus is used to implement the functions of the method in the foregoing embodiments, and each module in the apparatus corresponds to each step in the method, which has been described in the method and will not be described herein. Each module in the apparatus may include or perform the functions of one or more modules in the system described above.
For example, the first classification module is configured to: classifying the resource data to be processed to obtain at least one file package; and classifying the at least one file package to obtain the at least one independent resource package, wherein each resource package comprises at least one file package. Optionally, the fusion module is configured to: and storing the at least one file package, the resource data, the knowledge points, the at least one independent resource package and the at least one independent knowledge point file package.
For another example, the classification related in the device is to classify the first content according to the characteristics and/or elements of the first content to be classified to obtain second content, where the first content is the resource data to be processed, and the second content is at least one file package; and/or the first content is the at least one file package, and the second content is the at least one independent resource package; and/or the first content is a knowledge point, and the second content is at least one independent knowledge point packet; wherein the features and/or elements are the basis of classification. Optionally, the first content to be classified is set in a cache by the classification related to the device, and the second content after the classification is obtained by classifying the content to be classified in the cache.
For another example, the first classification module is configured to: processing the resource data to be processed, wherein the processing comprises at least one of the following: deleting repeated data, deleting invalid data and deleting outdated data; and classifying the processed resource data to obtain the at least one independent resource package.
For another example, the method further includes: the search module is used for receiving a query request of a user, wherein the query request corresponds to a query sentence; analyzing the query statement to obtain the query intention of the user, and obtaining the topic category of the user query according to the query intention, wherein the user query intention and the topic category of the user query are obtained according to the content stored in a historical log record, and the historical query statement used by the user and the evaluation of the retrieval result obtained by the user on the historical query statement are stored in the log record; searching in basic data used as retrieval according to the topic category to obtain a search result; and returning the search result to the user.
The embodiment solves the problem that the prior art has single processing capacity of various resources and does not provide good basic data for retrieval, thereby providing basic data for retrieval and improving the retrieval experience of users.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (5)
1. A knowledge fusion processing method for heterogeneous resources is characterized by comprising the following steps:
capturing resource data to be processed from the disclosed network channel, wherein the resource data comprises at least one of the following: pictures, documents, and data;
processing the resource data to be processed into at least one independent resource packet after classification, wherein each independent resource packet in the at least one independent resource packet is different in classification;
extracting knowledge points from the at least one independent resource package;
classifying and packaging according to the extracted knowledge points to form at least one independent knowledge point file package;
fusing the resource data, the knowledge points, the at least one independent resource package and the at least one independent knowledge point file package, wherein the fused data is used as basic data for retrieval;
processing the resource data to be processed into at least one classified independent resource package comprises:
classifying the resource data to be processed to obtain at least one file package;
classifying the at least one file package to obtain at least one independent resource package, wherein each resource package comprises at least one file package;
fusing the resource data, the knowledge points, the at least one independent resource package, and the at least one independent knowledge point file package includes:
storing the at least one file package, the resource data, the knowledge points, the at least one independent resource package and the at least one independent knowledge point file package;
processing the resource data to be processed into at least one classified independent resource package comprises:
processing the resource data to be processed, wherein the processing comprises at least one of the following: deleting repeated data, deleting invalid data and deleting outdated data;
and classifying the processed resource data to obtain the at least one independent resource package.
2. The method according to claim 1, wherein the classification related to the method is that a second content is obtained after classifying the first content according to the characteristics and/or elements of the first content to be classified, where the first content is the resource data to be processed, and the second content is at least one file package; and/or the first content is the at least one file package, and the second content is the at least one independent resource package; and/or the first content is a knowledge point, and the second content is at least one independent knowledge point packet; wherein the features and/or elements are the basis of classification.
3. The method according to claim 2, wherein the classification involved in the method sets a first content to be classified in a cache, and the content to be classified is classified in the cache to obtain a second content after classification.
4. The method as recited in claim 1, further comprising:
receiving a query request of a user, wherein the query request corresponds to a query statement; analyzing the query statement to obtain the query intention of the user, and obtaining the topic category of the user query according to the query intention, wherein the user query intention and the topic category of the user query are obtained according to the content stored in a historical log record, and the historical query statement used by the user and the evaluation of the retrieval result obtained by the user on the historical query statement are stored in the log record; searching in basic data used as retrieval according to the topic category to obtain a search result; and returning the search result to the user.
5. The knowledge fusion processing device for heterogeneous resources is characterized by comprising:
the system comprises a grabbing module, a storage module and a processing module, wherein the grabbing module is used for grabbing resource data to be processed from a public network channel, and the resource data comprises at least one of the following: pictures, documents, and data;
the first classification module is used for processing the resource data to be processed into at least one independent resource packet after classification, wherein each independent resource packet in the at least one independent resource packet is different in classification;
an extraction module for extracting knowledge points from the at least one independent resource package;
the second classification module is used for classifying according to the extracted knowledge points and packaging to form at least one independent knowledge point file package;
the fusion module is used for fusing the resource data, the knowledge points, the at least one independent resource package and the at least one independent knowledge point file package, wherein the fused data is used as basic data for retrieval;
processing the resource data to be processed into at least one classified independent resource package comprises:
classifying the resource data to be processed to obtain at least one file package;
classifying the at least one file package to obtain at least one independent resource package, wherein each resource package comprises at least one file package;
fusing the resource data, the knowledge points, the at least one independent resource package, and the at least one independent knowledge point file package includes:
storing the at least one file package, the resource data, the knowledge points, the at least one independent resource package and the at least one independent knowledge point file package;
processing the resource data to be processed into at least one classified independent resource package comprises:
processing the resource data to be processed, wherein the processing comprises at least one of the following: deleting repeated data, deleting invalid data and deleting outdated data;
and classifying the processed resource data to obtain the at least one independent resource package.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111641087.7A CN114519131B (en) | 2021-12-29 | 2021-12-29 | Knowledge fusion processing method and device for heterogeneous resources |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111641087.7A CN114519131B (en) | 2021-12-29 | 2021-12-29 | Knowledge fusion processing method and device for heterogeneous resources |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114519131A CN114519131A (en) | 2022-05-20 |
| CN114519131B true CN114519131B (en) | 2023-07-25 |
Family
ID=81597163
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111641087.7A Active CN114519131B (en) | 2021-12-29 | 2021-12-29 | Knowledge fusion processing method and device for heterogeneous resources |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114519131B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3633569A1 (en) * | 2018-10-05 | 2020-04-08 | Verint Americas Inc. | Building of knowledge base and faq from voice, chat, email, and social interactions |
| CN113568998A (en) * | 2021-06-18 | 2021-10-29 | 武汉理工数字传播工程有限公司 | Knowledge service resource processing method, device, equipment and storage medium |
| CN113806550A (en) * | 2021-04-15 | 2021-12-17 | 京东科技控股股份有限公司 | Generation method and device of personalized knowledge graph and computer equipment |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8335754B2 (en) * | 2009-03-06 | 2012-12-18 | Tagged, Inc. | Representing a document using a semantic structure |
| US20180366013A1 (en) * | 2014-08-28 | 2018-12-20 | Ideaphora India Private Limited | System and method for providing an interactive visual learning environment for creation, presentation, sharing, organizing and analysis of knowledge on subject matter |
| CN107958004A (en) * | 2016-10-17 | 2018-04-24 | 中国移动通信有限公司研究院 | The construction method and device of a kind of knowledge base |
| US10423631B2 (en) * | 2017-01-13 | 2019-09-24 | International Business Machines Corporation | Automated data exploration and validation |
| CN110674123B (en) * | 2019-09-16 | 2021-09-14 | 北京云从科技有限公司 | Data preprocessing method, device, equipment and medium |
| CN111414492A (en) * | 2020-04-17 | 2020-07-14 | 桑滨 | Knowledge network construction method and system |
| CN113569007A (en) * | 2021-06-18 | 2021-10-29 | 武汉理工数字传播工程有限公司 | Method, device and storage medium for processing knowledge service resources |
| CN113742496A (en) * | 2021-09-10 | 2021-12-03 | 国网江苏省电力有限公司电力科学研究院 | Power knowledge learning system and method based on heterogeneous resource fusion |
-
2021
- 2021-12-29 CN CN202111641087.7A patent/CN114519131B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3633569A1 (en) * | 2018-10-05 | 2020-04-08 | Verint Americas Inc. | Building of knowledge base and faq from voice, chat, email, and social interactions |
| CN113806550A (en) * | 2021-04-15 | 2021-12-17 | 京东科技控股股份有限公司 | Generation method and device of personalized knowledge graph and computer equipment |
| CN113568998A (en) * | 2021-06-18 | 2021-10-29 | 武汉理工数字传播工程有限公司 | Knowledge service resource processing method, device, equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114519131A (en) | 2022-05-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Khare et al. | Big data in IoT | |
| Haddad et al. | The impact of intention of use on the success of big data adoption via organization readiness factor | |
| US7873594B2 (en) | System analysis program, system analysis method, and system analysis apparatus | |
| KR20230025714A (en) | Document processing and response generation system | |
| US20060129745A1 (en) | Process and appliance for data processing and computer program product | |
| MXPA04006378A (en) | Method and apparatus for automatic detection of data types for data type dependent processing. | |
| KR102376201B1 (en) | System and method for generating multimedia knowledge base | |
| Patwardhan et al. | A survey on predictive maintenance through big data | |
| US20130094403A1 (en) | Method and apparatus for providing sensor network information | |
| CN110162512A (en) | A kind of log searching method, apparatus and storage medium | |
| CN109710667A (en) | A kind of shared realization method and system of the multisource data fusion based on big data platform | |
| CN114519131B (en) | Knowledge fusion processing method and device for heterogeneous resources | |
| CN112905571B (en) | Train rail transit sensor data management method and device | |
| McNeill et al. | Communication in emergency management through data integration and trust: an introduction to the CEM-DIT system | |
| CN117033744A (en) | Data query method and device, storage medium and electronic equipment | |
| de Mendonça et al. | LOP: capturing and linking open provenance on LOD cycle | |
| CN114817256A (en) | Quick unified storage system of thing networking | |
| KR20220074571A (en) | A method and an apparatus for embedding sns contents based on deep learning analysis for collecting channel information of marketing knowledge graphs | |
| CN105915376A (en) | Log information network structuring method and log information network structuring system based on P2P program requesting system | |
| CN112528056A (en) | Double-index field data retrieval system and method | |
| Di Lucca et al. | Towards a better comprehensibility of web applications: Lessons learned from reverse engineering experiments | |
| Patil et al. | Optimized web-crawling of conversational data from social media and context-based filtering | |
| US11835989B1 (en) | FPGA search in a cloud compute node | |
| CN111309683B (en) | Method and device for scanning full disk data | |
| Mestl et al. | Time Challenges-Challenging Times for Future Information Search |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |